Experimental Detection of Embedded Rebar Corrosion in Concrete with Ground Penetrating Radar

Experimental Detection of Embedded Rebar Corrosion in Concrete with Ground Penetrating Radar | 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 Experimental Detection of Embedded Rebar Corrosion in Concrete with Ground Penetrating Radar Khadiza Binte Jalal, Nur Yazdani, Eyosias Beneberu, Mohd Mezanur Rahman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4366218/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Reinforced concrete structures have been extensively investigated using Ground Penetrating Radar (GPR) for qualitatively analyzing corrosion-induced degradation. However, no reliable quantitative model exists for the corrosion evaluation of the embedded steel rebars. Such corrosion may significantly impact the strength, serviceability and long-term durability of concrete structures. This study involved an experimental work to determine the relationship between the rebar corrosion quantity and the maximum amplitude as well as the two-way travel time (TWTT) of the GPR electromagnetic wave. A direct current was impressed into embedded steel rebars in concrete beams immersed in a 5% saltwater solution to induce accelerated corrosion. GRP data were collected before saline submersion and at 10-day intervals. A multivariate regression equation with high reliability was developed to estimate corrosion-indued rebar mass loss with independent variables, including concrete cover, rebar diameter, age of the reinforced concrete, concrete strength and GPR scanning parameters. Accelerated Corrosion Amplitude Ground Penetrating Radar Multivariant Regression Model Two-way Travel Time Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1 Introduction Embedded steel rebar corrosion in concrete is a time-dependent electromagnetic phenomenon and is one of the major causes of structural deterioration. It occurs due to the infiltration of carbon dioxide and chlorine through the protective concrete cover. Any loss of protective cover can lead to rebar corrosion and concrete deterioration, such as cracking, spalling and debonding. Consequently, structural capacity deteriorates and the service life could be compromised [ 1 ]. Many previous catastrophic collapses of concrete structures were due to a lack of knowledge on the extent of embedded rebar corrosion [ 2 ]. According to the National Association of Corrosion Engineers (NACE) report, approximately 1 to 5% of the USA Gross Domestic Product (GDP) is allocated to repairing and rehabilitating structures with corrosion damage [ 3 ]. Developing a reliable monitoring and condition assessment methodology for affected concrete structures is essential to make informed decision on the extent of rebar corrosion, any effect on structural safety and serviceability, and to effectively reduce the maintenance/rehabilitation budget. Visual inspection is one of the conventional ways for condition assessment of structures. Rebar corrosion detection at an early stage through visual inspection is quite difficult and requires and may not be effective or accurate. In general, Non-Destructive Evaluation (NDE) is considered a more reliable method than visual inspection. Electrochemical methods such as Half-Cell Potential (HCP), Linear Polarization Resistance (LPR), and X-ray Computed Tomography (XCT) have been widely used for the condition assessment of corroded concrete structures. However, these methods have certain limitations. The HCP and LPR are semi-destructive methods requiring physical contact with embedded rebars, and they only detect instantaneous corrosion activity. The XCT is unsuitable for on-site measurements due to its large size and transportation challenges [ 1 ]. Apart from the methods mentioned above, Ground Penetrating Radar (GPR) is another NDE technique to determine reinforcement cover, concrete thickness, rebar location and spacing, localization of voids and cracks and concrete deterioration mapping [ 4 , 5 ]. Past studies showed that GPR can detect rebar corrosion through differential amplitude reflection. Narayanan et al. [ 6 ], found that reflected GPR amplitude can indicate possible corrosion in steel rebars. Hubbard et al. [ 7 ] investigated the difference between GPR signals collected before and after accelerated rebar corrosion for 10 days. It was found that the GPR amplitude was affected, and the two-way travel time (TWTT) was influenced by the moisture introduced during the accelerated corrosion process. Zaki et al. [ 2 ] used GPR for corrosion detection in acceleratedly corroded rebars in a slab and deduced that the reflected wave amplitude was reduced due to corrosion. Lai et al. [ 8 ] also monitored the accelerated corrosion of concrete specimens with GPR and observed amplitude reduction as the corrosion level increased. This agreed with the finding from Hong et al. [ 9 , 10 ]. In addition, Lai et al. [ 11 ] proposed a new corrosion evaluation approach using GPR, employing changes in lapsed travel time, amplitudes and peak frequencies. The maximum positive amplitude of the reflected wave was found to change at different phases of corrosion, and the reflected amplitude decreased with an increase in travel time. Zhan et al. [ 12 ] investigated the changes in GPR parameters during accelerated corrosion. A reduction in TWTT and an increase in the amplitude of the reflected wave with an increase in corrosion rate were found. Hasan and Yazdani [ 4 ] corroded three steel rebars with accelerated corrosion, then placed them at different depths under known dielectric constant materials to simulate concrete embedment. The rebars were then scanned using a GPR, which showed an increase in TWTT and a decrease in amplitude, agreeing with the findings from other researchers [ 13 – 15 , 5 ]. Actual rebar corrosion in real life is often a slow process, and waiting a long time to measure the corrosion extent is not practical. Hence, accelerated corrosion through impressed current techniques is a suitable solution to induce rebar corrosion in a relatively short time. In addition to saving time, it is cost-effective, and the corrosion rate can be controlled as needed [ 16 ]. Several previous accelerated corrosion studies were conducted using the impressed current technique [ 17 – 19 ]. Impressed current techniques are also known as galvanostatic, which uses a direct current (DC) from an external power source to corrode steel embedded in the concrete Prior studies have shown the differences in GPR data collected before and after the rebar corrosion process. However, none of them investigated the effect of important parameters, such as rebar diameter and concrete cover, concrete strength, and corrosion duration on GPR data response. Understanding the effect of these parameters will allow more precise and targeted rebar corrosion determination than the general GPR-based methods from the past. The current study was conducted to bridge this significant knowledge-gap through an experimental investigation of these parameters under the accelerated corrosion environment. In addition, a reliable multivariate equation was developed to quantify the amount of embedded rebar corrosion in consideration of these parameters. 