Comparative Analysis of Smart and Traditional Evacuation Strategies in Underground Mines Utilizing Virtual Reality Simulations

Comparative Analysis of Smart and Traditional Evacuation Strategies in Underground Mines Utilizing Virtual Reality Simulations | 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 Research Article Comparative Analysis of Smart and Traditional Evacuation Strategies in Underground Mines Utilizing Virtual Reality Simulations Simone Gaab, Amin Moniri-Morad, Frederick C. Harris, Javad Sattarvand This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4320209/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Underground mining operations inherently involve significant risks, such as the collapse of surrounding rock or fires. While infrequent, the potentially catastrophic nature of these events highlights the essential need for swift and secure evacuation procedures, ensuring the safety and survival of mineworkers in such situations. The traditional static evacuation strategy, relying on exit signs or guidance markers, may become blocked or barely discernible in low-visibility circumstances resulting from smoke or dust. This paper investigates the feasibility of integrating smart evacuation technology into underground mine operations by examining its effectiveness compared to traditional practices. This procedure involves real-time evacuation guidance of individuals along optimized paths to egress, effectively avoiding danger zones. A virtual simulation environment was built based on the actual layout of a gold mine in Nevada. In this instance, the simulation process involved evacuating a group of participants employing both traditional practices and smart evacuation strategies equipped with live assistance. The findings revealed a notable contrast in the effectiveness of the total evacuation duration between the traditional and smart evacuation strategies. The smart method achieved the most substantial decrease in total evacuation time, almost a 40% reduction. Additionally, 83% of participants expressed a preference for the smart evacuation strategies compared to the traditional practices, with all participants agreeing that the smart evacuation strategy has the potential to improve mine safety. Fire Emergency Wayfinding Smart Evacuation Simulation Virtual Reality (VR) Underground Mining Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Essential mineral reserves like copper, platinum, and gold are commonly excavated from the earth to sustain modern human society. Among the various mining methods, underground mining stands out as one of the common methods for obtaining these resources, characterized by excavated drift networks within the ground. Chile's El Teniente, one of the largest underground mine globally, boasts an extensive drift network spanning 1900 miles (Jamasmie, 2015 ). Excavating rock underground poses inherent risks that may prompt mine evacuations when incidents arise. In such emergencies, every minute is crucial for miners' health and survival. Typically, the primary evacuation destination is the mine's main entryway, like a shaft or ramp. Refuge chambers, if available, provide temporary safe locations until the arrival of the rescue team, who then facilitate the transportation of individuals to the surface. According to data from the MSHA database, there were 127 recorded fires in underground metal/nonmetal mines and 122 recorded fires in the United States' underground coal mines between 2000 and 2021 (National Institute for Occupational Safety and Health - NIOSH, 2022 ). Despite the implementation of safety measures mandated by mine safety laws, such as the 2006 MINER Act, a study examining escape plans for underground coal mines noted a deficiency in literature concerning contemporary mining procedures and the impacts of the MINER Act (Alexander et al., 2010 ). Additionally, the research highlights the absence of US-based studies or technology dissemination within the broader US mine emergency response framework, contrasting with other industries and mining methodologies abroad. Numerous endeavors have investigated intelligent evacuation guidance systems across various settings, including highway tunnels, occupational buildings, and other domains, as demonstrated by the works of Inoue et al. (Inoue et al., 2008 ), Chen (C.-Y. Chen, 2009 ), Kobes et al. (Kobes et al., 2010 ), Ahn and Han (Ahn & Han, 2011 ), Ronchi et al. (Ronchi et al., 2015a ), Bernardes et al. (Bernardes et al., 2015 ), Cosma et al. (Cosma et al., 2016a ), Ronchi et al. (Ronchi et al., 2016 ), Galea et al. (Galea et al., 2017 ), Majumder et al. (Majumder et al., 2017 ), Lujak et al. (Lujak et al., 2017 ), Arias et al. (Arias, La Mendola, et al., 2019 ), and Damaševičius et al. (Damaševičius et al., 2023 ). Despite notable advancements in these sectors, the integration of smart evacuation systems within the mining industry remains largely unexplored. Present regulations in mining primarily rely on conventional route marking for evacuation procedures in underground non-coal mines, as mandated by law (30 CFR § 57.11051 - Escape Routes, 1985). Although a few studies, such as those by Jalali and Noroozi ( 2009 ), Barker-Read and Li ( 1989 ), Rehman et al. ( 2019 ), and Jha et al. (Jha et al., 2022 ), have initiated exploration into smart evacuation strategies in underground mining settings, comprehensive implementation has yet to materialize. An effective smart evacuation strategy for underground mining operations necessitates continuous monitoring of individuals' whereabouts within the mine using advanced technologies such as the Internet of Things (IoT). This involves the deployment of devices such as beacons, Wi-Fi signal strength indicators, and Radio Frequency Identification (RFID) tags. Additionally, environmental surveillance is crucial for detecting hazards and preventing risks, employing tools such as smoke detectors, thermometers, and a range of sensors. Wireless communication systems, particularly wireless mesh networks, are resilient in harsh mining conditions, ensuring functionality even in the event of individual node failures (Carrier, 2018 ; Strickland, 2008 ). Furthermore, various strategies, such as smart exit signs with adaptable direction indicators or personal devices like smartwatches or smartphones can aid in guiding individuals through the mine. These devices allow for customization of localization and navigation for each occupant, enhancing overall safety and efficiency. Several studies have scrutinized the conventional static evacuation strategy within the mining sector. Brenkley et al. ( 1999 ) pointed out challenges associated with maintaining reflective signs and symbols, their reduced visibility in adverse conditions, and the limited attention from miners. Martell et al. (Martell et al., 2020 ) similarly observed that exit route signs may become significantly less conspicuous in situations of poor visibility. Furthermore, Chen et al. (J. Chen et al., 2018 ) and Jeon et al. (Jeon et al., 2011 ) illustrated those conditions of low visibility, such as those induced by smoke, can prolong evacuation durations. It is imperative to give due consideration to the design and placement of exit signs or wayfinding systems, as these aspects can impact evacuation efficiency. Virtual Reality (VR) has been widely utilized across diverse domains for its capacity to generate computer-simulated three-dimensional environments using techniques like photogrammetry and point cloud generation (Gaab, 2019 ; Kamran-Pishhesari et al., 2024 ; Valencia et al., 2024 ). R has demonstrated its effectiveness in assessing emergency evacuation behavior (Kinateder et al., 2014 ; Kinateder & Warren, 2016 ; Ronchi et al., 2015b ) and enhancing wayfinding systems in highway tunnels (Arias, Wahlqvist, et al., 2019 ; Cosma et al., 2016b ; Ronchi et al., 2015b ). Among recent VR technologies, CAVE and 360° screens incorporate a Head-Mounted Display (HMD) equipped with separate displays for each eye, providing users with an expansive 200° field of vision (Varjo Co., 2023). In the mining sector, numerous research endeavors have investigated VR applications, including safety and hazard training (Marsh et al., 2023 ; Nel et al., 2011 ; Orr et al., 2009 ; Stothard et al., 2008 ; Tichon & Burgess-Limerick, 2011 ), hazard evaluation and risk reduction (Isleyen & Duzgun, 2019 ), and the remote operation of equipment using VR (Foster & Burton, 2004 ). Hence, VR technologies present an innovative solution for implementing smart evacuation systems in underground mining operations. Recent research has shown that the adoption of real-time, personalized evacuation guidance strategies can significantly reduce evacuation times, thus improving safety in mining operations. These systems, by offering customized guidance, help prevent disorientation and mitigate biases in decision-making. This study seeks to deepen our understanding by conducting a thorough examination of the effectiveness of smart underground mine evacuation approach, comparing it with traditional strategies. The smart evacuation strategy proposed in this research integrates advanced features like beacon technology, evacuation optimization algorithms, and smartwatch navigation to provide optimal evacuation routes for underground mine workers. Utilizing virtual reality (VR) simulations, we replicated underground mine evacuation scenarios based on the drift network layout of an actual gold mine in Nevada, USA. Additionally, we created a simulation game that mimics real underground mine environments and emergency situations, enabling us to evaluate evacuation performance. Furthermore, the study explores the influence of low-visibility conditions and lighting on evacuation efficiency. Through these comprehensive analyses, this research offers valuable insights into the potential of smart evacuation strategies to enhance safety and productivity in underground mining operations. The structure of this paper is as follows. Section 2 provides the proposed theoretical and experimental approaches for this study. Section 3 delves into the obtained results from the analyses, providing an accurate insight into the problem. Finally, Section 4 presents the conclusions and notable remarks. 2. Materials and Methods Existing evacuation guidance strategies used in underground mining are not cutting-edge and may encounter significant challenges in situations of poor visibility or varying environmental conditions. These systems are anticipated to reduce evacuation durations, offer enhanced visibility even in extreme conditions, provide clear and unambiguous cues through visual, audible, and tactile means, require cost-effective maintenance, easily adapt to frequent layout changes in the mine, ensure reliability, and function independently of ambient lighting (D. Brenkley et al., 1999). This research aims to explore whether a new approach to evacuation guidance could expedite underground mine evacuations, mitigate the risk of entrapment, and ultimately, safeguard the lives of miners. 2.1. Experimental Setup The simulations took place within a virtual reality (VR) representation of an underground mine. Opting for a virtual environment for the simulations served to expedite the process, conserve resources, and eliminate the need to expose test participants to the inherent dangers of actual underground mining environments. To replicate real-world conditions, a virtual counterpart resembling an operational gold mine in Nevada was constructed for the simulations. Study volunteers engaged in two evacuation scenarios: one utilizing traditional exit signage and the other employing a novel smart evacuation wayfinder strategy. Throughout the evacuation simulations, participants were rerouted to alternative exits if they encountered obstacles, with metrics recorded including total evacuation time and response time to obstacles or directional changes. Additionally, both pre- and post-simulation surveys were administered to gather data on participants' familiarity with VR technology and underground mining, personal preferences, and their VR experience during the simulation. Subsequently, statistical analysis was employed to assess the efficiency of the smart evacuation method compared to the traditional strategy, with a focus on quantifying potential time savings. The projected advantages of the smart evacuation approach over traditional methods were evaluated based on two hypotheses: (A) Smart evacuation facilitates quicker evacuation and (B) Smart evacuation enhances decision-making confidence. 