Stability performance of leaning-type steel box arch bridges: a parametric study based on a validated finite element model

Stability performance of leaning-type steel box arch bridges: a parametric study based on a validated finite element model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Stability performance of leaning-type steel box arch bridges: a parametric study based on a validated finite element model Zhimin She, Qinghui Lin, Xiaoye Luo, Zhitong She, Wenjin Huang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8734345/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Leaning-type arch bridges have become increasingly attractive for wide-deck urban applications due to their distinctive aesthetics and enhanced spatial stiffness. By introducing inclined leaning arches connected to vertical main arches through transverse bracings, this structural system offers an effective alternative to conventional lateral bracing schemes. However, the stability mechanisms of leaning-type arch bridges and the influence of key design parameters have not yet been fully clarified. In this study, the stability performance of a leaning-type steel box arch bridge is systematically investigated using a three-dimensional finite element model validated against field measurements of arch deflections and hanger forces. A comprehensive parametric analysis is conducted to quantify the effects of the leaning arch system, rise-to-span ratio, arch stiffness, and transverse bracing configuration on global stability. The results show that the incorporation of leaning arches significantly enhances lateral stability, increasing the stability coefficient by up to 71% compared with systems without leaning arches. An optimal rise-to-span ratio for the main arches is identified in the range of 1/4.5 to 1/7, balancing geometric stiffness and structural efficiency. The bending stiffness of the main arches is found to govern global stability, whereas the inclining angle of the leaning arches and the stiffness of transverse bracings have comparatively limited influence. Moreover, transverse bracings located closer to the arch springings are shown to be more effective in improving stability than those near the arch crown. These findings provide quantitative insight into the stability mechanisms of leaning-type arch bridges and offer practical guidance for their efficient and reliable structural design. Physical sciences/Engineering Physical sciences/Materials science Leaning-type arch bridge Global stability Finite element analysis Parametric study rise-to-span ratio Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction In recent years, the design of urban bridges has increasingly emphasized not only structural functionality but also architectural quality and spatial integration within the urban environment 1 . With the rapid growth of urban traffic demands, bridges are often required to accommodate wider decks with multiple traffic lanes and pedestrian facilities, while also serving as visual landmarks. This trend has stimulated the development of special-shaped and spatial arch bridges that combine structural efficiency with distinctive aesthetic characteristics, including butterfly-shaped arches, basket-handle arches, network arch bridges, and leaning-type arch bridges 2 – 5 . For such spatial structural systems, global stability is frequently a governing design criterion, as instability may control structural safety and material efficiency more critically than strength 6 , 7 . Among various special-shaped arch systems, the leaning-type arch bridge has emerged as an effective solution for wide-deck urban bridges. This bridge type typically consists of two vertical main arches and two inclined leaning arches arranged on the outer sides, which are interconnected through transverse bracings to form an integrated spatial structural system 8 . Compared with conventional arch bridges relying solely on inter-rib bracing, the leaning-type configuration offers improved spatial transparency and enhanced adaptability to wide bridge decks, while maintaining a concise and expressive structural form. More importantly, the inclined leaning arches provide additional lateral restraint to the main arches, indicating a potentially significant contribution to the overall structural stability of the bridge system 9 . Extensive research has been conducted on the mechanical behavior and stability of spatial arch bridges with complex geometries, such as butterfly arches, basket-handle arches, network arch bridges, and other inclined arch systems 10 – 13 . These studies have demonstrated that parameters such as rise-to-span ratio, arch rib stiffness, and inclination angle can strongly influence internal force distribution and buckling behavior. For leaning-type arch bridges in particular, previous investigations have examined structural systems with or without horizontal thrust, analyzed internal force characteristics under dead loads, and explored the effects of selected geometric parameters on stability performance 14 – 17 . Despite these efforts, several issues related to the stability behavior of leaning-type arch bridges remain insufficiently understood. First, many existing studies are based on simplified structural models or specific material systems, while systematic investigations grounded in real engineering projects and validated by field measurements are still limited. Second, although the stabilizing effect of leaning arches has been qualitatively recognized, their quantitative contribution to global stability—especially for wide-deck steel box arch bridges—has not been clearly established. Third, parametric studies have often focused on individual factors in isolation, making it difficult to identify the relative importance of key design parameters and to distinguish dominant factors from secondary ones. In addition, the influence of transverse bracing configuration, particularly the relative effectiveness of bracing stiffness versus bracing layout, has received comparatively little attention. To address these gaps, the present study investigates the stability performance of a leaning-type steel box arch bridge through a comprehensive parametric analysis based on a validated three-dimensional finite element model. A real-world leaning-type arch bridge with a wide deck is selected as a representative case, and the numerical model is verified against field-measured arch deflections and hanger forces. Using this validated model, the effects of the leaning arch system, rise-to-span ratio, arch rib stiffness, inclining angle of the leaning arches, and transverse bracing characteristics are systematically evaluated. The study aims to clarify the stability mechanisms of leaning-type arch bridges, identify the dominant parameters governing global stability, and provide practical insights to support the efficient and reliable design of such bridge systems. Engineering background The Wetland Park Bridge in Quanzhou, China, is selected as the engineering background of this study. The bridge is a representative leaning-type steel box arch bridge designed to accommodate a wide deck while maintaining high structural efficiency and visual openness. The general arrangement of the bridge is illustrated in Fig. 1 . The superstructure adopts a three-span continuous arch-girder composite system with a span configuration of 14.9 m + 82.2 m + 14.9 m. The total bridge width varies from 44 m to 53 m, reflecting the functional requirement for multi-lane traffic and pedestrian facilities in an urban environment. The transverse structural system consists of two vertical main arches and two inclined leaning arches. The main arches are spaced 26.5 m apart and have a span of 78.33 m with a rise of 17.05 m, corresponding to a rise-to-span ratio of approximately 1/4.6. Each main arch adopts a single-cell steel box section with overall dimensions of 1800 mm × 2000 mm and plate thicknesses ranging from 20 mm to 36 mm. The leaning arches are arranged symmetrically on the outer sides of the main arches and are inclined at an angle of 21° with respect to the vertical plane. They have a span of 122 m and a rise of 21.9 m and are constructed using single-cell steel box sections with dimensions of 1000 mm × 1000 mm and plate thicknesses ranging from 20 mm to 36 mm. The main arches and leaning arches are interconnected by a system of transverse bracings to form an integrated spatial load-bearing structure. A total of fourteen transverse bracings, arranged in seven symmetric pairs, are provided along the arch span. Unlike conventional arch bridges where transverse bracings are typically installed only between main arches, the bracings in this bridge connect the main arches directly to the leaning arches. This configuration enhances the overall lateral stiffness of the arch system while improving visual transparency and driving comfort. The transverse bracings adopt single-cell steel box sections with cross-sectional dimensions of 700 mm × 700 mm and a uniform plate thickness of 24 mm. The deck system consists of an orthotropic steel deck supported by a grid framework. Two primary longitudinal girders, fabricated as welded steel box sections with dimensions of 1600 mm × 1800 mm and a plate thickness of 16 mm, are rigidly connected to the main arches to form an arch-girder composite structural system. This arrangement enables effective load transfer between the deck and the arches and contributes to the overall stiffness and load-carrying capacity of the bridge. The substructure comprises reinforced concrete solid piers and straight abutments, providing support for the entire arch-girder system in an externally statically determinate configuration. The piers primarily resist axial forces and bending moments induced by the superstructure. At the springings of the leaning arches, independent pile caps are constructed and monolithically connected to the main pier caps. This integrated foundation system ensures that the horizontal thrust generated by the leaning arches is collectively resisted by all foundation piles, thereby enhancing the stability and load distribution capacity of the substructure. Finite element model Establishment of the finite element model A three-dimensional finite element model of the leaning-type steel box arch bridge was established to investigate its global stability behavior. The model explicitly represents the primary load-bearing components of the bridge, including the main arches, leaning arches, transverse bracings, longitudinal girders, and deck system. The arch ribs, transverse bracings, and longitudinal girders were modeled using beam elements, with cross-sectional properties defined according to the corresponding single-cell steel box sections. The deck system was simplified as an equivalent beam-girder system rigidly connected to the main arches, forming an integrated arch-girder composite structural system. This modeling approach captures the global stiffness and load transfer characteristics of the bridge while maintaining computational efficiency. All steel components were made of Q345 steel and were modeled as linear elastic. The reinforced concrete substructure was not explicitly modeled, instead, its restraining effect was represented through boundary conditions at the arch springings. The main arches were assumed to be fixed at their springings, while the leaning arches were connected to independent pile caps monolithically integrated with the main pier caps. Dead load was applied automatically based on the self-weight of structural