2 Methodology 2.1 Experimental Design The factorial design method was used in the current study to determine the number of samples considering rebar dimeter, concrete strength, concrete cover and corrosion period as parameters [ 20 ]. The Minitab statistical software was utilized for this purpose employing three degrees of freedom [ 21 ]. The first degree expressed two levels of concrete strength: (1) Normal strength concrete (NSC) with low porosity; and (2) Low strength concrete (LSC) with high porosity. The compressive strength for Normal Strength Concrete ranges from 20 to 40 MPa whereas any concrete strength below that is connsidred as Low Strength Concrete. ACI 318 − 19 [ 22 ] prescribes minimum concrete covers between 19 to 76 mm for embedded rebars. Thus, the second degree considered 38, 50 and 75 mm covers. The third degree was used for different corrosion exposure periods. The experimental design is shown in Table 1 . Table 1 Experimental Design Criteria No. Accelerated corrosion period (days) Concrete strength (MPa) Concrete porosity (% air content) Concrete cover (mm) Rebar diameter (mm) No. of Samples 1 30 47 1.5 50 10, 22, 32 2 2 20 47 1.5 50 10, 22, 32 2 3 10 47 1.5 75 10, 22, 32 2 4 10 19 4.5 38 10, 22, 32 2 5 10 47 1.5 50 10, 22, 32 2 6 30 19 4.5 50 10, 22, 32 2 7 20 19 4.5 38 10, 22, 32 2 8 20 47 1.5 75 10, 22, 32 2 9 20 47 1.5 38 10, 22, 32 2 10 20 19 4.5 75 10, 22, 32 2 11 30 47 1.5 38 10, 22, 32 2 12 10 19 4.5 50 10, 22, 32 2 13 30 19 4.5 75 10, 22, 32 2 14 30 19 4.5 38 10, 22, 32 2 15 10 19 4.5 75 10, 22, 32 2 16 30 47 1.5 75 10, 22, 32 2 17 20 19 4.5 50 10, 22, 32 2 18 10 47 1.5 38 10, 22, 32 2 Total = 36 2.2 Sample Preparation A total of 36 rectangular beams of 200 x 380 x 910 mm size were prepared. The dimensions were chosen to account for the GPR antenna footprint, ensuring that reflections were free of interference from the edges and the bottom. Thus, the GPR waveforms contained two genuine signals, the direct wave propagating from the transmitter to the receiver and the signals reflected back from the steel rebars. The study used NSC and LSC with 28-day target compressive strengths of 19 and 47 MPa respectively. Table 2 shows the concrete mix designs. Each specimen contained six steel rebars with diameters of 10, 22 and 32 mm (#3, #7 and #10), out of which three were Grade 60 type and the remaining three were Type 304 stainless steel. The rebars were placed at 100 mm center-to-center spacing. Grade 60 rebars were used as anodes and type 304 stainless steel rebars were used as cathodes during the impressed current accelerated corrosion process. Table 2 Concrete Mix Designs LSC NSC Material Weight per m 3 Material Weight per m 3 Cement (Type I/II) 268 kg Cement (Type I/II) 346 kg Fly Ash 67 kg Fly Ash 87 kg Coarse Aggregate 606 kg Coarse Aggregate 1,098 kg Fine Aggregate 872 kg Fine Aggregate 743 kg Water Reducing Admixture 1,465 gm High Range Water Reducing Admixture 1,891 gm Air-Entraining Admixture 105 gm Viscosity Modifying Admixture 812 gm Water 143 kg Water 153 kg Air Void 0.035 m 3 Air Void 0.012 m 3 Plywood and lumber were used to make the formworks [Fig. 1 (a)]. Holes were drilled on the plywood to extend the rebars for the impressed current connection. Rope anchors were placed in the formwork to facilitate the moving of the beams. The formworks were sealed with silicone caulking. The specimens were compacted and troweled during casting to ensure proper consolidation and finish as per the ASTM C-31 specifications [ 32 ] [Fig. 1 (b)]. Twelve concrete cylinders (six each for NSC and LSC) were made to verify the target compressive strengths. A curing compound was applied to the exposed surfaces of the beams and the cylinders to facilitate curing. The specimens were covered with polythene sheets and the formwork was removed after four days. 2.3 Accelerated Corrosion Accelerated corrosion was induced in all 36 specimens by immersion in a 5% (30,000 ppm) NaCl solution [ 13 , 24 ]. Per ASTM A615/A615M-15a specifications [ 23 ], the Grade 60 (A572) rebar was used as anode and the Type 304 stainless steel rebar was used as cathode during the impressed current technique, as shown in Fig. 2 . Prior to immersion, one end of the wire was connected to the extended portion of the anode and the cathode in the form of a coil and then wrapped with white Teflon tape [Fig. 3 (a)]. This prevented corrosion of the extended portions of the rebars. The other end of the wire was soldered to size 35 mm alligator clips that were subsequently connected to the main circuit board. The specimens were immersed in 1143 x 965 x 965 mm plastic totes containing electrolyte NaCl solution, and finally a power supply was connected to the circuit board [Fig. 3 (b)]. A current of 0.65 A was used since a prior study found that an acceptable current range is 0.1 to 2 A [ 25 ]. A parallel connection scheme was used for the specimens to ensure an uninterrupted current supply in case of any connection failure. There are no existing guidelines for accurately time scaling accelerated corrosion period with the equivalent in-situ corrosion period. Most prior investigations employed current levels three to 100 times larger than the highest observed in field studies [ 26 ]. A past study [ 27 ] stated that the degree of damage produced by a current density of 3 µA/cm 2 in one year could be attained in two hours by utilizing a current density of 10400 µA/cm 2 . By incorporating this into the current study and with the constant 5% chloride bath solution and 0.65 A current flow, 10, 20 and 30 days of accelerated corrosion times corresponded to approximately 28, 56 and 84 years of real-life corrosion age of concrete structures, respectively. 3 GPR Scanning The GPR scans were performed using a 2.6 GHz antenna connected to a data acquisition system. This antenna can provide a high-resolution concrete scan up to a depth of 250 mm. To partially prevent interference from salt water, the top surface of the specimen was dried for a day before scanning. Eight scans were recorded for each sample at the following sequences: before the saline submersion, at 10 day intervals during saline submersion and at the end of a predetermined corrosion level. Figure 4 displays a sample during scanning. The first five scans were conducted normal to the embedded rebars, while the remaining three scans were made along the lengths of the rebars. Waveforms, frequency and amplitudes were recorded during the scanning process. The scans were analyzed employing the GPRSlice Software [ 28 ], considering different filters, background noise removal and time-zero cancellation. The exported results were calibrated according the the positioning of the rebars during sample casting. 4 Rebar Mass Loss Estimation Following the accelerated corrosion and scanning, the corroded rebars were extracted from the specimens using a jackhammer and cleaned per ASTM G1-03 [ 29 ] standard. The reinforcement percentage mass loss was then calculated by comparing the initial (new) rebar weight before corrosion and the final rebar weight after the corrosion, extraction and cleaning processes. 5 Results and Discussion The amplitude and TWTT of GPR waveforms were altered with multiple levels of corrosion. During the chloride contamination phase, the salt solution began seeping into the concrete and chloride ions accumulated around the rebars. This resulted in two simultaneous effects. First, ion buildup reduced the amplitude of the reflected waveforms. Second, the absorption of the GPR wave energy shortened the TWTT. The ions further affected the rebar passive layer, causing corrosion initiation. Corrosion products were distributed within the concrete pores as the corrosion progressed. As a result, the travel time from the direct wave to the anode bar was lengthened, resulting in increased amplitude. Stable ions in the anode are continually consumed, resulting in corrosion products that expand and cause micro-cracks within the concrete cover. Interfaces such as steel, corrosion product, concrete, fissures and outward movement of corrosion products result in broader footprints of the radar waveforms during the corrosion process. Figure 5 shows sample amplitude variation in NSC samples with a 50 mm cover. At the final stage, the GPR amplitude increased for all rebars with an increase in the duration of accelerated corrosion. 