2.2. Virtual Environment (VE) To develop these simulations, categorized as "serious" games, we utilized the Unity 2018.3.2f1 game engine. Unity serves as a software development environment for crafting games across various platforms, including Windows, Android, iOS, and PlayStation. Additionally, constructing intricate games within Unity typically involves scripting in C#. In this instance, the layout of the mine was obtained from mine planning software, Vulcan, and brought into Unity as central line strings representing the mine drift. Then, an HTC VIVE Head-mounted display and a Virtuix Omni treadmill for locomotion were employed to facilitate interaction within the VE (Figure 1). As part of the setup process, users don the Omni overshoes equipped with tracking sensors before stepping onto the Omni treadmill to commence walking. These specialized overshoes ensure precise tracking of users' movements within the virtual environment. The arrangment of the Omni base, coupled with low-friction shoes, allows users' feet to glide back to the center after each stride, enabling natural movement. This method contrasts with traditional VR locomotion techniques like teleportation or flying. Given that actual movement in the desired direction aids navigation in a three-dimensional world, prompting the selection of the Virtuix Omni treadmill. Indeed, the treadmill furnishes participants with essential idiothetic (self-motion) cues (Chance et al., 1998; Jansen-Osmann & Fuchs, 2006; Mallot et al., 1998; Sharma et al., 2017). The VE is modeled after a genuine underground gold mine located in Northern Nevada. As depicted in Figure 2, the top four levels of the mine (Levels 900, 1250, 1550, and 1715) were recreated. Due to constraints related to computational resources, labor, and overall effort, it was not feasible to produce a true-to-scale and highly detailed depiction of the mine. Consequently, the reconstruction maintains a conceptual nature. In this simulation, the primary focus was on replicating key aspects of the mine layout, notably the positioning of evacuation points such as shafts and refuge chambers, along with the placement of egress route signs and maps. These elements were strategically arranged with input from a former mine employee who provided insights based on the actual layout of the mine serving as the model for the VE. This meticulous process ensured that the simulated conditions closely mirrored those of the real mine. As a result, any potential bias from the experimenter was mitigated, particularly concerning the critical placement of exit route signs, which could significantly impact the outcomes of the simulation. The simulation sought to offer an impartial assessment of the two distinct evacuation wayfinding systems by faithfully reproducing these conditions, as illustrated in Figure 3. 2.3. Experimental Design and Evacuation Scenarios Before commencing the study, approval was obtained from the Institutional Review Board (IRB) of the University of Nevada, Reno to conduct experiments involving human subjects for this specific research endeavor. The experiments were conducted using a within-subject design to evaluate the effectiveness of each evacuation strategy. Additionally, an initial phase was established to minimize any learning curve when shifting from the traditional to the smart strategy. Throughout this phase, participants familiarized themselves with the haptic feedback, movement controls, and Head-Mounted Display (HMD) utilized in the simulation and VR environment until they felt "comfortable" with their usage. This step aimed to reduce differences in the gaming experience, allowing less experienced participants to learn. The experimental setup operated under the assumption that the majority of participants lacked prior experience with VR. Following the completion of the initial phase, participants commenced the mine evacuation simulations. In every simulation, participants were directed to the initial evacuation destination using either conventional signs or an intelligent wayfinder. However, upon reaching the first evacuation point, participants were redirected to the second point, as the first one became inaccessible during the simulation process. In this case, a timer was utilized to record the variable "total time" at the beginning of each simulation, aiming to assess the duration from the initial point to reaching the endpoint. Figure 5 illustrates the sequence of simulations, with illuminated simulations denoted by "I" occurring at the same mine level. Both sets of simulations were conducted under dark conditions ("D"). Contrary to illuminated conditions, the Field-of-View (FOV) was notably reduced in the dark, affecting visibility down the drift/path compared to the illuminated simulations (see Figure 4). To maintain consistency, identical mine levels were chosen for both traditional ("C") and smart strategies ("S"), ensuring comparable complexity in terms of possible directions and intersections along the shortest route. To mitigate potential memory bias, distinct evacuation routes were utilized for each strategy, while ensuring consistency in metrics such as number of curves, distance to the final target, occurrence time, and obstacles. The study concluded with a post-survey utilizing the Presence Questionnaire by Witmer and Singer (Witmer & Singer, 1998) to evaluate participants' sense of presence and gather insights into their preferences regarding evacuation methods, including perceptions of smart evacuation's efficacy compared to traditional approaches. 2.4. Result Documentation The simulation commences when the examiner initiates recording by pressing the "Start Recording" button, capturing the user's in-game view. The examiner retains the ability to conclude the simulation by turning off the play button whenever necessary. Once the game concludes, the recording is saved on the local drive. A script was created to capture a top-down screenshot of the mine level every five seconds, showing the player's current location and path. Consequently, the recorded video and screenshots enable analysis of areas where the player encountered difficulty in navigation. Notably, the path trail is visible solely in these screenshots and remains hidden from the user within the game interface. In this case, four text documents are generated, each indicating the date and start time. Upon activation of the trigger, a user interface resembling the one depicted in Figure 6 will appear for five seconds, delivering the message: "The mine shaft is full! Seek refuge in a chamber!" or "The refuge chamber is occupied! Head towards the shaft!" contingent upon the simulation conditions. Information regarding the step count and hip-ring angle of the Omni is captured at two-second intervals, allowing for the depiction of variations over time through graphical representation. This data aids in assessing how frequently the user adjusted direction or explored alternate routes in different simulation scenarios. 3. Results and Discussion The evacuation simulation process involved selecting different participants, all of whom were familiar with underground mining operations and trained for the evacuation process. Therefore, the analysis was performed based on the results of simulating the evacuation process in an underground mine emergency. 3.1 Quantitative Data Analysis Figure 7 (a) indicates a comparison of the average evacuation durations for each simulation scenario: SD (Mean = 183.56, Std = 49.76), SI (Mean = 198.23, Std = 47.41), CD (Mean = 220.89, Std = 52.36), and CI (Mean = 328.46, Std = 103.58). As depicted, the mean evacuation duration with smart evacuation strategies proved shorter compared to the duration required with the traditional strategies. This difference arises from providing optimal directions to people via smartphones, eliminating the need for them to make decisions themselves to find the shortest route to the refugee chamber and instead focusing their attention solely on the smartphone's guidelines. They do not need to inspect their environment or markings on the underground mine walls; their sole task is to follow the directions on the smartphones and reach the refugee chambers. Furthermore, it is notable that, on average, participants completed their mission more quickly under the SD and CD conditions compared to the SI and CI conditions. Therefore, despite the limited FOV in CD, participants exceeded the mean duration achieved in CI by 32.75%. This difference can be attributed to the fact that the illuminated emergency situation provides a wider FOV, leading to distraction as individual focus on unnecessary markings and situations. Conversely, dark conditions provide a restricted FOV, guiding individuals' focus towards evacuation directions. Figure 7 (b) depicts the variation in collected data for all scenarios. Notably, the standard deviation for CI stands at 103.58, markedly surpassing that of SD (49.76), CD (52.36), and SI (47.41). In the CI dataset, total evacuation times exhibited some variability around the mean, varying from 178 to 521 seconds. Although 521 initially seems as an outlier in the CI dataset, examination within the acceptable data range (121.30 to 535.62), defined as two standard deviations from the mean, verifies that all data points fall within the range. Evacuation times in CI show less consistency compared to SD, CD, and SI scenarios. The disparity in standard deviations among SD, SI, and CD is not notably high. To ascertain notable disparities among the scenarios, a matched-pair t-test is performed, contrasting each approach with the others, as delineated in Table 1 . Table 1 The examination of statistical significance regarding the variation in total times across the scenarios Time Difference Comparison (x̄ 1 > x̄ 2 ) Average Time Reduction Percentage Statistical Significance of Time Reduction (p-value) SI - SD 7.4% Significant (0.038) CI - CD 32.75% Significant (0.019) CD - SD 16.90% Significant (0.022) CI - SI 39.65% Significant (0.0006) For each p-value below the predetermined significance threshold of 5%, it demonstrates a notable distinction among the datasets of the compared scenarios. Across all scenarios, there existed a marked discrepancy in total time. Also, the examination results revealed that smart evacuation strategies consistently outperformed the traditional strategy in terms of speed. Additionally, experiments showed that evacuations conducted in dark situations were notably swifter than those in illuminated surroundings. The most substantial decrease in average time, at 39.65%, was observed between CI and SI. Additionally, there was a time reduction of 32.75% between CI and CD. Hence, evacuations proceeded more quickly in dark environments compared to illuminated ones. A p-value of 0.022 for CD versus SD highlights a notable distinction, signifying that SD scenarios notably advanced at a faster pace, resulting in a 16.90% reduction in average total time. When contrasting SI with SD, the p-value of 0.038 is close to reaching statistical significance, with a corresponding time reduction of 7.4%. 