components. Linear elastic eigenvalue buckling analysis was performed to evaluate the global stability coefficient of the bridge, and the first-order buckling mode was taken as the representative instability mode. Verification of the finite element model The accuracy of the finite element model was verified using field-measured data obtained during key construction stages and at the completed state of the Wetland Park Bridge. Both arch deflections and hanger tension forces were selected as verification indicators, as they are sensitive to the global stiffness characteristics and load-transfer behavior of the arch system. The Wetland Park Bridge was constructed using the falsework method. During construction, the deck grid beams were erected first, followed by the installation and closure of the main and leaning arches. Figure 3 presents photographs of the major construction stages, and the detailed construction sequence is summarized in Table 1. Based on the construction stages summarized in Table 1, three critical construction stages (CS5, CS8, and CS11) were selected for model verification, as these stages are associated with significant changes in structural stiffness and exhibit pronounced variations in arch deflections. Figure 4 compares the calculated arch deflections obtained from the finite element model with the corresponding measured values at the selected construction stages. The results show that the numerical predictions are in good agreement with the field measurements for all three stages. Both the distribution patterns and magnitudes of arch deflections are well captured by the model. The maximum deviation between the calculated and measured deflections does not exceed 10%, indicating that the finite element model can reasonably reproduce the stiffness evolution of the bridge during construction. In addition to deflection verification, the accuracy of the finite element model was further assessed by comparing the calculated and measured hanger tension forces at the completed bridge state. Table 2 and Table 3 present a detailed comparison between calculated and measured hanger forces for the main and leaning arches. The results indicate that the maximum error in hanger tension for the main arches is -8.9%, while that for the leaning arches is 8.5%. These discrepancies remain within an acceptable range for engineering analysis, demonstrating that the finite element model can accurately reflect the internal force distribution of the completed structure. Based on the comparison between numerical predictions and field measurements at multiple construction stages and in the completed bridge state, the established finite element model is considered sufficiently accurate and reliable for the subsequent stability analyses and parametric investigations presented in this study. Construction stages Stage description CS1 Construction of substructure CS2 Erection of deck girder falsework and installation of deck girders CS3 Erection of main arch falsework and closure of main arches CS4 Erection of leaning arch falsework and closure of leaning arches CS5 Dismantling of main arch falsework CS6 Installation and tensioning of main arch hangers CS7 Welding of transverse bracings No. 3–5 CS8 Dismantling of leaning arch falsework CS9 Installation and tensioning of leaning arch hangers to target forces CS10 Welding of remaining transverse bracings CS11 Dismantling of deck girder falsework CS12 Application of secondary permanent loads CS13 Secondary tensioning of main arch hangers to target forces Table 1. Construction stages of the Wetland Park Bridge NO. Calculated value Measured value - Main arch 1 Measured value - Main arch 2 Force Error Force Error 1 596 562 -5.6% 543 -8.9% 2 608 647 6.4% 598 -1.7% 3 612 585 -4.5% 583 -4.8% 4 616 562 -8.9% 659 6.9% 5 615 572 -7.0% 625 1.7% 6 574 612 6.7% 543 -5.4% 7 628 579 -7.8% 677 7.8% 8 574 605 5.4% 535 -6.8% 9 615 578 -6.0% 600 -2.3% 10 616 588 -4.6% 627 1.7% 11 612 626 2.2% 630 2.8% 12 608 627 3.1% 615 1.1% Table 2. Comparison between calculated and measured hanger forces for the main arches NO. Calculated value Measured value - Leaning arch 1 Measured value - Leaning arch 2 Force Error Force Error 1 47 50 5.9% 46 -2.6% 2 47 45 -4.4% 50 6.3% 3 52 50 -4.5% 48 -8.3% 4 65 65 -0.5% 64 -2.0% 5 61 56 -8.0% 66 8.5% 6 59 63 6.1% 58 -2.3% 7 59 58 -1.5% 61 3.6% 8 61 63 3.3% 59 -3.3% 9 60 57 -5.3% 57 -5.3% 10 89 85 -4.1% 86 -2.9% 11 60 59 -2.0% 62 3.0% 12 61 65 6.5% 56 -8.2% 13 59 62 5.3% 55 -6.6% 14 59 59 -0.6% 63 6.1% 15 61 57 -6.3% 57 -6.3% 16 65 65 -0.5% 61 -6.6% 17 52 56 7.0% 51 -2.6% 18 47 51 8.4% 44 -6.5% 19 47 47 -0.3% 48 1.8% Table 3. Comparison between calculated and measured hanger forces for the leaning arches Effect of leaning arches on the global stability To clarify the effect of leaning arches on the global stability of the bridge, three structural systems were investigated and compared, as summarized in Table 3. These systems represent different lateral restraint strategies commonly adopted in wide-deck arch bridges. Structural System 1 corresponds to a conventional configuration without leaning arches or transverse bracings. Structural System 2 excludes leaning arches but incorporates transverse bracings installed between the main arches. Structural System 3 represents the leaning-type arch bridge, in which inclined leaning arches are introduced and connected to the main arches through transverse bracings to form an integrated spatial structural system. The buckling modes and corresponding global stability coefficients obtained from linear elastic eigenvalue buckling analysis are presented in Table 4. For Structural System 1, the first-order buckling mode is characterized by out-of-plane symmetric buckling of a single main arch, and the corresponding stability coefficient is 20.5. When transverse bracings are added between the main arches (Structural System 2), the buckling mode changes to a global symmetric out-of-plane buckling mode involving both main arches, and the stability coefficient increases to 26.5, representing an improvement of approximately 29% compared with Structural System 1. Table 4. Buckling modes and global stability coefficients of different structural systems With the introduction of leaning arches and their connection to the main arches via transverse bracings (Structural System 3), the buckling behavior of the bridge exhibits a further transition. The first-order buckling mode becomes an out-of-plane antisymmetric mode involving the coupled deformation of both the main and leaning arches. The corresponding stability coefficient reaches 34.9, which is approximately 71% higher than that of Structural System 1. The comparison among the three structural systems clearly demonstrates that, for wide-deck arch bridges, the incorporation of leaning arches provides a substantially greater enhancement in global stability than that achieved by installing transverse bracings solely between the main arches. This result confirms the rationality and structural efficiency of the leaning-type arch bridge system in improving global stability performance. Analysis of influencing parameters Effect of inclining angle of leaning arch The installation of leaning arches effectively enhances the lateral stability of wide-span arch bridges. By varying the inclining angle of the leaning arches, this study further investigates its influence on bridge stability. Two approaches are adopted to adjust the inclining angle of the leaning arches: (1) keeping the arch springing positions fixed while changing the lateral position of the arch crown, and (2) keeping the crown position fixed while adjusting the lateral positions of the arch springings. Figure 6 presents the stability coefficients corresponding to different inclining angles obtained using these two approaches. As illustrated in Figure 6, the variation trends of the stability coefficient differ depending on the method used to adjust the inclining angle. When the arch springing positions are fixed and the lateral position of the crown is varied, the stability coefficient initially increases and then decreases with increasing inclining angle, reaching a maximum value at an inclining angle of 21°. In contrast, when the crown position is fixed and the lateral positions of the arch springings are adjusted, the stability coefficient first decreases and then increases, attaining its minimum value at 21°. In terms of the magnitude of variation, taking the designed inclining angle of 21° as the reference, the maximum deviations of the stability coefficient within the inclining angle range of 12° to 27° are 2.3% and 3.7% for the two adjustment approaches, respectively. These results indicate that although variations in the inclining angle of the leaning arches have a measurable influence on bridge stability, the overall effect is relatively limited. Therefore, for leaning-type steel box arch bridges, the influence of the inclining angle of the leaning arches on global stability may be considered negligible. In practical design, the inclining angle can be primarily determined based on considerations such as the spatial arrangement of the arch springings and crown, as well as aesthetic requirements. Effect of arch stiffness The bending stiffness of the arches is a key factor influencing the global stability of the bridge. Taking the designed bending stiffness of the Wetland Park Bridge as the reference case (1.0 EI M for the main arches and 1.0 EI L for the leaning arches), the cross-sectional bending stiffness of the main and leaning arches was varied independently to investigate their respective effects on global stability. Figure 7 presents the first-order stability coefficients of the bridge corresponding to different arch stiffness values. It can be observed that the stability coefficient increases with increasing bending stiffness of both the main and leaning arches. In particular, an approximately linear relationship is observed between the stability coefficient and the bending stiffness of the main arches. When the stiffness of the main arches decreases from 1.0 EI M to 0.5 EI M , the stability coefficient decreases from 34.9 to 25.3, corresponding to a reduction of 27.5%. Conversely, when the stiffness increases from 1.0 EI M to 2.0 EI M , the stability coefficient increases from 34.9 to 45.6, representing an increase of 30.7%. These results indicate that the bending stiffness of the main arches has a pronounced influence on the global stability performance of the bridge. By comparison, the influence of the bending stiffness of the leaning arches on the stability coefficient is relatively less significant. When the stiffness of the leaning arches increases from 0.5 EI L to 1.0 EI L , the stability coefficient increases from 28.8 to 34.9, corresponding to an increase of 17.5%. However, when the stiffness is further increased from 1.0 EI L to 2.0 EI L , the stability coefficient increases only from 34.9 to 38.6, an increment of 10.5%. Within the range of 0.5 EI L to 1.0 EI L , the rate of increase in the stability coefficient is higher than that observed in the range of 1.0 EI L to 2.0 EI L . Once the stiffness exceeds 1.0 EI L , the stability coefficient tends to gradually level off, demonstrating the rationality of the designed stiffness for the leaning arches in the Wetland Park Bridge. In summary, since the cross-sectional stiffness of the main arches exerts a more significant influence on global stability than that of the leaning arches, the stability performance of leaning-type steel box arch bridges can be effectively