5.1 Influence of Parameters on Amplitude Figures 6 and 7 show change in maximum positive GPR amplitude with an increase in rebar diameter and concrete cover. The bigger surface area of 32 mm (#10) rebars meant more exposure to the corrosive environment and higher amplitudes than the smaller 22 mm and 10 mm (#3 and #7) rebars. As the rebar diameter increased, the amplitude values also increased. Figure 8 shows the effect of changing concrete strength and porosity on the GPR amplitude. The amplitudes from LSC specimens were greater than the amplitudes from NSC specimens for all corrosion durations. LSC contains a high percentage of air, and the dielectric constant of air is lower than concrete. Air absorbs less radar radiation resulting in a higher amplitude for LSC samples. Figure 9 shows the effect of concrete cover on GPR amplitude. The amplitudes for 75 mm and 50 mm covers were smaller than that for 38 mm cover since the radar waves had to travel longer distances for increased cover depths, causing most of the radar energy to be absorbed. The greater the depth, the greater the loss of radar energy and the smaller the amplitude of the reflected wave. 5.3 Influence of Parameters on TWTT The TWTT of reflected waveforms at different corrosion levels were plotted for varying rebar diameters, concrete strengths and concrete covers, as shown in Figs. 10 and 11 . The TWTT dropped during the saltwater contamination phase and increased with the progression of the corrosion period. This maybe attributed to increased corrosion within the concrete pores. Restoration of the original TWTT and increased value was noticed later as a result of concrete cover drying and corrosion product dispersion inside the concrete pores, suggesting an increase in the dielectric property of concrete. The TWTT, similar to amplitude, varies with rebar sizes for different concrete covers. Even though there was little change in the TWTT between different rebar diameters, there was a significant variance in TWTT at different cover depths because the wave traveled longer in concrete for increased cover. It is apparent that increased cover resulted in greater TWTT. Similar to the amplitude, the TWTT changes with concrete strength and porosity. The TWTT for LSC is much higher than that in the NSC samples. 5.4 Rebar Mass Loss Parameters such as chloride ions, water content and current flow were kept constant throughout the accelerated corrosion process. Consequently, the rebar mass loss was directly related to the corrosion levels. As shown in Fig. 12 , the percentage rebar mass loss was the highest for LSC specimens with the smallest rebar diameter and clear cover and subjected to the longest corrosion duration. The length of rebars extending outside the concrete specimens for making electrical connections was not considered in calculating rebar mass loss. 5.5 Quantitative Relationships A multivariant linear regression equation, Eq. 1, was generated through the python code [ 30 ] as a quantitative relationship between rebar corrosion mass loss and related parameters. Linear regression is a basic and extensively used type of predictive analysis that works with continuous data. Multivariant linear regression allows researchers to predict or explain criterion variable scores based on scores on two or more predictor factors and knowledge of the relationships between them. Our approach was to determine how much each predictor variable contributes to explaining the criterion variable by providing a regression or beta coefficient), which indicates the extent to which each predictor variable contributes to explaining the criterion variable scores while controlling for the other predictor variables [ 33 ]. The coefficient of determination, R 2 , was 0.84 as determined from the IBM SPSS Statistics [ 31 ] software, implying that the model’s inputs can explain around 84% of the observed variation. C r = 5.095 + 0.122 a – 0.012 S − 0.183 d – 1.208 t + 0.016 z − 2.8*10 − 10 A (1) Where: C r = Corrosion amount (%) a = Corrosion period (years) S = Concrete strength (MPa) d = Initial (new) rebar diameter (mm) t = Two-way travel time (ns) z = Concrete cover (mm) A = GPR amplitude In evaluating existing structures, the concrete strength, cover and rebar diameter may be found from as-built drawings and in-situ or laboratory testing. The GPR amplitude and TWTT can be obtained from the GPR scanning. In the absence of initial parameters for the equation input, normalization can be performed by back-calculating the GPR amplitude collected from a corrosion-free section from the same structure (where C r = 0%), followed by inspection and exposing a portion of rebar. Based on this, the residual rebar area can be estimated by deducting the corrosion percentage from the uncorroded rebar area, which may be used to compute the remaining capacity of reinforced concrete structures. Knowing the residual capacity of a member can aid engineers and owners in making an informed decision to retrofit or replace building or bridge structural components. 5.6 Model Validation The proposed model was validated by comparing with the corrosion rate of a rebar retrieved during the demolition of a 32-year-old bridge deck on IH-30 in Dallas, Texas (Fig. 13 ). The compressive strength of the deck concrete was 20.7 MPa. The retrieved sample had 203 mm long and 12.7 mm (#4) in diameter corroded rebars embedded in 50 mm thick concrete. A GPR scan was conducted on the sample and data were obtained. The GPR scan waveform is shown in Fig. 14 . The post-processed GPR data from the sample resulted in an average value of 5,758 amplitude and 0.9 ns TWTT. Employing the proposed Eq. 1, a 6.14% corrosion rate was predicted for the rebar. The corroded rebar was extracted from the concrete sample, cleaned per ASTM G1-03 [ 29 ] and then weighed to determine the mass loss owing to natural corrosion. A mass loss of 6.3% was determined, considering the masses of the extracted rebar and uncorroded rebar with the same length and diameter. So, the corrosion rate from the proposed equation was around 0.16% lower than the actual value, which is an excellent match. Limitations Accelerated corrosion of steel rebars in concrete over a condensed period results in increased corrosion product concentrations around the rebars and diffusion of these products into the pores [ 25 ]. However, the extent of this activity within the concrete cover is minimal. As a result, the properties of the concrete around the rebars remain unchanged. When natural corrosion occurs over a longer time, corrosion products infiltrate more deeply into the surrounding concrete pores, resulting in a significant variation in the concrete property. The current study considered concrete strength with assumed porosity as one of the degrees of freedom. However, measured porosity as a parameter would improve the accuracy of the proposed model. In addition, incorporating water content as another parameter could also enhance the model since the concrete moisture profile influences GPR readings. Conclusions The following conclusions may be made based on the findings from this study: The present study successfully quantified the amount of steel rebar corrosion in concrete beams using GPR scanning. The research involved fabricating reinforced concrete beam specimens, exposing the beams to accelerated corrosion, scanning samples utilizing a 2.6 GHz frequency GPR, analyzing the data and developing a multivariant regression equation. Larger rebar sizes underwent more corrosion than smaller rebars due to the additional surface area and corrosion potential. As a result, the GPR reflected wave amplitudes also increased. The reflected GPR amplitudes from Low Strength Concrete (LSC) specimens were greater than the amplitudes from Normal Strength Concrete (NSC) specimens for all corrosion durations. LSC has higher air content than NSC and the dielectric constant of air is lower than concrete. Air absorbs less radar radiation resulting in higher amplitudes for LSC. Larger concrete covers for rebars resulted in smaller reflected GPR wave amplitudes. This is because the radar waves travel longer distances for larger cover depths, causing most of the radar energy to be absorbed by the concrete. It was found that the corrosion stages on the embedded rebars had a significant impact on the maximum amplitude and the Two Way Travel Time (TWTT) of the GPR signal. For a given cover depth, rebar diameter and concrete strength, the amplitude remained constant prior to corrosion commencement. The reflected amplitude decreased at the commencement of the accelerated corrosion but increased after a certain period. This may be attributed to increased corrosion within the concrete pores with time. Restoration