3.2. Analysis of Qualitative Data Figure 8 illustrates the frequency of participants' turning actions using the Omni in both SI and CI scenarios, reflecting alterations in walking direction or shifts in observation. Analyzing the number and distribution of vertical lines in both plots (Fig. 8 ) indicates a greater occurrence of turning among participants using the traditional evacuation strategy. This implies that certain participants may have deviated from the optimal route, necessitating redirection or confirmation of their path choice. In contrast, participants utilizing the smart evacuation strategy predominantly adhered to the arrow guidance. The consistent recurrence of turning patterns in SI indicates a reduced level of uncertainty regarding the correct route, with most participants making similar turns at comparable points. It is important to note that both conditions featured an equal number of 90° and 180° turns. Examination of the corresponding plots for SD and CD does not indicate a significant difference in the frequency or order of turning movements between the traditional and smart strategies. A post-survey also provided qualitative insights into participants' sense of immersion in the virtual environment and their individual preferences. Additionally, the study examined the potential impact of the equipment on participants' performance. Table 2 displays the average ratings for the Presence Questionnaire on a Likert scale ranging from 1 to 7. The achieved results revealed that most participants experienced a high level of immersion in the virtual environment, and the use of the treadmill for locomotion did not adversely affect their performance. While one participant found the experience intriguing and realistic, another reported motion sickness possibly due to perceived virtual world instability. Table 2 The results of the defined Questionnaire Number Questions Mean (x̄) Standard Deviation (σ) 1 To what extent did the mechanism governing movement through the environment feel natural? (Extremely artificial – Very natural) 3.84 1.50 2 To what degree did the information from your different senses feel inconsistent or disconnected? (Not at all – Completely) 3.38 1.92 3 To what extent did your virtual experiences align with your real-world experiences? (Not consistent – Very consistent) 4.88 1.38 4 To what extent could you thoroughly explore or examine the environment visually? (Not at all – Completely) 6.40 0.64 7 How disoriented did you feel at the beginning of breaks or the end of the experimental session? (Not disoriented – Very disoriented) 1.79 0.92 8 How rapidly did you adapt to the virtual environment? (Not at all – Within 2 minutes) 6.38 0.96 9 How skilled did you feel in navigating and engaging with the virtual environment by the end of the session? (Not skilled – Very skilled) 5.57 1.61 10 To what extent did the visual display quality impede or distract you from completing assigned tasks and necessary activities? (No interference - Task performance hindered) 2.15 1.34 11 To what degree did the control devices disrupt the execution of allocated activities or tasks? (No disruption - Task performance prevented) 2.68 1.64 Overall, 83% of the participants expressed a preference for smart evacuation strategies over the traditional strategy. Feedback highlighted its guidance and ease of use. Additionally, all participants believed smart evacuation could enhance mining safety, citing benefits such as energy conservation and quicker access to safety. While some suggested more signage, most still deemed smart evacuation superior. 4. Conclusions This research validates the first hypothesis that smart evacuation strategies outperform traditional ones, resulting in assured decision-making. Smart evacuation achieved a maximum time reduction of up to 40% compared to the traditional strategies. Moreover, smart evacuation was preferred by 83% of participants, all of whom recognized its potential to enhance safety during mine evacuations. To avoid a learning effect, a between-subjects design is suggested over a within-design, ensuring fair route assignments and reducing bias. Participants expressed confidence in the smart wayfinder, appreciating its simplicity and fewer turns. Despite efforts to mitigate the learning effect, minor impact persisted, yet participants generally performed well, particularly in the dark simulation environment. This improvement is attributed to participants' adaptation to the treadmill and wayfinder. For future research, it is suggested to investigate the superiority of smart evacuation strategies over traditional ones by pre-determining hazardous circumstances and directing evacuees along an optimal route from the start. Additionally, incorporating an algorithm considering individuals' fitness levels could help prioritize less fit miners for shorter distances to improve their chances of reaching safety promptly. Declarations Acknowledgements The authors express gratitude for the support received from the National Institute for Occupational Health and Safety (NIOSH) for financing this research endeavor through contract number 75D30119C06044. Furthermore, the authors would like to acknowledge Nevada Gold Mines for supplying the required materials. Funding The This study was funded by the National Institute for Occupational Health and Safety (NIOSH) through contract number 75D30119C06044. Conflicts of Interests The authors have no relevant financial or non-financial interests to disclose. References 30 CFR § 57.11051 - Escape Routes (1985). Ahn, J., & Han, R. (2011). RescueMe: An indoor mobile augmented-reality evacuation system by personalized pedometry. 2011 IEEE Asia-Pacific Services Computing Conference , 70–77. Alexander, D., Bealko, S., Brnich, M., Kowalski-Trakofler, K., & Peters, R. H. (2010). Strategies for Escape and Rescue from Underground Coal Mines. In Information Circular 9522 . Arias, S., La Mendola, S., Wahlqvist, J., Rios, O., Nilsson, D., & Ronchi, E. (2019). Virtual reality evacuation experiments on way-finding systems for the future circular collider. Fire Technology , 55 , 2319–2340. Arias, S., Wahlqvist, J., Nilsson, D., Ronchi, E., Saverio, L. M., & Rios, O. (2019). Virtual Reality Evacuation Experiments on Way-Finding Systems for the Future Circular Collider. Fire Technology , 55 . https://doi.org/10.1007/s10694-019-00868-y Barker-Read, G. R., & Li, H. (1989). Automatic selection of safe egress routes away from underground fires. Mining Science and Technology , 9 , 289–308. https://doi.org/10.1016/S0167-9031(89)90994-8 Bernardes, S. M. F., Rebelo, F., Vilar, E., Noriega, P., & Borges, T. (2015). Methodological approaches for use virtual reality to develop emergency evacuation simulations for training, in emergency situations. Procedia Manufacturing , 3 , 6313–6320. Brenkley, B., Bennett, D., & Jones, S. (1999). “Enhancing Mine Emergency Response.” 28th International Conference on Safety in Mines Research Institutes , 1–9. Brenkley, D., Bennett, S. C., & Jones, B. (1999). Enhancing mine emergency response. Proceedings of 28th International Conference on Safety in Mines Research Institutes, Sinaia, Romania . Carrier, P. (2018). Underground Communication: Safety and Much More. World Coal , 31–34. Chance, S. S., Gaunet, F., Beall, A. C., & Loomis, J. M. (1998). Locomotion Mode Affects the Updating of Objects Encountered During Travel: The Contribution of Vestibular and Proprioceptive Inputs to Path Integration. Presence: Teleoperators and Virtual Environments , 7 (2), 168–178. https://doi.org/10.1162/105474698565659 Chen, C.-Y. (2009). A fuzzy-based approach for smart building evacuation modeling. 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC) , 1200–1203. Chen, J., Wang, J., Wang, B., Liu, R., & Wang, Q. (2018). An experimental study of visibility effect on evacuation speed on stairs. Fire Safety Journal , 96 , 189–202. https://doi.org/10.1016/j.firesaf.2017.11.010 Cosma, G., Ronchi, E., & Nilsson, D. (2016a). Way-finding lighting systems for rail tunnel evacuation: A virtual reality experiment with Oculus Rift®. Journal of Transportation Safety & Security , 8 (sup1), 101–117. Cosma, G., Ronchi, E., & Nilsson, D. (2016b). Way-finding lighting systems for rail tunnel evacuation: A virtual reality experiment with Oculus Rift®. Journal of Transportation Safety & Security , 8 , 101–117. https://doi.org/10.1080/19439962.2015.1046621 Damaševičius, R., Bacanin, N., & Misra, S. (2023). From Sensors to Safety: Internet of Emergency Services (IoES) for Emergency Response and Disaster Management. Journal of Sensor and Actuator Networks , 12 (3), 41. https://doi.org/10.3390/jsan12030041 Foster, P., & Burton, A. (2004). Virtual reality in improving mining ergonomics. Journal of The South African Institute of Mining and Metallurgy , 104 (2), 129–133. Gaab, S. J. (2019). Evaluation of Smart Underground Mine Evacuation Efficiency through Virtual Reality . University of Nevada, Reno, Available from ProQuest Dissertations Publishing (UMI No. 27671183). Galea, E. R., Xie, H., Deere, S., Cooney, D., & Filippidis, L. (2017). An international survey and full‐scale evacuation trial demonstrating the effectiveness of the active dynamic signage system concept. Fire and Materials , 41 (5), 493–513. HTC Corporation. (2023). VIVE Products, https://www.vive.com/us/, Visited on September 17, 2023 . Inoue, Y., Sashima, A., Ikeda, T., & Kurumatani, K. (2008). Indoor emergency evacuation service on autonomous navigation system using mobile phone. 2008 Second International Symposium on Universal Communication , 79–85. Isleyen, E., & Duzgun, H. S. (2019). Use of virtual reality in underground roof fall hazard assessment and risk mitigation. International Journal of Mining Science and Technology , 29 (4), 603–607. https://doi.org/10.1016/j.ijmst.2019.06.003 Jalali, S. E., & Noroozi, M. (2009). Determination of the optimal escape routes of underground mine networks in emergency cases. Safety Science , 47 , 1077–1082. https://doi.org/10.1016/j.ssci.2009.01.001 Jamasmie, C. (2015). This is what the world’s largest underground mine looks like. MINING.COM . Jansen-Osmann, P., & Fuchs, P. (2006). Wayfinding Behavior and Spatial Knowledge of Adults and Children in a Virtual Environment. Experimental Psychology , 53 (3), 171–181. https://doi.org/10.1027/1618-3169.53.3.171 Jeon, G.-Y., Kim, J.-Y., Hong, W.-H., & Augenbroe, G. (2011). Evacuation performance of individuals in different visibility conditions. Building and Environment , 46 (5), 1094–1103. https://doi.org/10.1016/j.buildenv.2010.11.010 Jha, A., Verburg, A., & Tukkaraja, P. (2022). Internet of Things–Based Command Center to Improve Emergency Response in Underground Mines. Safety and Health at Work , 13 (1), 40–50. https://doi.org/10.1016/j.shaw.2021.10.003 Kamran-Pishhesari, A., Moniri-Morad, A., & Sattarvand, J. (2024). Applications of 3D Reconstruction in Virtual Reality-Based Teleoperation: A Review in the Mining Industry. Technologies , 12 (3), 40. Kinateder, M., Ronchi, E., Gromer, D., Müller, M., Jost, M., Nehfischer, M., Mühlberger, A., & Pauli, P. (2014). Social influence on route choice in a virtual reality tunnel fire. Transportation Research Part F: Traffic Psychology and Behaviour , 26 , 116–125. https://doi.org/10.1016/j.trf.2014.06.003 Kinateder, M., & Warren, W. H. (2016). Social influence on evacuation behavior in real and virtual environments. Frontiers Robotics AI , 3 (September). https://doi.org/10.3389/frobt.2016.00043 Kobes, M., Helsloot, I., De Vries, B., Post, J. G., Oberijé, N., & Groenewegen, K. (2010). Way finding during fire evacuation; an analysis of unannounced fire drills in a hotel at night. Building and Environment , 45 (3), 537–548. Lujak, M., Billhardt, H., Dunkel, J., Fernández, A., Hermoso, R., & Ossowski, S. (2017). A distributed architecture for real-time evacuation guidance in large smart buildings. Computer Science and Information Systems , 14 (1), 257–282. Majumder, S., O’Neil, S., & Kennedy, R. (2017). Smart apparatus for fire evacuation—An IoT based fire emergency monitoring and evacuation system. 