enhanced by moderately increasing the cross-sectional dimensions of the main arches while reducing those of the leaning arches. This design strategy not only improves global stability but also contributes to material savings and enhances the overall aesthetic performance of the bridge. Effect of transverse bracing stiffness The main and leaning arches of the leaning-type steel box arch bridge are interconnected by transverse bracings, which play an important role in ensuring the integrity and stability of the structural system. To investigate the influence of transverse bracing stiffness on global stability, the cross-sectional bending stiffness of the bracings was varied, with the designed stiffness (1.0 EI B ) taken as the reference case. Figure 8 presents the global stability coefficients of the bridge corresponding to different transverse bracing stiffness values. The results indicate that the global stability coefficient increases with increasing transverse bracing stiffness, although the magnitude of variation is relatively limited. When the bracing stiffness decreases from 1.0 EI B to 0.5 EI B , the stability coefficient decreases slightly from 34.9 to 32.0, corresponding to a reduction of 8.3%. Conversely, when the bracing stiffness increases from 1.0 EI B to 2.0 EI B , the stability coefficient increases from 34.9 to 38.6, representing a modest increase of 6.6%. These results suggest that the primary function of the transverse bracings is to connect the main and leaning arches into an integrated structural system, enabling them to resist loads in a coordinated manner. Although the cross-sectional stiffness of the transverse bracings does influence the global stability of the leaning-type steel box arch bridge, its effect is relatively limited compared with that of the arches themselves. Therefore, in practical design, excessively increasing the cross-sectional dimensions of the transverse bracings is unnecessary, and their stiffness may be determined primarily based on structural connectivity and construction requirements rather than stability considerations alone. Effect of number and arrangement of transverse bracings The Wetland Park Bridge is equipped with seven transverse bracings between the main and leaning arches on each side, which are sequentially numbered from 1 to 7. To investigate the influence of the number and arrangement of transverse bracings on the global stability of the leaning-type arch bridge, the stability coefficients corresponding to several representative bracing configurations were calculated, as summarized in Table 5. As shown in Table 5, the stability coefficient of the leaning-type arch bridge generally decreases as the number of transverse bracings is reduced. In addition to the number of bracings, the arrangement of the bracings is found to have a pronounced influence on global stability. A comparison of Cases 2 to 4, all of which contain five transverse bracings, indicates that the stability coefficient increases when the bracings are positioned closer to the arch springings. Specifically, when the number of bracings is reduced from seven to five by removing the bracings near the arch springings (Case 2), the stability coefficient decreases from 34.9 to 25.8, corresponding to a reduction of 26%. By contrast, when the bracings near the crown are removed while those close to the springings are retained (Case 4), the stability coefficient decreases only slightly from 34.9 to 34.1, representing a marginal reduction of 2.3%. These results indicate that, for the leaning-type steel box arch bridges, the bracing configuration adopted in Case 4 provides an effective balance between global stability performance and structural efficiency. This optimized arrangement not only maintains the stability coefficient at a level close to that of the original configuration but also reduces steel consumption and facilitates construction. Table 5. Stability coefficients under different transverse bracing configurations. A similar trend is observed for configurations with three transverse bracings (Cases 5 to 7). When all three bracings are located in the crown region (Case 5), the stability coefficient is relatively low, with a value of 18.9. In contrast, when two of the three bracings are arranged near the arch springings (Case 7), the stability coefficient increases significantly to 28.3, corresponding to an increase of 49.7%. This comparison further confirms that the transverse bracings located near the arch springings play a more critical role in enhancing the global stability of the bridge than those arranged near the crown. Summary and conclusions This study systematically investigated the stability performance of a leaning-type steel box arch bridge through a comprehensive parametric analysis based on a validated three-dimensional finite element model. The numerical model was rigorously verified against field-measured arch deflections and hanger forces obtained during key construction stages and in the completed bridge state. On this basis, the effects of the leaning arch system and several key design parameters on global stability were quantitatively evaluated. The main conclusions can be summarized as follows: 1. The incorporation of leaning arches provides a substantial enhancement in global stability. Compared with a system without leaning arches, the stability coefficient increases by up to 71%, significantly outperforming configurations that rely solely on transverse bracings between the main arches. This confirms the structural efficiency and rationality of the leaning-type arch bridge system for wide-deck applications. 2. The rise-to-span ratio of the main arches has a pronounced influence on global stability. An optimal range of approximately 1/4.5 to 1/7 is identified, within which the stability performance is maximized through a favorable balance between geometric stiffness and bending effects. 3. The bending stiffness of the main arches plays a dominant role in governing global stability. Doubling the stiffness of the main arches leads to an increase of 30.7% in the stability coefficient, whereas variations in the stiffness of the leaning arches result in comparatively smaller improvements. 4. Transverse bracings contribute to global stability primarily through their configuration rather than their stiffness. While increasing transverse bracing stiffness leads to only limited improvements in the stability coefficient, the number and arrangement of transverse bracings have a pronounced effect. Bracings located near the arch springings are significantly more effective than those near the arch crown, and an optimized layout can increase the stability coefficient by up to 49.7% compared with non-optimal arrangements. 5. The inclining angle of the leaning arches has a negligible influence on global stability within the investigated range. This allows the inclining angle to be primarily determined by architectural, spatial, and constructional considerations rather than stability requirements. Overall, the findings of this study provide quantitative insight into the stability mechanisms of leaning-type steel box arch bridges and highlight the relative importance of key geometric and structural parameters. The results offer practical guidance for the efficient and reliable design of leaning-type arch bridge systems, particularly for wide-deck urban applications. Declarations Competing interests The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to Xiaoye Luo. Funding This research was supported by Startup Fund for Advanced Talents of Putian University (grant number 2022057), Fujian Provincial Natural Science Foundation of China (grant number 2025J01389 and 2025J011696), Science and Technology Program Project of Putian City (grant number 2025SZ3001PTXY07). Author Contribution Zhimin She: Conceptualization, Funding acquisition, Methodology, Formal analysis, Investigation, Data curation, Writing - original draft. Qinghui Lin: Conceptualization, Methodology, Formal analysis, Data curation, Writing - original draft. Xiaoye Luo: Methodology, Funding acquisition, Data curation, Formal analysis. Zhitong She: Methodology, Data curation. Wenjin Huang: Conceptualization, Supervision. Jialiang Zhou: Writing - Review & Editing, Visualization. Data Availability All data supporting the findings in this study are available from the corresponding author upon reasonable request. References Han, Z. Aesthetics Innovation and practice of urban bridge design. Struct. Eng. Int. 31 (4), 543–549. https://doi.org/10.1080/10168664.2020.1848368 (2021). Xia, Q. et al. System design and demonstration of performance monitoring of a butterfly-shaped arch footbridge. Struct. Control Hlth . 28 (7), e2738. https://doi.org/10.1002/stc.2738 (2021). Alcayde, A. et al. Basket-Handle Arch and its optimum symmetry generation as a structural element and keeping the aesthetic point of view. Symmetry-Basel 11 (10), 1243. https://doi.org/10.3390/sym11101243 (2019). Danciu, A. D. et al. A review of the network arch bridge. Appl. Sci-Basel . 13 (19), 10966. https://doi.org/10.3390/app131910966 (2023). Zhang, Y. Design strategies for leaning-type arch bridges. J. World Archit. 7 (2), 11–16. https://doi.org/10.26689/jwa.v7i2.4752 (2023). Wu, Y., Jin, T., Wu, W. & Lu, P. Structural innovation design and mechanical performance of double-deck large-span steel truss arch bridges. Mech. Based Des. Struc . 1–20. https://doi.org/10.1080/15397734.2025.2574885 (2025). Wu, Y. et al. A Study on the ultimate span of a concrete-filled steel tube arch bridge. Buildings 14 (4), 896. https://doi.org/10.3390/buildings14040896 (2024). Xiao, R., Sun, H., Jia, L. & Sun, B. Kunshan Yufeng Bridge — design of the first long-span leaning - type arch bridge without thrust. China Civ. Eng. J. 38 (1), 78–83. https://doi.org/10.15951/j.tmgcxb.2005.01.010 (2005). Liu, A. et al. Experimental research on stable ultimate bearing capacity of leaning-type arch rib systems. J. Constr. Steel Res. 114 , 281–292. https://doi.org/10.1016/j.jcsr.2015.08.011 (2015). Wu, G., Ren, W., Zhu, Y. & Hussain, S. M. Static and dynamic evaluation of a butterfly-shaped concrete-filled steel tube arch bridge through numerical analysis and field tests. Adv. Mech. Eng. 13 (9), 1–13. https://doi.org/10.1177/16878140211044671 (2021). Sun, J., Chen, S., Wang, Z., Sui, W. & Zhang, Q. Study of the impact of varying inclination angles of arch ribs on the seismic behavior of half-through steel basket-handle arch bridge. Buildings 14 (3), 794. https://doi.org/10.3390/buildings14030794 (2024). Jiang, Z. et al. An analytical method for out-of-plane stability assessment of network arch bridges. Thin-Walled Struct . 204 , 112289. https://doi.org/10.1016/j.tws.2024.112289 (2024). Hu, X. et al. Case study on stability performance of asymmetric steel arch bridge with inclined arch ribs. Steel Compos. Struc . 18 (1), 273–288. https://doi.org/10.12989/scs.2015.18.1.273 (2015). Wang, Y., Liu, Y., Liang, Y. & Zhang, S. Nonlinear stability analysis and completed bridge test on slanting type CFST arch bridges. J. Build. Struct. 36 (S1), 107–113. https://doi.org/10.14006/j.jzjgxb.2015.S1.017 (2015). Li, Y., Xiao, R. & Sun, B. Study on design parameters of leaning-type arch bridges. Struct. Eng. Mech. 64 (2), 225–232. https://doi.org/10.12989/sem.2017.64.2.225 (2017). Liu, A., Huang, Y., Yu, Q. & Rao, R. An analytical solution for lateral buckling critical load calculation of leaning-type arch bridge. Math. Probl. Eng. 578473, (2014). https://doi.org/10.1155/2014/578473 (2014). Sun, J., Tan, Z., Zhang, J., Sun, W. & Zhu, L. Parameter sensitivity study on static and dynamic mechanical properties of the spatial Y-shaped tied arch bridge. Int. J. Steel Struct. 