of the original TWTT and increased value was noticed at latter stages due to concrete cover drying and corrosion product dispersion inside the concrete pores, suggesting an increase in the concrete dielectric property. The available chloride ions, water content and current flow were kept constant throughout the accelerated corrosion process. Therefore, the rebar corrosion mass loss was directly related to the corrosion levels. The mass loss was highest in LSC samples with the smallest rebar diameter and cover, and under longest corrosion duration. A reliable multivariant linear regression model was developed to determine the corrosion amount in consideration of the parametric effects. A user-friendly step by step approach is presented for estimating or finding the various input parameters, such as concrete strength, concrete cover, rebar diameter, GPR amplitude, GPR TWTT. The proposed regression model was validated by comparing with the actual corrosion rate of an embedd rebar retrieved during the demolition of an old concrete bridge deck in Dallas, TX. The model corrosion percent output was only 0.16% lower than the actual rebar mass loss, yielding a very high accuracy of 97.5%. Declarations Acknowlegement The study was performed under a grant from the Texas Department of Transportation (TxDOT). Author Contributions KBJ: Experimental work, Numerical modeling, manuscript drafting; NY: Study conceptualization, study coordination, manuscript review and updating; EB: Numerical modeling, Experimental work; MMR: Experimental work, manuscript drafting. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing Interests The authors declare that they have no competing interests as defined by Springer or other interests that might be perceived to influence the results and or discussion reported in this paper. Ethics Approval and Concent to Participate Not applicable Consent for Publication Not applicable References Hong S, Chen D, Dong B (2022) Numerical simulation and mechanism analysis of GPR-based reinforcement corrosion detection. Construction and Building Materials 317 . https://doi.org/10.1016/j.conbuildmat.2021.125913, Zaki A, Johari MAM, Hussin WMAW, Jusman Y (2018) Experimental Assessment of Rebar Corrosion in Concrete Slab Using Ground Penetrating Radar (GPR). International Journal of Corrosion. https://doi.org/10.1155/2018/5389829. Koch G, Varney J, Thompson N, Moghissi O, Gould M, Payer J (2016) International measures of prevention, application, and economics of corrosion technologies study,” Report No. OAPUS310GKOCH (AP110272), NACE international Houston, USA. Hasan MI, Yazdani N (2016) An experimental study for quantitative estimation of rebar corrosion in concrete using ground penetrating radar. Journal of Engineering (United Kingdom). https://doi.org/10.1155/2016/8536850. Hong SX, Wiggenhauser H, Helmerich R, Dong BQ, Dong, Xing F (2017) Long-term monitoring of reinforcement corrosion in concrete using ground penetrating radar. Corrosion Science 114: 123–132.. https://doi.org/10.1016/j. corsci.2016.11.003. Narayanan RM, Hudson SG, Kumke CJ, Beacham MW, Hall DD (1998) Detection of Rebar Corrosion in Bridge Decks using Statistical Variance of Radar Reflected Pulses. Seventh International Conference on Ground Penetrating Radar 601-605. Hubbard SS; Zhang J; Monteiro P.M; Peterson JE; Rubin Y (2003) Experimental Detection of Reinforcing Bar Corrosion Using Nondestructive Geophysical Techniques. ACI Materials Journal 100(6): 501-510. Lai WWL, Kind T, Wiggenhauser H (2011) Using ground penetrating radar and time-frequency analysis to characterize construction materials. NDT E International 44: 111–120. Hong S, Lai WWL, Wilsch G, Helmerich R, Helmerich R, Günther T, Wiggenhauser H (2014) Periodic mapping of reinforcement corrosion in intrusive chloride contaminated concrete with GPR. Construction of Building Materials 66: 671–684. Hong S, Lai WWL, Helmerich R (2015) Experimental monitoring of chloride-induced reinforcement corrosion and chloride contamination in concrete with ground-penetrating radar. Structure Infrastructucture Engineering 11: 15–25. Lai WWL, Kind T, Stoppel M, Wiggenhauser H (2013) Measurement of Accelerated Steel Corrosion in Concrete Using Ground- Penetrating Radar and a Modified Half-Cell Potential Method. Journal of Infrastructure System 19: 205–220. Zhan BJ, Lai WWL, Kou SC, Poon CS, Tsang WF (2011) Correlation between accelerated steel corrosion in concrete and ground penetrating radar parameters. In Proceedings of the International RILEM Conference on Advances in Construction Materials Through Science and Engineering, Hong Kong, China. Raju RK, Hasan MI, Yazdani N (2018) Quantitative relationship involving reinforcing bar corrosion and ground-penetrating radar amplitude. ACI Materials Journals 115: 449–457. Sossa V, Pérez-Gracia V, González-Drigo R, Rasol MA (2019) Lab nondestructive test to analyze the effect of corrosion on ground penetrating radar scans. Remote Sen. 11: 2814. Senin SF, Hamid R, Ahmad J, Rosli MIF, Yusuff A, Rohim R, Abdul Ghani KD, Mohamed Noor S (2019) Damage detection of artificial corroded rebars and quantification using non-destructive methods on reinforced concrete structure. J. Phys. Conf. Ser. 1349(1): 012044. https://doi.org/10.1088/1742-6596/ 1349/1/012044. Ahmad S (2009) Techniques for inducing accelerated corrosion of steel in concrete. Arabian Journal for Science and Engineering 34(2C): 95–104. Ahmed SF, Maalej M, Paramasivam P, Mihashi H (2006) Assesment of Corrosion-Induced Damage and its Effect on the Structural Behavior of RC Beams Containing Supplementary Cementitious Materials. Progress in Structural Engineering and Materials 8(2): 69–77. Ha TH, Muralidharan S, Bae JH, Ha YC, Lee HG, Park KW, Kim DK (2007) Accelerated Short-Term Techniques to Evaluate the Corrosion Performance of Steel in Fly Ash Blended Concrete. Building and Environment 42: 78–85. Almusallam A (2001) Effect of degree of corrosion on the properties of reinforcing steel bars. Construction and Building Materials 15(8):, 361–368. Shahar N, Renan S, Speyer E, Gueta T, Alan R, David S (2012) A factorial design experiment as a pilot study for noninvasive genetic sampling. Molecular Ecology Resources. https://doi.org/10.1111/j.1755-0998.2012.03170. Minitab statistical software (1998). ACI Committee 318 (2018) Building Code Requirements for Structural Concrete: (ACI 318-19); and Commentary (ACI 318R-19). Farmington Hills, MI: American Concrete Institute. ASTM A615/A615M-15a. (2015) Standard specification for deformed and plain carbon-steel bars for concrete reinforcement. Abouhussien AA, Hassan AAA (2014) Experimental and empirical time to corrosion of reinforced concrete structures under different curing conditions.Advances in Civil Engineering. https://doi.org/10.1155/2014/595743. El Maaddawy T, Soudki K (2003) Effectiveness of impressed current technique to simulate corrosion of steel reinforcement in concrete. Journal of Materials in Civil Engineering 15(1): 41–47. Broomfield JP (1997) Corrosion of steel in concrete: Understanding, investigation, and repair. London Malumbela G, Moyo P, Alexander M (2012) A step towards standardising accelerated corrosion tests on laboratory reinforced concrete specimens. J. South African Inst. Civ. Eng. 54: 78–8. GPR-SLICE (2021) Ground Penetrating Radar Imaging Software, Version 7 MT, Geophysical Archaeometry Laboratory Inc. ASTM G1-03 (2011) Standard Practice for Preparing, Cleaning, and Evaluating Corrosion Test Specimens, ASTM International, West Conshohocken, PA. Python Software Foundation. (2021) Python Language Reference, version 3.10. Available at http://www.python.org. IBM Corp. (2020) IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY: IBM Corp. ASTM C-31 (2019) Standard Practice for Making and Curing Concrete Test Specimens in the Field, ASTM International, West Conshohocken, PA. Sethuraman VS, Suguna K (2016) Regression based analysis and visualization of for identifying Flexural Behaviour of M60 Beams under repeated Compressive Load based on observational data sets. Procedia Computer Science 87: 264 – 269.. Additional Declarations No competing interests reported. 