2017 IEEE MIT Undergraduate Research Technology Conference (URTC) , 1–4. Mallot, H. A., Gillner, S., van Veen, H. A. H. C., & Bülthoff, H. H. (1998). Behavioral experiments in spatial cognition using virtual reality. In Springer (pp. 447–467). https://doi.org/10.1007/3-540-69342-4_21 Marsh, E., Dahl, J., Kamran Pishhesari, A., Sattarvand, J., & Harris Jr, F. C. (2023). A Virtual Reality Mining Training Simulator for Proximity Detection. International Conference on Information Technology-New Generations , 387–393. Martell, M. J., Sammarco, J. J., Macdonald, B., & Rubinstein, E. (2020). Detectability of a self-illuminating lifeline for self-escape in smoke conditions of an underground mine. Lighting Research & Technology , 52 (1), 64–78. National Institute for Occupational Safety and Health - NIOSH. (2022). MSHA Data File Downloads; https://www.cdc.gov/niosh/mining/data/default.html; Visited on October 23, 2023 . Nel, S., Kizil, M. S., & Knights, P. (2011). Improving truck-shovel matching. 35th APCOM Symposium-Application of Computers and Operations Research in the Minerals Industry, Proceedings , 381–391. Orr, T. J., Mallet, L. G., & Margolis, K. A. (2009). Enhanced fire escape training for mine workers using virtual reality simulation. Mining Engineering , 61 (11), 41. Rehman, A. U., Awuah-Offei, K., Baker, D., & Bristow, D. (2019). Emergency Evacuation Guidance System for Underground Mines. SME Annual Meeting 2019 , Preprint 19-100 . Ronchi, E., Kinateder, M., Müller, M., Jost, M., Nehfischer, M., Pauli, P., & Mühlberger, A. (2015a). Evacuation travel paths in virtual reality experiments for tunnel safety analysis. Fire Safety Journal , 71 , 257–267. Ronchi, E., Kinateder, M., Müller, M., Jost, M., Nehfischer, M., Pauli, P., & Mühlberger, A. (2015b). Evacuation travel paths in virtual reality experiments for tunnel safety analysis. Fire Safety Journal , 71 , 257–267. https://doi.org/10.1016/j.firesaf.2014.11.005 Ronchi, E., Nilsson, D., Kojić, S., Eriksson, J., Lovreglio, R., Modig, H., & Walter, A. L. (2016). A virtual reality experiment on flashing lights at emergency exit portals for road tunnel evacuation. Fire Technology , 52 , 623–647. Sharma, G., Kaushal, Y., Chandra, S., Singh, V., Mittal, A. P., & Dutt, V. (2017). Influence of Landmarks on Wayfinding and Brain Connectivity in Immersive Virtual Reality Environment. Frontiers in Psychology , 8 . https://doi.org/10.3389/fpsyg.2017.01220 Stothard, P., Mitra, R., & Kovalev, A. (2008). Assessing levels of immersive tendency and presence experienced by mine workers in interactive training simulators developed for the coal mining industry. Proc. SimTec 2008 Simulation Conf , 1–6. Strickland, J. (2008). Could a wireless radio network save a miner’s life?, https://electronics.howstuffworks.com/miner-wireless-radio-network.htm; Visited: January 10, 2024. Tichon, J., & Burgess-Limerick, R. (2011). A review of virtual reality as a medium for safety related training in mining. Journal of Health & Safety Research & Practice , 3 (1), 33–40. Valencia, J., Emami, E., Battulwar, R., Jha, A., Gomez, J. A., Moniri-Morad, A., & Sattarvand, J. (2024). Blasthole Location Detection Using Support Vector Machine and Convolutional Neural Networks on UAV Images and Photogrammetry Models. Electronics , 13 (7), 1291. Varjo Co. (2023). Field of view in VR/XR; https://varjo.com/learning-hub/field-of-view/; Visited on October 23, 2023 . Virtuix. (2023). Accessories - Omni Standee. Virtuix TM , https://varjo.com/learning-hub/field-of-view/, Visited on October 23, 2023 . Witmer, B. G., & Singer, M. J. (1998). Measuring Presence in Virtual Environments: A Presence Questionnaire. Presence: Teleoperators and Virtual Environments , 7 (3), 225–240. https://doi.org/10.1162/105474698565686 Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revisions 29 Jun, 2025 Reviewers agreed at journal 23 Oct, 2024 Reviewers invited by journal 22 Oct, 2024 Editor assigned by journal 27 Apr, 2024 First submitted to journal 25 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4320209","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":369230800,"identity":"66228888-4e1c-4ef2-a817-21d0ea74b3b3","order_by":0,"name":"Simone Gaab","email":"","orcid":"","institution":"University of Nevada Reno","correspondingAuthor":false,"prefix":"","firstName":"Simone","middleName":"","lastName":"Gaab","suffix":""},{"id":369230801,"identity":"dffa34e7-0d71-4ebe-b2e4-a08925ddf49b","order_by":1,"name":"Amin Moniri-Morad","email":"","orcid":"","institution":"University of Nevada Reno","correspondingAuthor":false,"prefix":"","firstName":"Amin","middleName":"","lastName":"Moniri-Morad","suffix":""},{"id":369230802,"identity":"a933c99f-ee98-44ee-901c-320e105a9051","order_by":2,"name":"Frederick C. Harris","email":"","orcid":"","institution":"University of Nevada Reno","correspondingAuthor":false,"prefix":"","firstName":"Frederick","middleName":"C.","lastName":"Harris","suffix":""},{"id":369230803,"identity":"2b869fa3-024f-4465-8b49-e8c050e1dcc7","order_by":3,"name":"Javad Sattarvand","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBACxgYE2/ABhE4gqIWx4QCEbWxAlBawLqgWMwmitDD3H37++EPNvcT+2c3bqnl33GPgZ88xwG/FjDTDhgPHihNn3DlWdpv3TDGDZM8bQloYgFrYEowZbuSY3eZtS2AwuEHIlv7jHxsO/EswlgdqKQZpsSeopSHHsOFgW4Ic0HAzZrAtEgT9klM442xfgpzhjbRiybltCTwSZ54V4NVi2H98w4eKbwk8cjeSN354C7SOvz15A34tDWgCPHiVg4A8QRWjYBSMglEwCgDt7EpjVDeXOQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-9364-8775","institution":"University of Nevada Reno","correspondingAuthor":true,"prefix":"","firstName":"Javad","middleName":"","lastName":"Sattarvand","suffix":""}],"badges":[],"createdAt":"2024-04-24 20:04:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4320209/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4320209/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67504837,"identity":"4cb8506d-88ab-4e3d-bb32-982649bd7c66","added_by":"auto","created_at":"2024-10-25 18:15:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":165812,"visible":true,"origin":"","legend":"\u003cp\u003eVirtual reality configuration incorporating the HTC VIVE (HTC Corporation, 2023) and the Virtuix Omni treadmill (Virtuix, 2023) within our laboratory\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4320209/v1/633a0088562677b062597e5e.png"},{"id":67504838,"identity":"8ce648ae-fe35-4ff3-bccb-51cbf0c45cbf","added_by":"auto","created_at":"2024-10-25 18:15:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":98580,"visible":true,"origin":"","legend":"\u003cp\u003eVirtual reality mine drift network: Right - Tilted perspective, Left - Side view\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4320209/v1/2962a181bbca8adc54bb0865.png"},{"id":67504844,"identity":"33d98d80-4cc8-4544-816c-f5f9083c6728","added_by":"auto","created_at":"2024-10-25 18:15:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":301567,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the novel smart strategy (Right) (smart wayfinder displayed via smartphones, pointing to the nearest refugee chamber) and the traditional simulation strategy (Left) (egress route sign on drift wall), along with details of experimental design and evacuation scenarios\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4320209/v1/76c9ca159ff09fac75d39d19.png"},{"id":67504839,"identity":"1b5509ed-c384-4a65-a7b1-bca110025ee3","added_by":"auto","created_at":"2024-10-25 18:15:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":217206,"visible":true,"origin":"","legend":"\u003cp\u003eLeft - Illuminated condition, Right - Dark condition with restricted Field-of-View\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4320209/v1/fe45ef21198a0bec4d3b05b0.png"},{"id":67505446,"identity":"67d2e0e1-eb3e-4a38-8985-b0dc7a628402","added_by":"auto","created_at":"2024-10-25 18:23:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":90826,"visible":true,"origin":"","legend":"\u003cp\u003eThe experimental procedure corresponding to various simulations\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4320209/v1/b629dcf2a993b3ff85a53018.png"},{"id":67504845,"identity":"9853ac5e-1d75-4319-b35e-6b93eb2dba02","added_by":"auto","created_at":"2024-10-25 18:15:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":230894,"visible":true,"origin":"","legend":"\u003cp\u003eA view of the user interface at the beginning of the game\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4320209/v1/fd6af9e192c7a40eec5c8be9.png"},{"id":67504843,"identity":"016c889f-5b84-4e69-8130-c91322f15152","added_by":"auto","created_at":"2024-10-25 18:15:52","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":89634,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Mean duration required for completion in each scenario alongside the error range. (b) The variability (σ), the average (x̄), maximum and minimum values, and data points of the total duration gathered for each scenario\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4320209/v1/04fdcb29a3f6b536c0aee124.png"},{"id":67505445,"identity":"8fe92af0-3411-4d9f-beef-33f30d2b281f","added_by":"auto","created_at":"2024-10-25 18:23:52","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":127231,"visible":true,"origin":"","legend":"\u003cp\u003eThe turning angle within the Omni for scenarios SI and CI, depicting each participant's turning sequence by their number alongside dotted, vertical lines\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4320209/v1/63cc33e375cf50022246749e.png"},{"id":67505610,"identity":"f2026e0e-d872-454c-a800-c8a7e3b9d707","added_by":"auto","created_at":"2024-10-25 18:31:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2011112,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4320209/v1/5e17700b-62b8-4ccb-a2d0-d04135276a81.pdf"}],"financialInterests":"","formattedTitle":"Comparative Analysis of Smart and Traditional Evacuation Strategies in Underground Mines Utilizing Virtual Reality Simulations","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEssential mineral reserves like copper, platinum, and gold are commonly excavated from the earth to sustain modern human society. Among the various mining methods, underground mining stands out as one of the common methods for obtaining these resources, characterized by excavated drift networks within the ground. Chile's El Teniente, one of the largest underground mine globally, boasts an extensive drift network spanning 1900 miles (Jamasmie, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Excavating rock underground poses inherent risks that may prompt mine evacuations when incidents arise. In such emergencies, every minute is crucial for miners' health and survival. Typically, the primary evacuation destination is the mine's main entryway, like a shaft or ramp. Refuge chambers, if available, provide temporary safe locations until the arrival of the rescue team, who then facilitate the transportation of individuals to the surface. According to data from the MSHA database, there were 127 recorded fires in underground metal/nonmetal mines and 122 recorded fires in the United States' underground coal mines between 2000 and 2021 (National Institute for Occupational Safety and Health - NIOSH, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite the implementation of safety measures mandated by mine safety laws, such as the 2006 MINER Act, a study examining escape plans for underground coal mines noted a deficiency in literature concerning contemporary mining procedures and the impacts of the MINER Act (Alexander et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Additionally, the research highlights the absence of US-based studies or technology dissemination within the broader US mine emergency response framework, contrasting with other industries and mining methodologies abroad.\u003c/p\u003e \u003cp\u003eNumerous endeavors have investigated intelligent evacuation guidance systems across various settings, including highway tunnels, occupational buildings, and other domains, as demonstrated by the works of Inoue et al. (Inoue et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), Chen (C.