23 (2), 458–479. https://doi.org/10.1007/s13296-022-00705-z (2023). Pan, Z., Liu, A., Wang, J. & Yang, J. Experimental and numerical analysis of out-of-plane ultimate resistance of high-strength steel arches subjected to a central concentrated load. Eng. Struct. 346 , 121664. https://doi.org/10.1016/j.engstruct.2025.121664 (2026). 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Ltd","correspondingAuthor":false,"prefix":"","firstName":"Qinghui","middleName":"","lastName":"Lin","suffix":""},{"id":585681352,"identity":"79b02994-4e78-4afe-9e50-3f14aff8d779","order_by":2,"name":"Xiaoye Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYBACAxCRACLYmxsgQgeI1sJzkBQtYCCRSKwWieRjEg9qbBL7JR+2SfPuYJDju5HA+LkAr5a0ZIOEY2mJM2cnArWcYTCWvJHALD0Dr5YcwwcJbIcTN9wGaWljSNxwI4GNmQe/FoMDCf8OJ+6/eRCspZ4YLYYPEtuAtkgwgrUkGBDUwvMs2SCxL814xpnEZsu5bRKGM888bJbGp8W+PfmY5I9vNrL97YcP3njbZiPPdzz54Gd8WmDAsYGBgUUCGDtANmMDERqAtgEx8weilI6CUTAKRsGIAwDICUyAFHxfxAAAAABJRU5ErkJggg==","orcid":"","institution":"Longyan University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoye","middleName":"","lastName":"Luo","suffix":""},{"id":585681353,"identity":"37c62ffe-90b7-49b8-9663-7efb3dcf87b2","order_by":3,"name":"Zhitong She","email":"","orcid":"","institution":"Putian University","correspondingAuthor":false,"prefix":"","firstName":"Zhitong","middleName":"","lastName":"She","suffix":""},{"id":585681354,"identity":"d3190fe8-9816-4e4e-9142-2238473589f2","order_by":4,"name":"Wenjin Huang","email":"","orcid":"","institution":"Putian University","correspondingAuthor":false,"prefix":"","firstName":"Wenjin","middleName":"","lastName":"Huang","suffix":""},{"id":585681355,"identity":"dd25e760-db78-4d17-b258-a50b17d08475","order_by":5,"name":"Jialiang Zhou","email":"","orcid":"","institution":"Putian University","correspondingAuthor":false,"prefix":"","firstName":"Jialiang","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2026-01-29 18:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8734345/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8734345/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102293951,"identity":"9d4c3136-acc6-4314-8fcc-5506c73090dc","added_by":"auto","created_at":"2026-02-10 09:42:48","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":722226,"visible":true,"origin":"","legend":"\u003cp\u003eGeneral arrangement of the leaning-type steel box arch bridge (Unit: mm)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8734345/v1/16af124423647455bf7b41e2.jpeg"},{"id":102293992,"identity":"7458c6c6-b5ee-40d7-a13e-4a5e0b0225b6","added_by":"auto","created_at":"2026-02-10 09:43:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62653,"visible":true,"origin":"","legend":"\u003cp\u003eFinite element model of the leaning-type steel box arch bridge\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8734345/v1/5966c50f3f3a6e9e6bd1394c.png"},{"id":102293980,"identity":"ae96c806-3499-4b3d-99b1-a654de34d260","added_by":"auto","created_at":"2026-02-10 09:43:00","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":969800,"visible":true,"origin":"","legend":"\u003cp\u003eKey construction stages of the Wetland Park Bridge\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8734345/v1/e59a70d87b5695af8e3da36a.jpeg"},{"id":102293978,"identity":"d2b7f88c-ca89-4260-8751-f3daf0a9d9ac","added_by":"auto","created_at":"2026-02-10 09:42:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":132743,"visible":true,"origin":"","legend":"","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8734345/v1/e8f32fd67204c5d52fe54020.png"},{"id":102293993,"identity":"50e142c3-d6b7-4523-86ea-bdd16134d21c","added_by":"auto","created_at":"2026-02-10 09:43:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":31089,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of rise-to-span ratio on global stability coefficient\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8734345/v1/6a521e5622a8a3d40ac1bff4.png"},{"id":102293977,"identity":"3c38d29a-b359-4260-a722-996a183323dc","added_by":"auto","created_at":"2026-02-10 09:42:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":41250,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of inclining angle of leaning arch on the stability coefficient\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8734345/v1/28b1b787b59796a8203e093f.png"},{"id":102293991,"identity":"dbd4058c-4fc0-45fd-bab4-c57bf7cda6b7","added_by":"auto","created_at":"2026-02-10 09:43:03","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":36810,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of arch stiffness on the stability coefficient\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8734345/v1/b6bce42ebaa7c671bcc767e8.png"},{"id":102293976,"identity":"2ee2ce1f-30ad-4a80-986d-ffe7bc8b73d5","added_by":"auto","created_at":"2026-02-10 09:42:54","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":33443,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of transverse bracing stiffness on the stability coefficient\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8734345/v1/7a753e813700971198246ace.png"},{"id":102298074,"identity":"c4de8171-5ce4-4296-8be0-af00ae9355e4","added_by":"auto","created_at":"2026-02-10 10:30:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2831698,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8734345/v1/14a748a2-5b0f-426e-9842-bdc90ed59380.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stability performance of leaning-type steel box arch bridges: a parametric study based on a validated finite element model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, the design of urban bridges has increasingly emphasized not only structural functionality but also architectural quality and spatial integration within the urban environment\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. With the rapid growth of urban traffic demands, bridges are often required to accommodate wider decks with multiple traffic lanes and pedestrian facilities, while also serving as visual landmarks. This trend has stimulated the development of special-shaped and spatial arch bridges that combine structural efficiency with distinctive aesthetic characteristics, including butterfly-shaped arches, basket-handle arches, network arch bridges, and leaning-type arch bridges\u003csup\u003e\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. For such spatial structural systems, global stability is frequently a governing design criterion, as instability may control structural safety and material efficiency more critically than strength\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAmong various special-shaped arch systems, the leaning-type arch bridge has emerged as an effective solution for wide-deck urban bridges. This bridge type typically consists of two vertical main arches and two inclined leaning arches arranged on the outer sides, which are interconnected through transverse bracings to form an integrated spatial structural system\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Compared with conventional arch bridges relying solely on inter-rib bracing, the leaning-type configuration offers improved spatial transparency and enhanced adaptability to wide bridge decks, while maintaining a concise and expressive structural form. More importantly, the inclined leaning arches provide additional lateral restraint to the main arches, indicating a potentially significant contribution to the overall structural stability of the bridge system\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eExtensive research has been conducted on the mechanical behavior and stability of spatial arch bridges with complex geometries, such as butterfly arches, basket-handle arches, network arch bridges, and other inclined arch systems\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. These studies have demonstrated that parameters such as rise-to-span ratio, arch rib stiffness, and inclination angle can strongly influence internal force distribution and buckling behavior. For leaning-type arch bridges in particular, previous investigations have examined structural systems with or without horizontal thrust, analyzed internal force characteristics under dead loads, and explored the effects of selected geometric parameters on stability performance\u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite these efforts, several issues related to the stability behavior of leaning-type arch bridges remain insufficiently understood. First, many existing studies are based on simplified structural models or specific material systems, while systematic investigations grounded in real engineering projects and validated by field measurements are still limited. Second, although the stabilizing effect of leaning arches has been qualitatively recognized, their quantitative contribution to global stability\u0026mdash;especially for wide-deck steel box arch bridges\u0026mdash;has not been clearly established. Third, parametric studies have often focused on individual factors in isolation, making it difficult to identify the relative importance of key design parameters and to distinguish dominant factors from secondary ones. In addition, the influence of transverse bracing configuration, particularly the relative effectiveness of bracing stiffness versus bracing layout, has received comparatively little attention.\u003c/p\u003e \u003cp\u003eTo address these gaps, the present study investigates the stability performance of a leaning-type steel box arch bridge through a comprehensive parametric analysis based on a validated three-dimensional finite element model. A real-world leaning-type arch bridge with a wide deck is selected as a representative case, and the numerical model is verified against field-measured arch deflections and hanger forces. Using this validated model, the effects of the leaning arch system, rise-to-span ratio, arch rib stiffness, inclining angle of the leaning arches, and transverse bracing characteristics are systematically evaluated. The study aims to clarify the stability mechanisms of leaning-type arch bridges, identify the dominant parameters governing global stability, and provide practical insights to support the efficient and reliable design of such bridge systems.\u003c/p\u003e"},{"header":"Engineering background","content":"\u003cp\u003eThe Wetland Park Bridge in Quanzhou, China, is selected as the engineering background of this study. The bridge is a representative leaning-type steel box arch bridge designed to accommodate a wide deck while maintaining high structural efficiency and visual openness. The general arrangement of the bridge is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The superstructure adopts a three-span continuous arch-girder composite system with a span configuration of 14.9 m\u0026thinsp;+\u0026thinsp;82.2 m\u0026thinsp;+\u0026thinsp;14.9 m. The total bridge width varies from 44 m to 53 m, reflecting the functional requirement for multi-lane traffic and pedestrian facilities in an urban environment.