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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-4366218","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":300286422,"identity":"5e514753-0dc7-41a6-8d85-8680fee4d0bd","order_by":0,"name":"Khadiza Binte Jalal","email":"data:image/png;base64,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","orcid":"","institution":"Bridge Engineer, HNTB","correspondingAuthor":true,"prefix":"","firstName":"Khadiza","middleName":"Binte","lastName":"Jalal","suffix":""},{"id":300286424,"identity":"77437ef3-0f30-4f16-9944-828bdb4326e2","order_by":1,"name":"Nur Yazdani","email":"","orcid":"","institution":"University of Texas at Arlington","correspondingAuthor":false,"prefix":"","firstName":"Nur","middleName":"","lastName":"Yazdani","suffix":""},{"id":300286426,"identity":"dfd750cc-061f-4d90-977d-03ec941003e5","order_by":2,"name":"Eyosias Beneberu","email":"","orcid":"","institution":"Bridgefarmer \u0026 Associates, Inc","correspondingAuthor":false,"prefix":"","firstName":"Eyosias","middleName":"","lastName":"Beneberu","suffix":""},{"id":300286428,"identity":"1fa157f8-2337-4778-a9b5-c4413337d979","order_by":3,"name":"Mohd Mezanur Rahman","email":"","orcid":"","institution":"Consor Engineers","correspondingAuthor":false,"prefix":"","firstName":"Mohd","middleName":"Mezanur","lastName":"Rahman","suffix":""}],"badges":[],"createdAt":"2024-05-04 00:41:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4366218/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4366218/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56174512,"identity":"a99426f4-7168-4b20-beea-82290d1f8823","added_by":"auto","created_at":"2024-05-09 12:44:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1707887,"visible":true,"origin":"","legend":"\u003cp\u003eSample preparation: (a) Formwork; (b) Concrete casting\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4366218/v1/af56340ecac88a16e5a2aecc.png"},{"id":56173916,"identity":"648393e0-dcf8-4dae-9ab2-31b92200451c","added_by":"auto","created_at":"2024-05-09 12:36:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72872,"visible":true,"origin":"","legend":"\u003cp\u003eAccelerated corrosion process, schematic diagram\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4366218/v1/abcc3bda7638b787cfac2ba6.png"},{"id":56174508,"identity":"e9aaf8c4-0e9e-481f-bfe0-740e7b2de517","added_by":"auto","created_at":"2024-05-09 12:44:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1714179,"visible":true,"origin":"","legend":"\u003cp\u003eAccelerated corrosion: (a) Anode-cathode wiring connection; (b) Power supply to the submerged specimens\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4366218/v1/08c0e8f15c5222e580d86791.png"},{"id":56173547,"identity":"9b3df807-737c-448d-a73e-f82a0c6f14f0","added_by":"auto","created_at":"2024-05-09 12:28:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1133882,"visible":true,"origin":"","legend":"\u003cp\u003eGPR Scanning\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4366218/v1/e75cf4b434cdbbf7c415cb95.png"},{"id":56173921,"identity":"98044250-a6fa-45c9-80a2-8f8dfe00001d","added_by":"auto","created_at":"2024-05-09 12:36:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1243026,"visible":true,"origin":"","legend":"\u003cp\u003eGPR amplitude contours for NSC samples with 50 mm cover: (a) 10 mm (#3) rebar before corrosion; (b) 10 mm (#3) rebar at 10 days of corrosion; (c) 10 mm (#3) rebar at 20 days of corrosion; (d) 10 mm (#3) rebar at 30 days of corrosion; (e) 22 mm (#7) rebar before corrosion; (f) 22 mm (#7) rebar at 10 days of corrosion; (g) 22 mm (#7) rebar at 20 days of corrosion; (h) 22 mm (#7) rebar at 30 days of corrosion; (i) 32 mm (#10) rebar before corrosion; (j) 32 mm (#10) rebar at 10 days of corrosion; (k) 32 mm (#10) rebar at 20 days of corrosion; (l) 32 mm (#10) rebar at 30 days of corrosion\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4366218/v1/0f4a6751ad4ff2f4dc9190d8.png"},{"id":56173930,"identity":"2c0c091e-6dda-4091-81f9-32b766ddcaad","added_by":"auto","created_at":"2024-05-09 12:36:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":258440,"visible":true,"origin":"","legend":"\u003cp\u003eVariation of GPR amplitudes: (a) NSC with 38 mm cover; (b) NSC with 50 mm cover; (c) NSC with 75 mm cover\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4366218/v1/5cbb062c40c16f74f7533f6f.png"},{"id":56173924,"identity":"fe4802c5-3efb-4cb0-a9d5-cfc010a01086","added_by":"auto","created_at":"2024-05-09 12:36:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":261030,"visible":true,"origin":"","legend":"\u003cp\u003eVariation of GPR amplitudes: (a) LSC with 38 mm cover; (b) LSC with 50 mm cover; (c) LSC with 75 mm cover\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4366218/v1/30d15fc20ac0157d5919a147.png"},{"id":56173553,"identity":"970534c2-0522-4e83-89e3-da78a004c639","added_by":"auto","created_at":"2024-05-09 12:28:14","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":183229,"visible":true,"origin":"","legend":"\u003cp\u003eGPR amplitude variations with concrete type and rebar diameter\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4366218/v1/ca37d4a30cd73f89f7c2ccb5.png"},{"id":56175323,"identity":"879d9faa-14e9-4c0a-a494-06a3bda7dffc","added_by":"auto","created_at":"2024-05-09 12:52:37","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":292079,"visible":true,"origin":"","legend":"\u003cp\u003eGPR amplitude variation with cover depth and rebar diameter\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4366218/v1/bf1e82dfd3aa302562f70b7d.png"},{"id":56173558,"identity":"5174da74-c0b6-4563-997c-4482bb973b2f","added_by":"auto","created_at":"2024-05-09 12:28:15","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":240740,"visible":true,"origin":"","legend":"\u003cp\u003eVariation of TWTT: (a) NSC with 38 mm cover; (b) NSC with 50 mm cover; (c) NSC with 75 mm cover\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4366218/v1/574bb5b53ae6a6fab1a7a463.png"},{"id":56173927,"identity":"86c9709a-7b38-4f3d-9a9d-32ab6c8a3135","added_by":"auto","created_at":"2024-05-09 12:36:16","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":230716,"visible":true,"origin":"","legend":"\u003cp\u003eVariation of TWTT: (a) LSC with 38 mm cover; (b) LSC with 50 mm (c) LSC with 75 mm cover\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-4366218/v1/cfe70ff0f0b2fbadda0adc78.png"},{"id":56175299,"identity":"b7a29a3a-5c4a-4100-98bf-b75e64af33e7","added_by":"auto","created_at":"2024-05-09 12:52:30","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":465110,"visible":true,"origin":"","legend":"\u003cp\u003eChange in percentage of corrosion with: (a) Rebar diameter; (b) Corrosion period; (c) Concrete cover; (d) Concrete strength\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-4366218/v1/8c798c0ddbe36dc509c2b759.png"},{"id":56173559,"identity":"b83ab2ab-654f-4bc3-9ea1-a4110bc634b0","added_by":"auto","created_at":"2024-05-09 12:28:16","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":799773,"visible":true,"origin":"","legend":"\u003cp\u003eSpecimen obtained from the IH-30 bridge deck\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-4366218/v1/c347e4905369a50da2d4716b.png"},{"id":56174516,"identity":"7d7a43ba-155f-4d36-807a-4e5d3e07ee62","added_by":"auto","created_at":"2024-05-09 12:44:15","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":376589,"visible":true,"origin":"","legend":"\u003cp\u003eGPR waveform for 12.7 mm (#4) rebar\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-4366218/v1/c07d966daed67273849b7d9d.png"},{"id":99792198,"identity":"b90cbcc9-5004-4894-98b6-239e8e7de32c","added_by":"auto","created_at":"2026-01-08 13:16:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14094363,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4366218/v1/2927d743-2e82-4b52-9223-3eb3d0a79f6d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Experimental Detection of Embedded Rebar Corrosion in Concrete with Ground Penetrating Radar","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eEmbedded