-Y. Chen, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), Kobes et al. (Kobes et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), Ahn and Han (Ahn \u0026amp; Han, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), Ronchi et al. (Ronchi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e), Bernardes et al. (Bernardes et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Cosma et al. (Cosma et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e), Ronchi et al. (Ronchi et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Galea et al. (Galea et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Majumder et al. (Majumder et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Lujak et al. (Lujak et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Arias et al. (Arias, La Mendola, et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and Damaševičius et al. (Damaševičius et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite notable advancements in these sectors, the integration of smart evacuation systems within the mining industry remains largely unexplored. Present regulations in mining primarily rely on conventional route marking for evacuation procedures in underground non-coal mines, as mandated by law (30 CFR \u0026sect;\u0026nbsp;57.11051 - Escape Routes, 1985). Although a few studies, such as those by Jalali and Noroozi (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), Barker-Read and Li (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), Rehman et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and Jha et al. (Jha et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), have initiated exploration into smart evacuation strategies in underground mining settings, comprehensive implementation has yet to materialize.\u003c/p\u003e \u003cp\u003eAn effective smart evacuation strategy for underground mining operations necessitates continuous monitoring of individuals' whereabouts within the mine using advanced technologies such as the Internet of Things (IoT). This involves the deployment of devices such as beacons, Wi-Fi signal strength indicators, and Radio Frequency Identification (RFID) tags. Additionally, environmental surveillance is crucial for detecting hazards and preventing risks, employing tools such as smoke detectors, thermometers, and a range of sensors. Wireless communication systems, particularly wireless mesh networks, are resilient in harsh mining conditions, ensuring functionality even in the event of individual node failures (Carrier, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Strickland, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Furthermore, various strategies, such as smart exit signs with adaptable direction indicators or personal devices like smartwatches or smartphones can aid in guiding individuals through the mine. These devices allow for customization of localization and navigation for each occupant, enhancing overall safety and efficiency.\u003c/p\u003e \u003cp\u003eSeveral studies have scrutinized the conventional static evacuation strategy within the mining sector. Brenkley et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) pointed out challenges associated with maintaining reflective signs and symbols, their reduced visibility in adverse conditions, and the limited attention from miners. Martell et al. (Martell et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) similarly observed that exit route signs may become significantly less conspicuous in situations of poor visibility. Furthermore, Chen et al. (J. Chen et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Jeon et al. (Jeon et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) illustrated those conditions of low visibility, such as those induced by smoke, can prolong evacuation durations. It is imperative to give due consideration to the design and placement of exit signs or wayfinding systems, as these aspects can impact evacuation efficiency.\u003c/p\u003e \u003cp\u003eVirtual Reality (VR) has been widely utilized across diverse domains for its capacity to generate computer-simulated three-dimensional environments using techniques like photogrammetry and point cloud generation (Gaab, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kamran-Pishhesari et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Valencia et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). R has demonstrated its effectiveness in assessing emergency evacuation behavior (Kinateder et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kinateder \u0026amp; Warren, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ronchi et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e) and enhancing wayfinding systems in highway tunnels (Arias, Wahlqvist, et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cosma et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016b\u003c/span\u003e; Ronchi et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e). Among recent VR technologies, CAVE and 360\u0026deg; screens incorporate a Head-Mounted Display (HMD) equipped with separate displays for each eye, providing users with an expansive 200\u0026deg; field of vision (Varjo Co., 2023). In the mining sector, numerous research endeavors have investigated VR applications, including safety and hazard training (Marsh et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nel et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Orr et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Stothard et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Tichon \u0026amp; Burgess-Limerick, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), hazard evaluation and risk reduction (Isleyen \u0026amp; Duzgun, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and the remote operation of equipment using VR (Foster \u0026amp; Burton, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Hence, VR technologies present an innovative solution for implementing smart evacuation systems in underground mining operations.\u003c/p\u003e \u003cp\u003eRecent research has shown that the adoption of real-time, personalized evacuation guidance strategies can significantly reduce evacuation times, thus improving safety in mining operations. These systems, by offering customized guidance, help prevent disorientation and mitigate biases in decision-making. This study seeks to deepen our understanding by conducting a thorough examination of the effectiveness of smart underground mine evacuation approach, comparing it with traditional strategies. The smart evacuation strategy proposed in this research integrates advanced features like beacon technology, evacuation optimization algorithms, and smartwatch navigation to provide optimal evacuation routes for underground mine workers. Utilizing virtual reality (VR) simulations, we replicated underground mine evacuation scenarios based on the drift network layout of an actual gold mine in Nevada, USA. Additionally, we created a simulation game that mimics real underground mine environments and emergency situations, enabling us to evaluate evacuation performance. Furthermore, the study explores the influence of low-visibility conditions and lighting on evacuation efficiency. Through these comprehensive analyses, this research offers valuable insights into the potential of smart evacuation strategies to enhance safety and productivity in underground mining operations.\u003c/p\u003e \u003cp\u003eThe structure of this paper is as follows. Section 2 provides the proposed theoretical and experimental approaches for this study. Section 3 delves into the obtained results from the analyses, providing an accurate insight into the problem. Finally, Section 4 presents the conclusions and notable remarks.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eExisting evacuation guidance strategies used in underground mining are not cutting-edge and may encounter significant challenges in situations of poor visibility or varying environmental conditions. These systems are anticipated to reduce evacuation durations, offer enhanced visibility even in extreme conditions, provide clear and unambiguous cues through visual, audible, and tactile means, require cost-effective maintenance, easily adapt to frequent layout changes in the mine, ensure reliability, and function independently of ambient lighting (D. Brenkley et al., 1999). This research aims to explore whether a new approach to evacuation guidance could expedite underground mine evacuations, mitigate the risk of entrapment, and ultimately, safeguard the lives of miners.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.1.\u0026nbsp;Experimental Setup\u003c/h2\u003e\n\u003cp\u003eThe simulations took place within a virtual reality (VR) representation of an underground mine. Opting for a virtual environment for the simulations served to expedite the process, conserve resources, and eliminate the need to expose test participants to the inherent dangers of actual underground mining environments. To replicate real-world conditions, a virtual counterpart resembling an operational gold mine in Nevada was constructed for the simulations. Study volunteers engaged in two evacuation scenarios: one utilizing traditional exit signage and the other employing a novel smart evacuation wayfinder strategy. Throughout the evacuation simulations, participants were rerouted to alternative exits if they encountered obstacles, with metrics recorded including total evacuation time and response time to obstacles or directional changes. Additionally, both pre- and post-simulation surveys were administered to gather data on participants\u0026apos; familiarity with VR technology and underground mining, personal preferences, and their VR experience during the simulation. Subsequently, statistical analysis was employed to assess the efficiency of the smart evacuation method compared to the traditional strategy, with a focus on quantifying potential time savings. The projected advantages of the smart evacuation approach over traditional methods were evaluated based on two hypotheses: (A) Smart evacuation facilitates quicker evacuation and (B) Smart evacuation enhances decision-making confidence.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.2.\u0026nbsp;Virtual Environment (VE)\u003c/h2\u003e\n\u003cp\u003eTo develop these simulations, categorized as \u0026quot;serious\u0026quot; games, we utilized the Unity 2018.3.2f1 game engine. Unity serves as a software development environment for crafting games across various platforms, including Windows, Android, iOS, and PlayStation. Additionally, constructing intricate games within Unity typically involves scripting in C#. In this instance, the layout of the mine was obtained from mine planning software, Vulcan, and brought into Unity as central line strings representing the mine drift. Then, an HTC VIVE Head-mounted display and a Virtuix Omni treadmill for locomotion were employed to facilitate interaction within the VE (Figure 1). As part of the setup process, users don the Omni overshoes equipped with tracking sensors before stepping onto the Omni treadmill to commence walking. These specialized overshoes ensure precise tracking of users\u0026apos; movements within the virtual environment. The arrangment of the Omni base, coupled with low-friction shoes, allows users\u0026apos; feet to glide back to the center after each stride, enabling natural movement. This method contrasts with traditional VR locomotion techniques like teleportation or flying. Given that actual movement in the desired direction aids navigation in a three-dimensional world, prompting the selection of the Virtuix Omni treadmill. Indeed, the treadmill furnishes participants with essential idiothetic (self-motion) cues (Chance et al., 1998; Jansen-Osmann \u0026amp; Fuchs, 2006; Mallot et al., 1998; Sharma et al., 2017).\u003c/p\u003e\n\u003cp\u003eThe VE is modeled after a genuine underground gold mine located in Northern Nevada. As depicted in Figure 2, the top four levels of the mine (Levels 900, 1250, 1550, and 1715) were recreated. Due to constraints related to computational resources, labor, and overall effort, it was not feasible to produce a true-to-scale and highly detailed depiction of the mine. Consequently, the reconstruction maintains a conceptual nature.