\u003c/p\u003e \u003cp\u003eThe transverse structural system consists of two vertical main arches and two inclined leaning arches. The main arches are spaced 26.5 m apart and have a span of 78.33 m with a rise of 17.05 m, corresponding to a rise-to-span ratio of approximately 1/4.6. Each main arch adopts a single-cell steel box section with overall dimensions of 1800 mm \u0026times; 2000 mm and plate thicknesses ranging from 20 mm to 36 mm. The leaning arches are arranged symmetrically on the outer sides of the main arches and are inclined at an angle of 21\u0026deg; with respect to the vertical plane. They have a span of 122 m and a rise of 21.9 m and are constructed using single-cell steel box sections with dimensions of 1000 mm \u0026times; 1000 mm and plate thicknesses ranging from 20 mm to 36 mm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe main arches and leaning arches are interconnected by a system of transverse bracings to form an integrated spatial load-bearing structure. A total of fourteen transverse bracings, arranged in seven symmetric pairs, are provided along the arch span. Unlike conventional arch bridges where transverse bracings are typically installed only between main arches, the bracings in this bridge connect the main arches directly to the leaning arches. This configuration enhances the overall lateral stiffness of the arch system while improving visual transparency and driving comfort. The transverse bracings adopt single-cell steel box sections with cross-sectional dimensions of 700 mm \u0026times; 700 mm and a uniform plate thickness of 24 mm.\u003c/p\u003e \u003cp\u003eThe deck system consists of an orthotropic steel deck supported by a grid framework. Two primary longitudinal girders, fabricated as welded steel box sections with dimensions of 1600 mm \u0026times; 1800 mm and a plate thickness of 16 mm, are rigidly connected to the main arches to form an arch-girder composite structural system. This arrangement enables effective load transfer between the deck and the arches and contributes to the overall stiffness and load-carrying capacity of the bridge.\u003c/p\u003e \u003cp\u003eThe substructure comprises reinforced concrete solid piers and straight abutments, providing support for the entire arch-girder system in an externally statically determinate configuration. The piers primarily resist axial forces and bending moments induced by the superstructure. At the springings of the leaning arches, independent pile caps are constructed and monolithically connected to the main pier caps. This integrated foundation system ensures that the horizontal thrust generated by the leaning arches is collectively resisted by all foundation piles, thereby enhancing the stability and load distribution capacity of the substructure.\u003c/p\u003e"},{"header":"Finite element model","content":"\u003cp\u003e\u003cstrong\u003eEstablishment of the finite element model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA three-dimensional finite element model of the leaning-type steel box arch bridge was established to investigate its global stability behavior. The model explicitly represents the primary load-bearing components of the bridge, including the main arches, leaning arches, transverse bracings, longitudinal girders, and deck system.\u003c/p\u003e\n\u003cp\u003eThe arch ribs, transverse bracings, and longitudinal girders were modeled using beam elements, with cross-sectional properties defined according to the corresponding single-cell steel box sections. The deck system was simplified as an equivalent beam-girder system rigidly connected to the main arches, forming an integrated arch-girder composite structural system. This modeling approach captures the global stiffness and load transfer characteristics of the bridge while maintaining computational efficiency.\u003c/p\u003e\n\u003cp\u003eAll steel components were made of Q345 steel and were modeled as linear elastic. The reinforced concrete substructure was not explicitly modeled, instead, its restraining effect was represented through boundary conditions at the arch springings. The main arches were assumed to be fixed at their springings, while the leaning arches were connected to independent pile caps monolithically integrated with the main pier caps.\u003c/p\u003e\n\u003cp\u003eDead load was applied automatically based on the self-weight of structural components. Linear elastic eigenvalue buckling analysis was performed to evaluate the global stability coefficient of the bridge, and the first-order buckling mode was taken as the representative instability mode.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVerification of the finite element model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe accuracy of the finite element model was verified using field-measured data obtained during key construction stages and at the completed state of the Wetland Park Bridge. Both arch deflections and hanger tension forces were selected as verification indicators, as they are sensitive to the global stiffness characteristics and load-transfer behavior of the arch system.\u003c/p\u003e\n\u003cp\u003eThe Wetland Park Bridge was constructed using the falsework method. During construction, the deck grid beams were erected first, followed by the installation and closure of the main and leaning arches. Figure 3 presents photographs of the major construction stages, and the detailed construction sequence is summarized in Table 1. Based on the construction stages summarized in Table 1, three critical construction stages (CS5, CS8, and CS11) were selected for model verification, as these stages are associated with significant changes in structural stiffness and exhibit pronounced variations in arch deflections.\u003c/p\u003e\n\u003cp\u003eFigure 4 compares the calculated arch deflections obtained from the finite element model with the corresponding measured values at the selected construction stages. The results show that the numerical predictions are in good agreement with the field measurements for all three stages. Both the distribution patterns and magnitudes of arch deflections are well captured by the model. The maximum deviation between the calculated and measured deflections does not exceed 10%, indicating that the finite element model can reasonably reproduce the stiffness evolution of the bridge during construction.\u003c/p\u003e\n\u003cp\u003eIn addition to deflection verification, the accuracy of the finite element model was further assessed by comparing the calculated and measured hanger tension forces at the completed bridge state. Table 2 and Table 3 present a detailed comparison between calculated and measured hanger forces for the main and leaning arches. The results indicate that the maximum error in hanger tension for the main arches is -8.9%, while that for the leaning arches is 8.5%. These discrepancies remain within an acceptable range for engineering analysis, demonstrating that the finite element model can accurately reflect the internal force distribution of the completed structure.\u003c/p\u003e\n\u003cp\u003eBased on the comparison between numerical predictions and field measurements at multiple construction stages and in the completed bridge state, the established finite element model is considered sufficiently accurate and reliable for the subsequent stability analyses and parametric investigations presented in this study.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8253%;\"\u003e\n \u003cp\u003eConstruction stages\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81.1747%;\"\u003e\n \u003cp\u003eStage description\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8253%;\"\u003e\n \u003cp\u003eCS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81.1747%;\"\u003e\n \u003cp\u003eConstruction of substructure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8253%;\"\u003e\n \u003cp\u003eCS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81.1747%;\"\u003e\n \u003cp\u003eErection of deck girder falsework and installation of deck girders\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8253%;\"\u003e\n \u003cp\u003eCS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81.1747%;\"\u003e\n \u003cp\u003eErection of main arch falsework and closure of main arches\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8253%;\"\u003e\n \u003cp\u003eCS4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81.1747%;\"\u003e\n \u003cp\u003eErection of leaning arch falsework and closure of leaning arches\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8253%;\"\u003e\n \u003cp\u003eCS5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81.1747%;\"\u003e\n \u003cp\u003eDismantling of main arch falsework\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8253%;\"\u003e\n \u003cp\u003eCS6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81.1747%;\"\u003e\n \u003cp\u003eInstallation and tensioning of main arch hangers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8253%;\"\u003e\n \u003cp\u003eCS7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81.1747%;\"\u003e\n \u003cp\u003eWelding of transverse bracings No. 3\u0026ndash;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8253%;\"\u003e\n \u003cp\u003eCS8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81.1747%;\"\u003e\n \u003cp\u003eDismantling of leaning arch falsework\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8253%;\"\u003e\n \u003cp\u003eCS9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81.1747%;\"\u003e\n \u003cp\u003eInstallation and tensioning of leaning arch hangers to target forces\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8253%;\"\u003e\n \u003cp\u003eCS10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81.1747%;\"\u003e\n \u003cp\u003eWelding of remaining transverse bracings\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8253%;\"\u003e\n \u003cp\u003eCS11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81.1747%;\"\u003e\n \u003cp\u003eDismantling of deck girder falsework\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8253%;\"\u003e\n \u003cp\u003eCS12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81.1747%;\"\u003e\n \u003cp\u003eApplication of secondary permanent loads\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8253%;\"\u003e\n \u003cp\u003eCS13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81.1747%;\"\u003e\n \u003cp\u003eSecondary tensioning of main arch hangers to target forces\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eConstruction stages of the Wetland Park Bridge\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003eNO.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 143px;\"\u003e\n \u003cp\u003eCalculated value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 241px;\"\u003e\n \u003cp\u003eMeasured value - Main arch 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 241px;\"\u003e\n \u003cp\u003eMeasured value - Main arch 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eForce\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eForce\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 126px;\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e-5.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-8.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e6.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 126px;\"\u003e\n \u003cp\u003e-1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e-4.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 126px;\"\u003e\n \u003cp\u003e-4.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e-8.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 126px;\"\u003e\n \u003cp\u003e6.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e-7.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 126px;\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e6.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 126px;\"\u003e\n \u003cp\u003e-5.