\u003c/p\u003e\n\u003cp\u003eIn this simulation, the primary focus was on replicating key aspects of the mine layout, notably the positioning of evacuation points such as shafts and refuge chambers, along with the placement of egress route signs and maps. These elements were strategically arranged with input from a former mine employee who provided insights based on the actual layout of the mine serving as the model for the VE. This meticulous process ensured that the simulated conditions closely mirrored those of the real mine. As a result, any potential bias from the experimenter was mitigated, particularly concerning the critical placement of exit route signs, which could significantly impact the outcomes of the simulation. The simulation sought to offer an impartial assessment of the two distinct evacuation wayfinding systems by faithfully reproducing these conditions, as illustrated in Figure 3.\u003c/p\u003e\n\u003ch2\u003e2.3.\u0026nbsp;Experimental Design and Evacuation Scenarios\u003c/h2\u003e\n\u003cp\u003eBefore commencing the study, approval was obtained from the Institutional Review Board (IRB) of the University of Nevada, Reno to conduct experiments involving human subjects for this specific research endeavor. The experiments were conducted using a within-subject design to evaluate the effectiveness of each evacuation strategy. Additionally, an initial phase was established to minimize any learning curve when shifting from the traditional to the smart strategy. Throughout this phase, participants familiarized themselves with the haptic feedback, movement controls, and Head-Mounted Display (HMD) utilized in the simulation and VR environment until they felt \u0026quot;comfortable\u0026quot; with their usage. This step aimed to reduce differences in the gaming experience, allowing less experienced participants to learn. The experimental setup operated under the assumption that the majority of participants lacked prior experience with VR. Following the completion of the initial phase, participants commenced the mine evacuation simulations. In every simulation, participants were directed to the initial evacuation destination using either conventional signs or an intelligent wayfinder. However, upon reaching the first evacuation point, participants were redirected to the second point, as the first one became inaccessible during the simulation process. In this case, a timer was utilized to record the variable \u0026quot;total time\u0026quot; at the beginning of each simulation, aiming to assess the duration from the initial point to reaching the endpoint.\u003c/p\u003e\n\u003cp\u003eFigure 5 illustrates the sequence of simulations, with illuminated simulations denoted by \u0026quot;I\u0026quot; occurring at the same mine level. Both sets of simulations were conducted under dark conditions (\u0026quot;D\u0026quot;). Contrary to illuminated conditions, the Field-of-View (FOV) was notably reduced in the dark, affecting visibility down the drift/path compared to the illuminated simulations (see Figure 4). To maintain consistency, identical mine levels were chosen for both traditional (\u0026quot;C\u0026quot;) and smart strategies (\u0026quot;S\u0026quot;), ensuring comparable complexity in terms of possible directions and intersections along the shortest route. To mitigate potential memory bias, distinct evacuation routes were utilized for each strategy, while ensuring consistency in metrics such as number of curves, distance to the final target, occurrence time, and obstacles. The study concluded with a post-survey utilizing the Presence Questionnaire by Witmer and Singer (Witmer \u0026amp; Singer, 1998) to evaluate participants\u0026apos; sense of presence and gather insights into their preferences regarding evacuation methods, including perceptions of smart evacuation\u0026apos;s efficacy compared to traditional approaches.\u003c/p\u003e\n\u003ch2\u003e2.4.\u0026nbsp;Result Documentation\u003c/h2\u003e\n\u003cp\u003eThe simulation commences when the examiner initiates recording by pressing the \u0026quot;Start Recording\u0026quot; button, capturing the user\u0026apos;s in-game view. The examiner retains the ability to conclude the simulation by turning off the play button whenever necessary. Once the game concludes, the recording is saved on the local drive. A script was created to capture a top-down screenshot of the mine level every five seconds, showing the player\u0026apos;s current location and path. Consequently, the recorded video and screenshots enable analysis of areas where the player encountered difficulty in navigation. Notably, the path trail is visible solely in these screenshots and remains hidden from the user within the game interface.\u003c/p\u003e\n\u003cp\u003eIn this case, four text documents are generated, each indicating the date and start time. Upon activation of the trigger, a user interface resembling the one depicted in Figure 6 will appear for five seconds, delivering the message: \u0026quot;The mine shaft is full! Seek refuge in a chamber!\u0026quot; or \u0026quot;The refuge chamber is occupied! Head towards the shaft!\u0026quot; contingent upon the simulation conditions. Information regarding the step count and hip-ring angle of the Omni is captured at two-second intervals, allowing for the depiction of variations over time through graphical representation. This data aids in assessing how frequently the user adjusted direction or explored alternate routes in different simulation scenarios.\u003c/p\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003eThe evacuation simulation process involved selecting different participants, all of whom were familiar with underground mining operations and trained for the evacuation process. Therefore, the analysis was performed based on the results of simulating the evacuation process in an underground mine emergency.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003e3.1 Quantitative Data Analysis\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e(a) indicates a comparison of the average evacuation durations for each simulation scenario: SD (Mean\u0026thinsp;=\u0026thinsp;183.56, Std\u0026thinsp;=\u0026thinsp;49.76), SI (Mean\u0026thinsp;=\u0026thinsp;198.23, Std\u0026thinsp;=\u0026thinsp;47.41), CD (Mean\u0026thinsp;=\u0026thinsp;220.89, Std\u0026thinsp;=\u0026thinsp;52.36), and CI (Mean\u0026thinsp;=\u0026thinsp;328.46, Std\u0026thinsp;=\u0026thinsp;103.58). As depicted, the mean evacuation duration with smart evacuation strategies proved shorter compared to the duration required with the traditional strategies. This difference arises from providing optimal directions to people via smartphones, eliminating the need for them to make decisions themselves to find the shortest route to the refugee chamber and instead focusing their attention solely on the smartphone\u0026apos;s guidelines. They do not need to inspect their environment or markings on the underground mine walls; their sole task is to follow the directions on the smartphones and reach the refugee chambers. Furthermore, it is notable that, on average, participants completed their mission more quickly under the SD and CD conditions compared to the SI and CI conditions. Therefore, despite the limited FOV in CD, participants exceeded the mean duration achieved in CI by 32.75%. This difference can be attributed to the fact that the illuminated emergency situation provides a wider FOV, leading to distraction as individual focus on unnecessary markings and situations. Conversely, dark conditions provide a restricted FOV, guiding individuals\u0026apos; focus towards evacuation directions.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e (b) depicts the variation in collected data for all scenarios. Notably, the standard deviation for CI stands at 103.58, markedly surpassing that of SD (49.76), CD (52.36), and SI (47.41). In the CI dataset, total evacuation times exhibited some variability around the mean, varying from 178 to 521 seconds. Although 521 initially seems as an outlier in the CI dataset, examination within the acceptable data range (121.30 to 535.62), defined as two standard deviations from the mean, verifies that all data points fall within the range. Evacuation times in CI show less consistency compared to SD, CD, and SI scenarios. The disparity in standard deviations among SD, SI, and CD is not notably high. To ascertain notable disparities among the scenarios, a matched-pair t-test is performed, contrasting each approach with the others, as delineated in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe examination of statistical significance regarding the variation in total times across the scenarios\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eTime Difference Comparison (x̄\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;x̄\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage Time Reduction Percentage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistical Significance of Time Reduction (p-value)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignificant (0.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignificant (0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignificant (0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignificant (0.0006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFor each p-value below the predetermined significance threshold of 5%, it demonstrates a notable distinction among the datasets of the compared scenarios. Across all scenarios, there existed a marked discrepancy in total time. Also, the examination results revealed that smart evacuation strategies consistently outperformed the traditional strategy in terms of speed. Additionally, experiments showed that evacuations conducted in dark situations were notably swifter than those in illuminated surroundings. The most substantial decrease in average time, at 39.65%, was observed between CI and SI. Additionally, there was a time reduction of 32.75% between CI and CD. Hence, evacuations proceeded more quickly in dark environments compared to illuminated ones. A p-value of 0.022 for CD versus SD highlights a notable distinction, signifying that SD scenarios notably advanced at a faster pace, resulting in a 16.90% reduction in average total time. When contrasting SI with SD, the p-value of 0.038 is close to reaching statistical significance, with a corresponding time reduction of 7.4%.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003e3.2. Analysis of Qualitative Data\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e illustrates the frequency of participants\u0026apos; turning actions using the Omni in both SI and CI scenarios, reflecting alterations in walking direction or shifts in observation.\u003c/p\u003e\n\u003cp\u003eAnalyzing the number and distribution of vertical lines in both plots (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e) indicates a greater occurrence of turning among participants using the traditional evacuation strategy. This implies that certain participants may have deviated from the optimal route, necessitating redirection or confirmation of their path choice. In contrast, participants utilizing the smart evacuation strategy predominantly adhered to the arrow guidance. The consistent recurrence of turning patterns in SI indicates a reduced level of uncertainty regarding the correct route, with most participants making similar turns at comparable points. It is important to note that both conditions featured an equal number of 90\u0026deg; and 180\u0026deg; turns. Examination of the corresponding plots for SD and CD does not indicate a significant difference in the frequency or order of turning movements between the traditional and smart strategies.\u003c/p\u003e\n\u003cp\u003eA post-survey also provided qualitative insights into participants\u0026apos; sense of immersion in the virtual environment and their individual preferences. Additionally, the study examined the potential impact of the equipment on participants\u0026apos; performance. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e displays the average ratings for the Presence Questionnaire on a Likert scale ranging from 1 to 7. The achieved results revealed that most participants experienced a high level of immersion in the virtual environment, and the use of the treadmill for locomotion did not adversely affect their performance. While one participant found the experience intriguing and realistic, another reported motion sickness possibly due to perceived virtual world instability.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe results of the defined Questionnaire\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQuestions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean (x̄)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard Deviation (\u0026sigma;)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo what extent did the mechanism governing movement through the environment feel natural?\u003c/p\u003e\n \u003cp\u003e(Extremely artificial \u0026ndash; Very natural)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo what degree did the information from your different senses feel inconsistent or disconnected?\u003c/p\u003e\n \u003cp\u003e(Not at all \u0026ndash; Completely)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo what extent did your virtual experiences align with your real-world experiences?\u003c/p\u003e\n \u003cp\u003e(Not consistent \u0026ndash; Very consistent)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo what extent could you thoroughly explore or examine the environment visually?\u003c/p\u003e\n \u003cp\u003e(Not at all \u0026ndash; Completely)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHow disoriented did you feel at the beginning of breaks or the end of the experimental session?\u003c/p\u003e\n \u003cp\u003e(Not disoriented \u0026ndash; Very disoriented)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHow rapidly did you adapt to the virtual environment?\u003c/p\u003e\n \u003cp\u003e(Not at all \u0026ndash; Within 2 minutes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHow skilled did you feel in navigating and engaging with the virtual environment by the end of the session?\u003c/p\u003e\n \u003cp\u003e(Not skilled \u0026ndash; Very skilled)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo what extent did the visual display quality impede or distract you from completing assigned tasks and necessary activities?\u003c/p\u003e\n \u003cp\u003e(No interference - Task performance hindered)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo what degree did the control devices disrupt the execution of allocated activities or tasks?\u003c/p\u003e\n \u003cp\u003e(No disruption - Task performance prevented)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eOverall, 83% of the participants expressed a preference for smart evacuation strategies over the traditional strategy. Feedback highlighted its guidance and ease of use. Additionally, all participants believed smart evacuation could enhance mining safety, citing benefits such as energy conservation and quicker access to safety. While some suggested more signage, most still deemed smart evacuation superior.\u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThis research validates the first hypothesis that smart evacuation strategies outperform traditional ones, resulting in assured decision-making. Smart evacuation achieved a maximum time reduction of up to 40% compared to the traditional strategies. Moreover, smart evacuation was preferred by 83% of participants, all of whom recognized its potential to enhance safety during mine evacuations. To avoid a learning effect, a between-subjects design is suggested over a within-design, ensuring fair route assignments and reducing bias. Participants expressed confidence in the smart wayfinder, appreciating its simplicity and fewer turns. Despite efforts to mitigate the learning effect, minor impact persisted, yet participants generally performed well, particularly in the dark simulation environment. This improvement is attributed to participants' adaptation to the treadmill and wayfinder. For future research, it is suggested to investigate the superiority of smart evacuation strategies over traditional ones by pre-determining hazardous circumstances and directing evacuees along an optimal route from the start. Additionally, incorporating an algorithm considering individuals' fitness levels could help prioritize less fit miners for shorter distances to improve their chances of reaching safety promptly.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express gratitude for the support received from the National Institute for Occupational Health and Safety (NIOSH) for financing this research endeavor through contract number 75D30119C06044. Furthermore, the authors would like to acknowledge Nevada Gold Mines for supplying the required materials.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe This study was funded by the National Institute for Occupational Health and Safety (NIOSH) through contract number 75D30119C06044.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e30 CFR \u0026sect; 57.11051 - Escape Routes (1985).\u003c/li\u003e\n\u003cli\u003eAhn, J., \u0026amp; Han, R. (2011). RescueMe: An indoor mobile augmented-reality evacuation system by personalized pedometry. \u003cem\u003e2011 IEEE Asia-Pacific Services Computing Conference\u003c/em\u003e, 70\u0026ndash;77.\u003c/li\u003e\n\u003cli\u003eAlexander, D., Bealko, S., Brnich, M., Kowalski-Trakofler, K., \u0026amp; Peters, R. H. (2010). Strategies for Escape and Rescue from Underground Coal Mines. In \u003cem\u003eInformation Circular 9522\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eArias, S., La Mendola, S., Wahlqvist, J., Rios, O., Nilsson, D., \u0026amp; Ronchi, E. (2019). Virtual reality evacuation experiments on way-finding systems for the future circular collider. \u003cem\u003eFire Technology\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e, 2319\u0026ndash;2340.\u003c/li\u003e\n\u003cli\u003eArias, S., Wahlqvist, J., Nilsson, D., Ronchi, E., Saverio, L. M., \u0026amp; Rios, O. (2019). Virtual Reality Evacuation Experiments on Way-Finding Systems for the Future Circular Collider. \u003cem\u003eFire Technology\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e. https://doi.org/10.1007/s10694-019-00868-y\u003c/li\u003e\n\u003cli\u003eBarker-Read, G. R., \u0026amp; Li, H. (1989). Automatic selection of safe egress routes away from underground fires. \u003cem\u003eMining Science and Technology\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, 289\u0026ndash;308. https://doi.org/10.1016/S0167-9031(89)90994-8\u003c/li\u003e\n\u003cli\u003eBernardes, S. M. F., Rebelo, F., Vilar, E., Noriega, P., \u0026amp; Borges, T. (2015). Methodological approaches for use virtual reality to develop emergency evacuation simulations for training, in emergency situations. \u003cem\u003eProcedia Manufacturing\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e, 6313\u0026ndash;6320.\u003c/li\u003e\n\u003cli\u003eBrenkley, B., Bennett, D., \u0026amp; Jones, S. (1999). \u0026ldquo;Enhancing Mine Emergency Response.\u0026rdquo; \u003cem\u003e28th International Conference on Safety in Mines Research Institutes\u003c/em\u003e, 1\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eBrenkley, D., Bennett, S. C., \u0026amp; Jones, B. (1999). Enhancing mine emergency response. \u003cem\u003eProceedings of 28th International Conference on Safety in Mines Research Institutes, Sinaia, Romania\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eCarrier, P. (2018). Underground Communication: Safety and Much More. \u003cem\u003eWorld Coal\u003c/em\u003e, 31\u0026ndash;34.\u003c/li\u003e\n\u003cli\u003eChance, S. S., Gaunet, F., Beall, A. C., \u0026amp; Loomis, J. M. (1998). Locomotion Mode Affects the Updating of Objects Encountered During Travel: The Contribution of Vestibular and Proprioceptive Inputs to Path Integration. \u003cem\u003ePresence: Teleoperators and Virtual Environments\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(2), 168\u0026ndash;178. https://doi.org/10.1162/105474698565659\u003c/li\u003e\n\u003cli\u003eChen, C.-Y. (2009). A fuzzy-based approach for smart building evacuation modeling. \u003cem\u003e2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC)\u003c/em\u003e, 1200\u0026ndash;1203.\u003c/li\u003e\n\u003cli\u003eChen, J., Wang, J., Wang, B., Liu, R., \u0026amp; Wang, Q. (2018). An experimental study of visibility effect on evacuation speed on stairs. \u003cem\u003eFire Safety Journal\u003c/em\u003e, \u003cem\u003e96\u003c/em\u003e, 189\u0026ndash;202. https://doi.org/10.1016/j.firesaf.2017.11.010\u003c/li\u003e\n\u003cli\u003eCosma, G., Ronchi, E., \u0026amp; Nilsson, D. (2016a). Way-finding lighting systems for rail tunnel evacuation: A virtual reality experiment with Oculus Rift\u0026reg;. \u003cem\u003eJournal of Transportation Safety \u0026amp; Security\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(sup1), 101\u0026ndash;117.\u003c/li\u003e\n\u003cli\u003eCosma, G., Ronchi, E., \u0026amp; Nilsson, D. (2016b). Way-finding lighting systems for rail tunnel evacuation: A virtual reality experiment with Oculus Rift\u0026reg;. \u003cem\u003eJournal of Transportation Safety \u0026amp; Security\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 101\u0026ndash;117. https://doi.org/10.1080/19439962.2015.1046621\u003c/li\u003e\n\u003cli\u003eDama\u0026scaron;evičius, R., Bacanin, N., \u0026amp; Misra, S. (2023). From Sensors to Safety: Internet of Emergency Services (IoES) for Emergency Response and Disaster Management. \u003cem\u003eJournal of Sensor and Actuator Networks\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(3), 41. https://doi.org/10.3390/jsan12030041\u003c/li\u003e\n\u003cli\u003eFoster, P., \u0026amp; Burton, A. (2004). Virtual reality in improving mining ergonomics. \u003cem\u003eJournal of The South African Institute of Mining and Metallurgy\u003c/em\u003e, \u003cem\u003e104\u003c/em\u003e(2), 129\u0026ndash;133.\u003c/li\u003e\n\u003cli\u003eGaab, S. J. (2019). \u003cem\u003eEvaluation of Smart Underground Mine Evacuation Efficiency through Virtual Reality\u003c/em\u003e. University of Nevada, Reno, Available from ProQuest Dissertations Publishing (UMI No. 27671183).\u003c/li\u003e\n\u003cli\u003eGalea, E. R., Xie, H., Deere, S., Cooney, D., \u0026amp; Filippidis, L. (2017). An international survey and full‐scale evacuation trial demonstrating the effectiveness of the active dynamic signage system concept. \u003cem\u003eFire and Materials\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(5), 493\u0026ndash;513.\u003c/li\u003e\n\u003cli\u003eHTC Corporation. (2023). \u003cem\u003eVIVE Products, https://www.vive.com/us/, Visited on September 17, 2023\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eInoue, Y., Sashima, A., Ikeda, T., \u0026amp; Kurumatani, K. (2008). Indoor emergency evacuation service on autonomous navigation system using mobile phone. \u003cem\u003e2008 Second International Symposium on Universal Communication\u003c/em\u003e, 79\u0026ndash;85.\u003c/li\u003e\n\u003cli\u003eIsleyen, E., \u0026amp; Duzgun, H. S. (2019). Use of virtual reality in underground roof fall hazard assessment and risk mitigation. \u003cem\u003eInternational Journal of Mining Science and Technology\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(4), 603\u0026ndash;607. https://doi.org/10.1016/j.ijmst.2019.06.003\u003c/li\u003e\n\u003cli\u003eJalali, S. E., \u0026amp; Noroozi, M. (2009). Determination of the optimal escape routes of underground mine networks in emergency cases. \u003cem\u003eSafety Science\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e, 1077\u0026ndash;1082. https://doi.org/10.1016/j.ssci.2009.01.001\u003c/li\u003e\n\u003cli\u003eJamasmie, C. (2015). This is what the world\u0026rsquo;s largest underground mine looks like. \u003cem\u003eMINING.COM\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eJansen-Osmann, P., \u0026amp; Fuchs, P. (2006). Wayfinding Behavior and Spatial Knowledge of Adults and Children in a Virtual Environment. \u003cem\u003eExperimental Psychology\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(3), 171\u0026ndash;181. https://doi.org/10.1027/1618-3169.53.3.171\u003c/li\u003e\n\u003cli\u003eJeon, G.