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e-7.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 126px;\"\u003e\n \u003cp\u003e7.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e5.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 126px;\"\u003e\n \u003cp\u003e-6.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e-6.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 126px;\"\u003e\n \u003cp\u003e-2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e-4.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 126px;\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 126px;\"\u003e\n \u003cp\u003e2.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e3.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 126px;\"\u003e\n \u003cp\u003e1.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eComparison between calculated and measured hanger forces for the main arches\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003eNO.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003eCalculated value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003eMeasured\u0026nbsp;value\u0026nbsp;- Leaning arch 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003eMeasured\u0026nbsp;value\u0026nbsp;- Leaning arch 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eForce\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eForce\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e47\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e5.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e46\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-2.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e47\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e45\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-4.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e6.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e52\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-4.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e48\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-8.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e65\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e65\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e64\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-2.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-8.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e66\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e6.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e58\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e58\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-1.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e3.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e3.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-3.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e60\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-5.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-5.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e89\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e85\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-4.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e86\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-2.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e60\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-2.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e62\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e3.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e65\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e6.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-8.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e62\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e5.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e55\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-6.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e6.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-6.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-6.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e65\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e65\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-6.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e52\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e7.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-2.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e47\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e8.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e44\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-6.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e47\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e47\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e48\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eComparison between calculated and measured hanger forces for the leaning arches\u003c/p\u003e"},{"header":"Effect of leaning arches on the global stability","content":"\u003cp\u003eTo clarify the effect of leaning arches on the global stability of the bridge, three structural systems were investigated and compared, as summarized in Table 3. These systems represent different lateral restraint strategies commonly adopted in wide-deck arch bridges.\u003c/p\u003e\n\u003cp\u003eStructural System 1 corresponds to a conventional configuration without leaning arches or transverse bracings. Structural System 2 excludes leaning arches but incorporates transverse bracings installed between the main arches. Structural System 3 represents the leaning-type arch bridge, in which inclined leaning arches are introduced and connected to the main arches through transverse bracings to form an integrated spatial structural system.\u003c/p\u003e\n\u003cp\u003eThe buckling modes and corresponding global stability coefficients obtained from linear elastic eigenvalue buckling analysis are presented in Table 4. For Structural System 1, the first-order buckling mode is characterized by out-of-plane symmetric buckling of a single main arch, and the corresponding stability coefficient is 20.5. When transverse bracings are added between the main arches (Structural System 2), the buckling mode changes to a global symmetric out-of-plane buckling mode involving both main arches, and the stability coefficient increases to 26.5, representing an improvement of approximately 29% compared with Structural System 1.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"724\" height=\"660\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eBuckling modes and global stability coefficients of different structural systems\u003c/p\u003e\n\u003cp\u003eWith the introduction of leaning arches and their connection to the main arches via transverse bracings (Structural System 3), the buckling behavior of the bridge exhibits a further transition. The first-order buckling mode becomes an out-of-plane antisymmetric mode involving the coupled deformation of both the main and leaning arches. The corresponding stability coefficient reaches 34.9, which is approximately 71% higher than that of Structural System 1.\u003c/p\u003e\n\u003cp\u003eThe comparison among the three structural systems clearly demonstrates that, for wide-deck arch bridges, the incorporation of leaning arches provides a substantially greater enhancement in global stability than that achieved by installing transverse bracings solely between the main arches. This result confirms the rationality and structural efficiency of the leaning-type arch bridge system in improving global stability performance.\u003c/p\u003e"},{"header":"Analysis of influencing parameters","content":"\u003cp\u003e\u003cstrong\u003eEffect of inclining angle of leaning arch\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe installation of leaning arches effectively enhances the lateral stability of wide-span arch bridges. By varying the inclining angle of the leaning arches, this study further investigates its influence on bridge stability. Two approaches are adopted to adjust the inclining angle of the leaning arches: (1) keeping the arch springing positions fixed while changing the lateral position of the arch crown, and (2) keeping the crown position fixed while adjusting the lateral positions of the arch springings. Figure 6 presents the stability coefficients corresponding to different inclining angles obtained using these two approaches.\u003c/p\u003e\n\u003cp\u003eAs illustrated in Figure 6, the variation trends of the stability coefficient differ depending on the method used to adjust the inclining angle. When the arch springing positions are fixed and the lateral position of the crown is varied, the stability coefficient initially increases and then decreases with increasing inclining angle, reaching a maximum value at an inclining angle of 21\u0026deg;. In contrast, when the crown position is fixed and the lateral positions of the arch springings are adjusted, the stability coefficient first decreases and then increases, attaining its minimum value at 21\u0026deg;.\u003c/p\u003e\n\u003cp\u003eIn terms of the magnitude of variation, taking the designed inclining angle of 21\u0026deg; as the reference, the maximum deviations of the stability coefficient within the inclining angle range of 12\u0026deg; to 27\u0026deg; are 2.3% and 3.7% for the two adjustment approaches, respectively. These results indicate that although variations in the inclining angle of the leaning arches have a measurable influence on bridge stability, the overall effect is relatively limited. Therefore, for leaning-type steel box arch bridges, the influence of the inclining angle of the leaning arches on global stability may be considered negligible. In practical design, the inclining angle can be primarily determined based on considerations such as the spatial arrangement of the arch springings and crown, as well as aesthetic requirements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect of arch stiffness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe bending stiffness of the arches is a key factor influencing the global stability of the bridge. Taking the designed bending stiffness of the Wetland Park Bridge as the reference case (1.0 \u003cem\u003eEI\u003c/em\u003e\u003csub\u003eM\u003c/sub\u003e for the main arches and 1.0 \u003cem\u003eEI\u003c/em\u003e\u003csub\u003eL\u003c/sub\u003e for the leaning arches), the cross-sectional bending stiffness of the main and leaning arches was varied independently to investigate their respective effects on global stability.\u003c/p\u003e\n\u003cp\u003eFigure 7 presents the first-order stability coefficients of the bridge corresponding to different arch stiffness values. It can be observed that the stability coefficient increases with increasing bending stiffness of both the main and leaning arches. In particular, an approximately linear relationship is observed between the stability coefficient and the bending stiffness of the main arches. When the stiffness of the main arches decreases from 1.0\u003cem\u003e\u0026nbsp;EI\u003c/em\u003e\u003csub\u003eM\u003c/sub\u003e to 0.5\u003cem\u003e\u0026nbsp;EI\u003c/em\u003e\u003csub\u003eM\u003c/sub\u003e, the stability coefficient decreases from 34.9 to 25.3, corresponding to a reduction of 27.5%. Conversely, when the stiffness increases from 1.0 \u003cem\u003eEI\u003c/em\u003e\u003csub\u003eM\u003c/sub\u003e to 2.0 \u003cem\u003eEI\u003c/em\u003e\u003csub\u003eM\u003c/sub\u003e, the stability coefficient increases from 34.9 to 45.6, representing an increase of 30.7%. These results indicate that the bending stiffness of the main arches has a pronounced influence on the global stability performance of the bridge.