-Y., Kim, J.-Y., Hong, W.-H., \u0026amp; Augenbroe, G. (2011). Evacuation performance of individuals in different visibility conditions. \u003cem\u003eBuilding and Environment\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(5), 1094\u0026ndash;1103. https://doi.org/10.1016/j.buildenv.2010.11.010\u003c/li\u003e\n\u003cli\u003eJha, A., Verburg, A., \u0026amp; Tukkaraja, P. (2022). Internet of Things\u0026ndash;Based Command Center to Improve Emergency Response in Underground Mines. \u003cem\u003eSafety and Health at Work\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(1), 40\u0026ndash;50. https://doi.org/10.1016/j.shaw.2021.10.003\u003c/li\u003e\n\u003cli\u003eKamran-Pishhesari, A., Moniri-Morad, A., \u0026amp; Sattarvand, J. (2024). Applications of 3D Reconstruction in Virtual Reality-Based Teleoperation: A Review in the Mining Industry. \u003cem\u003eTechnologies\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(3), 40.\u003c/li\u003e\n\u003cli\u003eKinateder, M., Ronchi, E., Gromer, D., M\u0026uuml;ller, M., Jost, M., Nehfischer, M., M\u0026uuml;hlberger, A., \u0026amp; Pauli, P. (2014). Social influence on route choice in a virtual reality tunnel fire. \u003cem\u003eTransportation Research Part F: Traffic Psychology and Behaviour\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e, 116\u0026ndash;125. https://doi.org/10.1016/j.trf.2014.06.003\u003c/li\u003e\n\u003cli\u003eKinateder, M., \u0026amp; Warren, W. H. (2016). Social influence on evacuation behavior in real and virtual environments. \u003cem\u003eFrontiers Robotics AI\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(September). https://doi.org/10.3389/frobt.2016.00043\u003c/li\u003e\n\u003cli\u003eKobes, M., Helsloot, I., De Vries, B., Post, J. G., Oberij\u0026eacute;, N., \u0026amp; Groenewegen, K. (2010). Way finding during fire evacuation; an analysis of unannounced fire drills in a hotel at night. \u003cem\u003eBuilding and Environment\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(3), 537\u0026ndash;548.\u003c/li\u003e\n\u003cli\u003eLujak, M., Billhardt, H., Dunkel, J., Fern\u0026aacute;ndez, A., Hermoso, R., \u0026amp; Ossowski, S. (2017). A distributed architecture for real-time evacuation guidance in large smart buildings. \u003cem\u003eComputer Science and Information Systems\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 257\u0026ndash;282.\u003c/li\u003e\n\u003cli\u003eMajumder, S., O\u0026rsquo;Neil, S., \u0026amp; Kennedy, R. (2017). Smart apparatus for fire evacuation\u0026mdash;An IoT based fire emergency monitoring and evacuation system. \u003cem\u003e2017 IEEE MIT Undergraduate Research Technology Conference (URTC)\u003c/em\u003e, 1\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eMallot, H. A., Gillner, S., van Veen, H. A. H. C., \u0026amp; B\u0026uuml;lthoff, H. H. (1998). Behavioral experiments in spatial cognition using virtual reality. In \u003cem\u003eSpringer\u003c/em\u003e (pp. 447\u0026ndash;467). https://doi.org/10.1007/3-540-69342-4_21\u003c/li\u003e\n\u003cli\u003eMarsh, E., Dahl, J., Kamran Pishhesari, A., Sattarvand, J., \u0026amp; Harris Jr, F. C. (2023). A Virtual Reality Mining Training Simulator for Proximity Detection. \u003cem\u003eInternational Conference on Information Technology-New Generations\u003c/em\u003e, 387\u0026ndash;393.\u003c/li\u003e\n\u003cli\u003eMartell, M. J., Sammarco, J. J., Macdonald, B., \u0026amp; Rubinstein, E. (2020). Detectability of a self-illuminating lifeline for self-escape in smoke conditions of an underground mine. \u003cem\u003eLighting Research \u0026amp; Technology\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(1), 64\u0026ndash;78.\u003c/li\u003e\n\u003cli\u003eNational Institute for Occupational Safety and Health - NIOSH. (2022). \u003cem\u003eMSHA Data File Downloads; https://www.cdc.gov/niosh/mining/data/default.html; Visited on October 23, 2023\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eNel, S., Kizil, M. S., \u0026amp; Knights, P. (2011). Improving truck-shovel matching. \u003cem\u003e35th APCOM Symposium-Application of Computers and Operations Research in the Minerals Industry, Proceedings\u003c/em\u003e, 381\u0026ndash;391.\u003c/li\u003e\n\u003cli\u003eOrr, T. J., Mallet, L. G., \u0026amp; Margolis, K. A. (2009). Enhanced fire escape training for mine workers using virtual reality simulation. \u003cem\u003eMining Engineering\u003c/em\u003e, \u003cem\u003e61\u003c/em\u003e(11), 41.\u003c/li\u003e\n\u003cli\u003eRehman, A. U., Awuah-Offei, K., Baker, D., \u0026amp; Bristow, D. (2019). Emergency Evacuation Guidance System for Underground Mines. \u003cem\u003eSME Annual Meeting 2019\u003c/em\u003e, \u003cem\u003ePreprint 19-100\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eRonchi, E., Kinateder, M., M\u0026uuml;ller, M., Jost, M., Nehfischer, M., Pauli, P., \u0026amp; M\u0026uuml;hlberger, A. (2015a). Evacuation travel paths in virtual reality experiments for tunnel safety analysis. \u003cem\u003eFire Safety Journal\u003c/em\u003e, \u003cem\u003e71\u003c/em\u003e, 257\u0026ndash;267.\u003c/li\u003e\n\u003cli\u003eRonchi, E., Kinateder, M., M\u0026uuml;ller, M., Jost, M., Nehfischer, M., Pauli, P., \u0026amp; M\u0026uuml;hlberger, A. (2015b). Evacuation travel paths in virtual reality experiments for tunnel safety analysis. \u003cem\u003eFire Safety Journal\u003c/em\u003e, \u003cem\u003e71\u003c/em\u003e, 257\u0026ndash;267. https://doi.org/10.1016/j.firesaf.2014.11.005\u003c/li\u003e\n\u003cli\u003eRonchi, E., Nilsson, D., Kojić, S., Eriksson, J., Lovreglio, R., Modig, H., \u0026amp; Walter, A. L. (2016). A virtual reality experiment on flashing lights at emergency exit portals for road tunnel evacuation. \u003cem\u003eFire Technology\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e, 623\u0026ndash;647.\u003c/li\u003e\n\u003cli\u003eSharma, G., Kaushal, Y., Chandra, S., Singh, V., Mittal, A. P., \u0026amp; Dutt, V. (2017). Influence of Landmarks on Wayfinding and Brain Connectivity in Immersive Virtual Reality Environment. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e. https://doi.org/10.3389/fpsyg.2017.01220\u003c/li\u003e\n\u003cli\u003eStothard, P., Mitra, R., \u0026amp; Kovalev, A. (2008). Assessing levels of immersive tendency and presence experienced by mine workers in interactive training simulators developed for the coal mining industry. \u003cem\u003eProc. SimTec 2008 Simulation Conf\u003c/em\u003e, 1\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eStrickland, J. (2008). \u003cem\u003eCould a wireless radio network save a miner\u0026rsquo;s life?, https://electronics.howstuffworks.com/miner-wireless-radio-network.htm; Visited: January 10, 2024.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eTichon, J., \u0026amp; Burgess-Limerick, R. (2011). A review of virtual reality as a medium for safety related training in mining. \u003cem\u003eJournal of Health \u0026amp; Safety Research \u0026amp; Practice\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1), 33\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eValencia, J., Emami, E., Battulwar, R., Jha, A., Gomez, J. A., Moniri-Morad, A., \u0026amp; Sattarvand, J. (2024). Blasthole Location Detection Using Support Vector Machine and Convolutional Neural Networks on UAV Images and Photogrammetry Models. \u003cem\u003eElectronics\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(7), 1291.\u003c/li\u003e\n\u003cli\u003eVarjo Co. (2023). \u003cem\u003eField of view in VR/XR; https://varjo.com/learning-hub/field-of-view/; Visited on October 23, 2023\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eVirtuix. (2023). \u003cem\u003eAccessories - Omni Standee. Virtuix\u003csup\u003eTM\u003c/sup\u003e, https://varjo.com/learning-hub/field-of-view/, Visited on October 23, 2023\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eWitmer, B. G., \u0026amp; Singer, M. J. (1998). Measuring Presence in Virtual Environments: A Presence Questionnaire. \u003cem\u003ePresence: Teleoperators and Virtual Environments\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(3), 225\u0026ndash;240. https://doi.org/10.1162/105474698565686\u003c/li\u003e\n\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":"international-journal-of-system-assurance-engineering-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijsa","sideBox":"Learn more about [International Journal of System Assurance Engineering and Management](http://link.springer.com/journal/13198)","snPcode":"13198","submissionUrl":"https://www.editorialmanager.com/ijsa/default2.aspx","title":"International Journal of System Assurance Engineering and Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Fire Emergency, Wayfinding, Smart Evacuation Simulation, Virtual Reality (VR), Underground Mining ","lastPublishedDoi":"10.21203/rs.3.rs-4320209/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4320209/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderground mining operations inherently involve significant risks, such as the collapse of surrounding rock or fires. While infrequent, the potentially catastrophic nature of these events highlights the essential need for swift and secure evacuation procedures, ensuring the safety and survival of mineworkers in such situations. The traditional static evacuation strategy, relying on exit signs or guidance markers, may become blocked or barely discernible in low-visibility circumstances resulting from smoke or dust. This paper investigates the feasibility of integrating smart evacuation technology into underground mine operations by examining its effectiveness compared to traditional practices. This procedure involves real-time evacuation guidance of individuals along optimized paths to egress, effectively avoiding danger zones. A virtual simulation environment was built based on the actual layout of a gold mine in Nevada. In this instance, the simulation process involved evacuating a group of participants employing both traditional practices and smart evacuation strategies equipped with live assistance. The findings revealed a notable contrast in the effectiveness of the total evacuation duration between the traditional and smart evacuation strategies. The smart method achieved the most substantial decrease in total evacuation time, almost a 40% reduction. Additionally, 83% of participants expressed a preference for the smart evacuation strategies compared to the traditional practices, with all participants agreeing that the smart evacuation strategy has the potential to improve mine safety.\u003c/p\u003e","manuscriptTitle":"Comparative Analysis of Smart and Traditional Evacuation Strategies in Underground Mines Utilizing Virtual Reality Simulations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-25 18:15:47","doi":"10.21203/rs.3.rs-4320209/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2025-06-29T16:01:01+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-10-23T14:44:02+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-22T20:19:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-27T06:19:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of System Assurance Engineering and Management","date":"2024-04-25T12:54:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-system-assurance-engineering-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijsa","sideBox":"Learn more about [International Journal of System Assurance Engineering and Management](http://link.springer.com/journal/13198)","snPcode":"13198","submissionUrl":"https://www.editorialmanager.com/ijsa/default2.aspx","title":"International Journal of System Assurance Engineering and Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"38732387-69fc-4bf4-8e82-a642e6772d93","owner":[],"postedDate":"October 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-11T05:31:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-25 18:15:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4320209","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4320209","identity":"rs-4320209","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}