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy comparison, the influence of the bending stiffness of the leaning arches on the stability coefficient is relatively less significant. When the stiffness of the leaning arches increases from 0.5 \u003cem\u003eEI\u003c/em\u003e\u003csub\u003eL\u003c/sub\u003e to 1.0 \u003cem\u003eEI\u003c/em\u003e\u003csub\u003eL\u003c/sub\u003e, the stability coefficient increases from 28.8 to 34.9, corresponding to an increase of 17.5%. However, when the stiffness is further increased from 1.0 \u003cem\u003eEI\u003c/em\u003e\u003csub\u003eL\u003c/sub\u003e to 2.0 \u003cem\u003eEI\u003c/em\u003e\u003csub\u003eL\u003c/sub\u003e, the stability coefficient increases only from 34.9 to 38.6, an increment of 10.5%. Within the range of 0.5 \u003cem\u003eEI\u003c/em\u003e\u003csub\u003eL\u003c/sub\u003e to 1.0 \u003cem\u003eEI\u003c/em\u003e\u003csub\u003eL\u003c/sub\u003e, the rate of increase in the stability coefficient is higher than that observed in the range of 1.0 \u003cem\u003eEI\u003c/em\u003e\u003csub\u003eL\u003c/sub\u003e to 2.0 \u003cem\u003eEI\u003c/em\u003e\u003csub\u003eL\u003c/sub\u003e. Once the stiffness exceeds 1.0 \u003cem\u003eEI\u003c/em\u003e\u003csub\u003eL\u003c/sub\u003e, the stability coefficient tends to gradually level off, demonstrating the rationality of the designed stiffness for the leaning arches in the Wetland Park Bridge.\u003c/p\u003e\n\u003cp\u003eIn summary, since the cross-sectional stiffness of the main arches exerts a more significant influence on global stability than that of the leaning arches, the stability performance of leaning-type steel box arch bridges can be effectively enhanced by moderately increasing the cross-sectional dimensions of the main arches while reducing those of the leaning arches. This design strategy not only improves global stability but also contributes to material savings and enhances the overall aesthetic performance of the bridge.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect of transverse bracing stiffness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main and leaning arches of the leaning-type steel box arch bridge are interconnected by transverse bracings, which play an important role in ensuring the integrity and stability of the structural system. To investigate the influence of transverse bracing stiffness on global stability, the cross-sectional bending stiffness of the bracings was varied, with the designed stiffness (1.0 \u003cem\u003eEI\u003c/em\u003e \u003csub\u003eB\u003c/sub\u003e) taken as the reference case. Figure 8 presents the global stability coefficients of the bridge corresponding to different transverse bracing stiffness values.\u003c/p\u003e\n\u003cp\u003eThe results indicate that the global stability coefficient increases with increasing transverse bracing stiffness, although the magnitude of variation is relatively limited. When the bracing stiffness decreases from 1.0 \u003cem\u003eEI\u003c/em\u003e \u003csub\u003eB\u003c/sub\u003e to 0.5 \u003cem\u003eEI\u003c/em\u003e \u003csub\u003eB\u003c/sub\u003e, the stability coefficient decreases slightly from 34.9 to 32.0, corresponding to a reduction of 8.3%. Conversely, when the bracing stiffness increases from 1.0 \u003cem\u003eEI\u003c/em\u003e \u003csub\u003eB\u003c/sub\u003e to 2.0 \u003cem\u003eEI\u003c/em\u003e \u003csub\u003eB\u003c/sub\u003e, the stability coefficient increases from 34.9 to 38.6, representing a modest increase of 6.6%.\u003c/p\u003e\n\u003cp\u003eThese results suggest that the primary function of the transverse bracings is to connect the main and leaning arches into an integrated structural system, enabling them to resist loads in a coordinated manner. Although the cross-sectional stiffness of the transverse bracings does influence the global stability of the leaning-type steel box arch bridge, its effect is relatively limited compared with that of the arches themselves. Therefore, in practical design, excessively increasing the cross-sectional dimensions of the transverse bracings is unnecessary, and their stiffness may be determined primarily based on structural connectivity and construction requirements rather than stability considerations alone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect of number and arrangement of transverse bracings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Wetland Park Bridge is equipped with seven transverse bracings between the main and leaning arches on each side, which are sequentially numbered from 1 to 7. To investigate the influence of the number and arrangement of transverse bracings on the global stability of the leaning-type arch bridge, the stability coefficients corresponding to several representative bracing configurations were calculated, as summarized in Table 5.\u003c/p\u003e\n\u003cp\u003eAs shown in Table 5, the stability coefficient of the leaning-type arch bridge generally decreases as the number of transverse bracings is reduced. In addition to the number of bracings, the arrangement of the bracings is found to have a pronounced influence on global stability. A comparison of Cases 2 to 4, all of which contain five transverse bracings, indicates that the stability coefficient increases when the bracings are positioned closer to the arch springings.\u003c/p\u003e\n\u003cp\u003eSpecifically, when the number of bracings is reduced from seven to five by removing the bracings near the arch springings (Case 2), the stability coefficient decreases from 34.9 to 25.8, corresponding to a reduction of 26%. By contrast, when the bracings near the crown are removed while those close to the springings are retained (Case 4), the stability coefficient decreases only slightly from 34.9 to 34.1, representing a marginal reduction of 2.3%. These results indicate that, for the leaning-type steel box arch bridges, the bracing configuration adopted in Case 4 provides an effective balance between global stability performance and structural efficiency. This optimized arrangement not only maintains the stability coefficient at a level close to that of the original configuration but also reduces steel consumption and facilitates construction.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"724\" height=\"620\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003eStability coefficients under different transverse bracing configurations.\u003c/p\u003e\n\u003cp\u003eA similar trend is observed for configurations with three transverse bracings (Cases 5 to 7). When all three bracings are located in the crown region (Case 5), the stability coefficient is relatively low, with a value of 18.9. In contrast, when two of the three bracings are arranged near the arch springings (Case 7), the stability coefficient increases significantly to 28.3, corresponding to an increase of 49.7%. This comparison further confirms that the transverse bracings located near the arch springings play a more critical role in enhancing the global stability of the bridge than those arranged near the crown.\u003c/p\u003e"},{"header":"Summary and conclusions","content":"\u003cp\u003eThis study systematically investigated the stability performance of a leaning-type steel box arch bridge through a comprehensive parametric analysis based on a validated three-dimensional finite element model. The numerical model was rigorously verified against field-measured arch deflections and hanger forces obtained during key construction stages and in the completed bridge state. On this basis, the effects of the leaning arch system and several key design parameters on global stability were quantitatively evaluated. The main conclusions can be summarized as follows:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp; \u0026nbsp;The incorporation of leaning arches provides a substantial enhancement in global stability. Compared with a system without leaning arches, the stability coefficient increases by up to 71%, significantly outperforming configurations that rely solely on transverse bracings between the main arches. This confirms the structural efficiency and rationality of the leaning-type arch bridge system for wide-deck applications.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp; \u0026nbsp;The rise-to-span ratio of the main arches has a pronounced influence on global stability. An optimal range of approximately 1/4.5 to 1/7 is identified, within which the stability performance is maximized through a favorable balance between geometric stiffness and bending effects.\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp; \u0026nbsp;The bending stiffness of the main arches plays a dominant role in governing global stability. Doubling the stiffness of the main arches leads to an increase of 30.7% in the stability coefficient, whereas variations in the stiffness of the leaning arches result in comparatively smaller improvements.\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp; \u0026nbsp; \u0026nbsp;Transverse bracings contribute to global stability primarily through their configuration rather than their stiffness. While increasing transverse bracing stiffness leads to only limited improvements in the stability coefficient, the number and arrangement of transverse bracings have a pronounced effect. Bracings located near the arch springings are significantly more effective than those near the arch crown, and an optimized layout can increase the stability coefficient by up to 49.7% compared with non-optimal arrangements.\u003c/p\u003e\n\u003cp\u003e5.\u0026nbsp; \u0026nbsp; \u0026nbsp;The inclining angle of the leaning arches has a negligible influence on global stability within the investigated range. This allows the inclining angle to be primarily determined by architectural, spatial, and constructional considerations rather than stability requirements.\u003c/p\u003e\n\u003cp\u003eOverall, the findings of this study provide quantitative insight into the stability mechanisms of leaning-type steel box arch bridges and highlight the relative importance of key geometric and structural parameters. The results offer practical guidance for the efficient and reliable design of leaning-type arch bridge systems, particularly for wide-deck urban applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAdditional information\u003c/h2\u003e \u003cp\u003eCorrespondence and requests for materials should be addressed to Xiaoye Luo.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was supported by Startup Fund for Advanced Talents of Putian University (grant number 2022057), Fujian Provincial Natural Science Foundation of China (grant number 2025J01389 and 2025J011696), Science and Technology Program Project of Putian City (grant number 2025SZ3001PTXY07).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZhimin She: Conceptualization, Funding acquisition, Methodology, Formal analysis, Investigation, Data curation, Writing - original draft. Qinghui Lin: Conceptualization, Methodology, Formal analysis, Data curation, Writing - original draft. Xiaoye Luo: Methodology, Funding acquisition, Data curation, Formal analysis. Zhitong She: Methodology, Data curation. Wenjin Huang: Conceptualization, Supervision. Jialiang Zhou: Writing - Review \u0026amp; Editing, Visualization.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data supporting the findings in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHan, Z. Aesthetics Innovation and practice of urban bridge design. \u003cem\u003eStruct. Eng. Int.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e (4), 543\u0026ndash;549. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10168664.2020.1848368\u003c/span\u003e\u003cspan address=\"10.1080/10168664.2020.1848368\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia, Q. et al. System design and demonstration of performance monitoring of a butterfly-shaped arch footbridge. \u003cem\u003eStruct. Control Hlth\u003c/em\u003e. \u003cb\u003e28\u003c/b\u003e (7), e2738. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/stc.2738\u003c/span\u003e\u003cspan address=\"10.1002/stc.2738\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlcayde, A. et al. Basket-Handle Arch and its optimum symmetry generation as a structural element and keeping the aesthetic point of view. \u003cem\u003eSymmetry-Basel\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (10), 1243. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/sym11101243\u003c/span\u003e\u003cspan address=\"10.3390/sym11101243\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDanciu, A. D. et al. A review of the network arch bridge. \u003cem\u003eAppl. Sci-Basel\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e (19), 10966. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/app131910966\u003c/span\u003e\u003cspan address=\"10.3390/app131910966\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y. Design strategies for leaning-type arch bridges. \u003cem\u003eJ. World Archit.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (2), 11\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.26689/jwa.v7i2.4752\u003c/span\u003e\u003cspan address=\"10.26689/jwa.v7i2.4752\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, Y., Jin, T., Wu, W. \u0026amp; Lu, P. Structural innovation design and mechanical performance of double-deck large-span steel truss arch bridges. \u003cem\u003eMech. Based Des. Struc\u003c/em\u003e. 1\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15397734.2025.2574885\u003c/span\u003e\u003cspan address=\"10.1080/15397734.2025.2574885\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, Y. et al. A Study on the ultimate span of a concrete-filled steel tube arch bridge. \u003cem\u003eBuildings\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (4), 896. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/buildings14040896\u003c/span\u003e\u003cspan address=\"10.3390/buildings14040896\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao, R., Sun, H., Jia, L. \u0026amp; Sun, B. Kunshan Yufeng Bridge \u0026mdash; design of the first long-span leaning - type arch bridge without thrust. \u003cem\u003eChina Civ. Eng. J.\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e (1), 78\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15951/j.tmgcxb.2005.01.010\u003c/span\u003e\u003cspan address=\"10.15951/j.tmgcxb.2005.01.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, A. et al. Experimental research on stable ultimate bearing capacity of leaning-type arch rib systems. \u003cem\u003eJ. Constr. Steel Res.\u003c/em\u003e \u003cb\u003e114\u003c/b\u003e, 281\u0026ndash;292. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jcsr.2015.08.011\u003c/span\u003e\u003cspan address=\"10.1016/j.jcsr.2015.08.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, G., Ren, W., Zhu, Y. \u0026amp; Hussain, S. M. Static and dynamic evaluation of a butterfly-shaped concrete-filled steel tube arch bridge through numerical analysis and field tests. \u003cem\u003eAdv. Mech. Eng.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (9), 1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/16878140211044671\u003c/span\u003e\u003cspan address=\"10.1177/16878140211044671\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun, J., Chen, S., Wang, Z., Sui, W. \u0026amp; Zhang, Q. Study of the impact of varying inclination angles of arch ribs on the seismic behavior of half-through steel basket-handle arch bridge. \u003cem\u003eBuildings\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (3), 794. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/buildings14030794\u003c/span\u003e\u003cspan address=\"10.3390/buildings14030794\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang, Z. et al. An analytical method for out-of-plane stability assessment of network arch bridges. \u003cem\u003eThin-Walled Struct\u003c/em\u003e. \u003cb\u003e204\u003c/b\u003e, 112289. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tws.2024.112289\u003c/span\u003e\u003cspan address=\"10.1016/j.tws.2024.112289\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu, X. et al. Case study on stability performance of asymmetric steel arch bridge with inclined arch ribs. \u003cem\u003eSteel Compos. Struc\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e (1), 273\u0026ndash;288. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.12989/scs.2015.18.1.273\u003c/span\u003e\u003cspan address=\"10.12989/scs.2015.18.1.273\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Y., Liu, Y., Liang, Y. \u0026amp; Zhang, S. Nonlinear stability analysis and completed bridge test on slanting type CFST arch bridges. \u003cem\u003eJ. Build. Struct.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e (S1), 107\u0026ndash;113. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14006/j.jzjgxb.2015.S1.017\u003c/span\u003e\u003cspan address=\"10.14006/j.jzjgxb.2015.S1.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Y., Xiao, R. \u0026amp; Sun, B. Study on design parameters of leaning-type arch bridges. \u003cem\u003eStruct. Eng. Mech.\u003c/em\u003e \u003cb\u003e64\u003c/b\u003e (2), 225\u0026ndash;232. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.12989/sem.2017.64.2.225\u003c/span\u003e\u003cspan address=\"10.12989/sem.2017.64.2.225\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, A., Huang, Y., Yu, Q. \u0026amp; Rao, R. An analytical solution for lateral buckling critical load calculation of leaning-type arch bridge. \u003cem\u003eMath. Probl. Eng.\u003c/em\u003e 578473, (2014). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2014/578473\u003c/span\u003e\u003cspan address=\"10.1155/2014/578473\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun, J., Tan, Z., Zhang, J., Sun, W. \u0026amp; Zhu, L. Parameter sensitivity study on static and dynamic mechanical properties of the spatial Y-shaped tied arch bridge. \u003cem\u003eInt. J. Steel Struct.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (2), 458\u0026ndash;479. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13296-022-00705-z\u003c/span\u003e\u003cspan address=\"10.1007/s13296-022-00705-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan, Z., Liu, A., Wang, J. \u0026amp; Yang, J. Experimental and numerical analysis of out-of-plane ultimate resistance of high-strength steel arches subjected to a central concentrated load. \u003cem\u003eEng. Struct.\u003c/em\u003e \u003cb\u003e346\u003c/b\u003e, 121664. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.engstruct.2025.121664\u003c/span\u003e\u003cspan address=\"10.1016/j.engstruct.2025.121664\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2026).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Leaning-type arch bridge, Global stability, Finite element analysis, Parametric study, rise-to-span ratio","lastPublishedDoi":"10.21203/rs.3.rs-8734345/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8734345/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLeaning-type arch bridges have become increasingly attractive for wide-deck urban applications due to their distinctive aesthetics and enhanced spatial stiffness. By introducing inclined leaning arches connected to vertical main arches through transverse bracings, this structural system offers an effective alternative to conventional lateral bracing schemes. However, the stability mechanisms of leaning-type arch bridges and the influence of key design parameters have not yet been fully clarified. In this study, the stability performance of a leaning-type steel box arch bridge is systematically investigated using a three-dimensional finite element model validated against field measurements of arch deflections and hanger forces. A comprehensive parametric analysis is conducted to quantify the effects of the leaning arch system, rise-to-span ratio, arch stiffness, and transverse bracing configuration on global stability. The results show that the incorporation of leaning arches significantly enhances lateral stability, increasing the stability coefficient by up to 71% compared with systems without leaning arches. An optimal rise-to-span ratio for the main arches is identified in the range of 1/4.5 to 1/7, balancing geometric stiffness and structural efficiency. The bending stiffness of the main arches is found to govern global stability, whereas the inclining angle of the leaning arches and the stiffness of transverse bracings have comparatively limited influence. Moreover, transverse bracings located closer to the arch springings are shown to be more effective in improving stability than those near the arch crown. These findings provide quantitative insight into the stability mechanisms of leaning-type arch bridges and offer practical guidance for their efficient and reliable structural design.\u003c/p\u003e","manuscriptTitle":"Stability performance of leaning-type steel box arch bridges: a parametric study based on a validated finite element model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 09:42:32","doi":"10.21203/rs.3.rs-8734345/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"283963858128783602271990640889554934773","date":"2026-05-16T12:23:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T08:21:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78769275953938660937534439809167781176","date":"2026-05-13T09:05:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307077866709952797594292905846330476213","date":"2026-05-12T12:02:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T06:51:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"239433538720698829076466146301590918320","date":"2026-02-11T05:15:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50369534673394883941161865910927468612","date":"2026-02-10T15:14:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-04T11:22:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-04T08:21:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-31T11:22:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-31T11:20:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-29T17:55:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"489cbbf8-2578-4151-9c11-7e39cb69e5c7","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"283963858128783602271990640889554934773","date":"2026-05-16T12:23:14+00:00","index":67,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T08:21:45+00:00","index":66,"fulltext":""},{"type":"reviewerAgreed","content":"78769275953938660937534439809167781176","date":"2026-05-13T09:05:48+00:00","index":63,"fulltext":""},{"type":"reviewerAgreed","content":"307077866709952797594292905846330476213","date":"2026-05-12T12:02:05+00:00","index":61,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62310427,"name":"Physical sciences/Engineering"},{"id":62310428,"name":"Physical sciences/Materials science"}],"tags":[],"updatedAt":"2026-02-10T09:42:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 09:42:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8734345","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8734345","identity":"rs-8734345","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}