steel rebar corrosion in concrete is a time-dependent electromagnetic phenomenon and is one of the major causes of structural deterioration. It occurs due to the infiltration of carbon dioxide and chlorine through the protective concrete cover. Any loss of protective cover can lead to rebar corrosion and concrete deterioration, such as cracking, spalling and debonding. Consequently, structural capacity deteriorates and the service life could be compromised [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Many previous catastrophic collapses of concrete structures were due to a lack of knowledge on the extent of embedded rebar corrosion [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the National Association of Corrosion Engineers (NACE) report, approximately 1 to 5% of the USA Gross Domestic Product (GDP) is allocated to repairing and rehabilitating structures with corrosion damage [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Developing a reliable monitoring and condition assessment methodology for affected concrete structures is essential to make informed decision on the extent of rebar corrosion, any effect on structural safety and serviceability, and to effectively reduce the maintenance/rehabilitation budget.\u003c/p\u003e \u003cp\u003eVisual inspection is one of the conventional ways for condition assessment of structures. Rebar corrosion detection at an early stage through visual inspection is quite difficult and requires and may not be effective or accurate. In general, Non-Destructive Evaluation (NDE) is considered a more reliable method than visual inspection. Electrochemical methods such as Half-Cell Potential (HCP), Linear Polarization Resistance (LPR), and X-ray Computed Tomography (XCT) have been widely used for the condition assessment of corroded concrete structures. However, these methods have certain limitations. The HCP and LPR are semi-destructive methods requiring physical contact with embedded rebars, and they only detect instantaneous corrosion activity. The XCT is unsuitable for on-site measurements due to its large size and transportation challenges [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eApart from the methods mentioned above, Ground Penetrating Radar (GPR) is another NDE technique to determine reinforcement cover, concrete thickness, rebar location and spacing, localization of voids and cracks and concrete deterioration mapping [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Past studies showed that GPR can detect rebar corrosion through differential amplitude reflection. Narayanan et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], found that reflected GPR amplitude can indicate possible corrosion in steel rebars. Hubbard et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] investigated the difference between GPR signals collected before and after accelerated rebar corrosion for 10 days. It was found that the GPR amplitude was affected, and the two-way travel time (TWTT) was influenced by the moisture introduced during the accelerated corrosion process. Zaki et al. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] used GPR for corrosion detection in acceleratedly corroded rebars in a slab and deduced that the reflected wave amplitude was reduced due to corrosion. Lai et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] also monitored the accelerated corrosion of concrete specimens with GPR and observed amplitude reduction as the corrosion level increased. This agreed with the finding from Hong et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition, Lai et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] proposed a new corrosion evaluation approach using GPR, employing changes in lapsed travel time, amplitudes and peak frequencies. The maximum positive amplitude of the reflected wave was found to change at different phases of corrosion, and the reflected amplitude decreased with an increase in travel time. Zhan et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] investigated the changes in GPR parameters during accelerated corrosion. A reduction in TWTT and an increase in the amplitude of the reflected wave with an increase in corrosion rate were found.\u003c/p\u003e \u003cp\u003eHasan and Yazdani [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] corroded three steel rebars with accelerated corrosion, then placed them at different depths under known dielectric constant materials to simulate concrete embedment. The rebars were then scanned using a GPR, which showed an increase in TWTT and a decrease in amplitude, agreeing with the findings from other researchers [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eActual rebar corrosion in real life is often a slow process, and waiting a long time to measure the corrosion extent is not practical. Hence, accelerated corrosion through impressed current techniques is a suitable solution to induce rebar corrosion in a relatively short time. In addition to saving time, it is cost-effective, and the corrosion rate can be controlled as needed [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Several previous accelerated corrosion studies were conducted using the impressed current technique [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Impressed current techniques are also known as galvanostatic, which uses a direct current (DC) from an external power source to corrode steel embedded in the concrete\u003c/p\u003e \u003cp\u003ePrior studies have shown the differences in GPR data collected before and after the rebar corrosion process. However, none of them investigated the effect of important parameters, such as rebar diameter and concrete cover, concrete strength, and corrosion duration on GPR data response. Understanding the effect of these parameters will allow more precise and targeted rebar corrosion determination than the general GPR-based methods from the past. The current study was conducted to bridge this significant knowledge-gap through an experimental investigation of these parameters under the accelerated corrosion environment. In addition, a reliable multivariate equation was developed to quantify the amount of embedded rebar corrosion in consideration of these parameters.\u003c/p\u003e"},{"header":"2 Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental Design\u003c/h2\u003e \u003cp\u003eThe factorial design method was used in the current study to determine the number of samples considering rebar dimeter, concrete strength, concrete cover and corrosion period as parameters [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The Minitab statistical software was utilized for this purpose employing three degrees of freedom [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The first degree expressed two levels of concrete strength: (1) Normal strength concrete (NSC) with low porosity; and (2) Low strength concrete (LSC) with high porosity. The compressive strength for Normal Strength Concrete ranges from 20 to 40 MPa whereas any concrete strength below that is connsidred as Low Strength Concrete. ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] prescribes minimum concrete covers between 19 to 76 mm for embedded rebars. Thus, the second degree considered 38, 50 and 75 mm covers. The third degree was used for different corrosion exposure periods. The experimental design is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExperimental Design\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccelerated corrosion period\u003c/p\u003e \u003cp\u003e(days)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConcrete strength\u003c/p\u003e \u003cp\u003e(MPa)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConcrete porosity (% air content)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eConcrete cover\u003c/p\u003e \u003cp\u003e(mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRebar diameter (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eNo. of Samples\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e10, 22, 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e10, 22, 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e10, 22, 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e10, 22, 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e10, 22, 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e10, 22, 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e10, 22, 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e10, 22, 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e10, 22, 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e10, 22, 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e10, 22, 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e10, 22, 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e10, 22, 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e10, 22, 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e10, 22, 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e10, 22, 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e10, 22, 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e10, 22, 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eTotal\u0026thinsp;=\u0026thinsp;\u003cb\u003e36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample Preparation\u003c/h2\u003e \u003cp\u003eA total of 36 rectangular beams of 200 x 380 x 910 mm size were prepared. The dimensions were chosen to account for the GPR antenna footprint, ensuring that reflections were free of interference from the edges and the bottom. Thus, the GPR waveforms contained two genuine signals, the direct wave propagating from the transmitter to the receiver and the signals reflected back from the steel rebars.\u003c/p\u003e \u003cp\u003eThe study used NSC and LSC with 28-day target compressive strengths of 19 and 47 MPa respectively. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the concrete mix designs. Each specimen contained six steel rebars with diameters of 10, 22 and 32 mm (#3, #7 and #10), out of which three were Grade 60 type and the remaining three were Type 304 stainless steel. The rebars were placed at 100 mm center-to-center spacing. Grade 60 rebars were used as anodes and type 304 stainless steel rebars were used as cathodes during the impressed current accelerated corrosion process.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConcrete Mix Designs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLSC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNSC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaterial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003cp\u003eper m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaterial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003cp\u003eper m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCement (Type I/II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e268 kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCement (Type I/II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e346 kg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFly Ash\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFly Ash\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87 kg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoarse Aggregate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e606 kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoarse Aggregate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,098 kg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFine Aggregate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e872 kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFine Aggregate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e743 kg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater Reducing Admixture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,465 gm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh Range Water Reducing Admixture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,891 gm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAir-Entraining Admixture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 gm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eViscosity Modifying Admixture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e812 gm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143 kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153 kg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAir Void 0.035 m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAir Void 0.012 m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePlywood and lumber were used to make the formworks [Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(a)]. Holes were drilled on the plywood to extend the rebars for the impressed current connection. Rope anchors were placed in the formwork to facilitate the moving of the beams. The formworks were sealed with silicone caulking. The specimens were compacted and troweled during casting to ensure proper consolidation and finish as per the ASTM C-31 specifications [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] [Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(b)]. Twelve concrete cylinders (six each for NSC and LSC) were made to verify the target compressive strengths. A curing compound was applied to the exposed surfaces of the beams and the cylinders to facilitate curing. The specimens were covered with polythene sheets and the formwork was removed after four days.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Accelerated Corrosion\u003c/h2\u003e \u003cp\u003eAccelerated corrosion was induced in all 36 specimens by immersion in a 5% (30,000 ppm) NaCl solution [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Per ASTM A615/A615M-15a specifications [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], the Grade 60 (A572) rebar was used as anode and the Type 304 stainless steel rebar was used as cathode during the impressed current technique, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Prior to immersion, one end of the wire was connected to the extended portion of the anode and the cathode in the form of a coil and then wrapped with white Teflon tape [Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(a)]. This prevented corrosion of the extended portions of the rebars. The other end of the wire was soldered to size 35 mm alligator clips that were subsequently connected to the main circuit board. The specimens were immersed in 1143 x 965 x 965 mm plastic totes containing electrolyte NaCl solution, and finally a power supply was connected to the circuit board [Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(b)]. A current of 0.65 A was used since a prior study found that an acceptable current range is 0.1 to 2 A [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. A parallel connection scheme was used for the specimens to ensure an uninterrupted current supply in case of any connection failure.\u003c/p\u003e \u003cp\u003eThere are no existing guidelines for accurately time scaling accelerated corrosion period with the equivalent in-situ corrosion period. Most prior investigations employed current levels three to 100 times larger than the highest observed in field studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A past study [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] stated that the degree of damage produced by a current density of 3 \u0026micro;A/cm\u003csup\u003e2\u003c/sup\u003e in one year could be attained in two hours by utilizing a current density of 10400 \u0026micro;A/cm\u003csup\u003e2\u003c/sup\u003e. By incorporating this into the current study and with the constant 5% chloride bath solution and 0.65 A current flow, 10, 20 and 30 days of accelerated corrosion times corresponded to approximately 28, 56 and 84 years of real-life corrosion age of concrete structures, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 GPR Scanning","content":"\u003cp\u003eThe GPR scans were performed using a 2.6 GHz antenna connected to a data acquisition system. This antenna can provide a high-resolution concrete scan up to a depth of 250 mm. To partially prevent interference from salt water, the top surface of the specimen was dried for a day before scanning. Eight scans were recorded for each sample at the following sequences: before the saline submersion, at 10 day intervals during saline submersion and at the end of a predetermined corrosion level. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays a sample during scanning. The first five scans were conducted normal to the embedded rebars, while the remaining three scans were made along the lengths of the rebars. Waveforms, frequency and amplitudes were recorded during the scanning process. The scans were analyzed employing the GPRSlice Software [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], considering different filters, background noise removal and time-zero cancellation. The exported results were calibrated according the the positioning of the rebars during sample casting.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4 Rebar Mass Loss Estimation","content":"\u003cp\u003eFollowing the accelerated corrosion and scanning, the corroded rebars were extracted from the specimens using a jackhammer and cleaned per ASTM G1-03 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] standard. The reinforcement percentage mass loss was then calculated by comparing the initial (new) rebar weight before corrosion and the final rebar weight after the corrosion, extraction and cleaning processes.\u003c/p\u003e"},{"header":"5 Results and Discussion","content":"\u003cp\u003eThe amplitude and TWTT of GPR waveforms were altered with multiple levels of corrosion. During the chloride contamination phase, the salt solution began seeping into the concrete and chloride ions accumulated around the rebars. This resulted in two simultaneous effects. First, ion buildup reduced the amplitude of the reflected waveforms. Second, the absorption of the GPR wave energy shortened the TWTT. The ions further affected the rebar passive layer, causing corrosion initiation. Corrosion products were distributed within the concrete pores as the corrosion progressed. As a result, the travel time from the direct wave to the anode bar was lengthened, resulting in increased amplitude. Stable ions in the anode are continually consumed, resulting in corrosion products that expand and cause micro-cracks within the concrete cover. Interfaces such as steel, corrosion product, concrete, fissures and outward movement of corrosion products result in broader footprints of the radar waveforms during the corrosion process. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows sample amplitude variation in NSC samples with a 50 mm cover. At the final stage, the GPR amplitude increased for all rebars with an increase in the duration of accelerated corrosion.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Influence of Parameters on Amplitude\u003c/h2\u003e \u003cp\u003eFigures\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e show change in maximum positive GPR amplitude with an increase in rebar diameter and concrete cover. The bigger surface area of 32 mm (#10) rebars meant more exposure to the corrosive environment and higher amplitudes than the smaller 22 mm and 10 mm (#3 and #7) rebars. As the rebar diameter increased, the amplitude values also increased.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the effect of changing concrete strength and porosity on the GPR amplitude. The amplitudes from LSC specimens were greater than the amplitudes from NSC specimens for all corrosion durations. LSC contains a high percentage of air, and the dielectric constant of air is lower than concrete. Air absorbs less radar radiation resulting in a higher amplitude for LSC samples.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows the effect of concrete cover on GPR amplitude. The amplitudes for 75 mm and 50 mm covers were smaller than that for 38 mm cover since the radar waves had to travel longer distances for increased cover depths, causing most of the radar energy to be absorbed. The greater the depth, the greater the loss of radar energy and the smaller the amplitude of the reflected wave.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Influence of Parameters on TWTT\u003c/h2\u003e \u003cp\u003eThe TWTT of reflected waveforms at different corrosion levels were plotted for varying rebar diameters, concrete strengths and concrete covers, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e and \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. The TWTT dropped during the saltwater contamination phase and increased with the progression of the corrosion period. This maybe attributed to increased corrosion within the concrete pores. Restoration of the original TWTT and increased value was noticed later as a result of concrete cover drying and corrosion product dispersion inside the concrete pores, suggesting an increase in the dielectric property of concrete. The TWTT, similar to amplitude, varies with rebar sizes for different concrete covers. Even though there was little change in the TWTT between different rebar diameters, there was a significant variance in TWTT at different cover depths because the wave traveled longer in concrete for increased cover. It is apparent that increased cover resulted in greater TWTT. Similar to the amplitude, the TWTT changes with concrete strength and porosity. The TWTT for LSC is much higher than that in the NSC samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Rebar Mass Loss\u003c/h2\u003e \u003cp\u003eParameters such as chloride ions, water content and current flow were kept constant throughout the accelerated corrosion process. Consequently, the rebar mass loss was directly related to the corrosion levels. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e, the percentage rebar mass loss was the highest for LSC specimens with the smallest rebar diameter and clear cover and subjected to the longest corrosion duration. The length of rebars extending outside the concrete specimens for making electrical connections was not considered in calculating rebar mass loss.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Quantitative Relationships\u003c/h2\u003e \u003cp\u003eA multivariant linear regression equation, Eq.\u0026nbsp;1, was generated through the python code [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] as a quantitative relationship between rebar corrosion mass loss and related parameters. Linear regression is a basic and extensively used type of predictive analysis that works with continuous data. Multivariant linear regression allows researchers to predict or explain criterion variable scores based on scores on two or more predictor factors and knowledge of the relationships between them. Our approach was to determine how much each predictor variable contributes to explaining the criterion variable by providing a regression or beta coefficient), which indicates the extent to which each predictor variable contributes to explaining the criterion variable scores while controlling for the other predictor variables [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe coefficient of determination, R\u003csup\u003e2\u003c/sup\u003e, was 0.84 as determined from the IBM SPSS Statistics [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] software, implying that the model\u0026rsquo;s inputs can explain around 84% of the observed variation.\u003c/p\u003e \u003cp\u003eC\u003csub\u003er\u003c/sub\u003e = 5.095\u0026thinsp;+\u0026thinsp;0.122 a \u0026ndash; 0.012 S \u0026minus;\u0026thinsp;0.183 d \u0026ndash; 1.208 t\u0026thinsp;+\u0026thinsp;0.016 z \u0026minus;\u0026thinsp;2.8*10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e A (1)\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003eC\u003csub\u003er\u003c/sub\u003e = Corrosion amount (%)\u003c/p\u003e \u003cp\u003ea\u0026thinsp;=\u0026thinsp;Corrosion period (years)\u003c/p\u003e \u003cp\u003eS\u0026thinsp;=\u0026thinsp;Concrete strength (MPa)\u003c/p\u003e \u003cp\u003ed\u0026thinsp;=\u0026thinsp;Initial (new) rebar diameter (mm)\u003c/p\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;Two-way travel time (ns)\u003c/p\u003e \u003cp\u003ez\u0026thinsp;=\u0026thinsp;Concrete cover (mm)\u003c/p\u003e \u003cp\u003eA\u0026thinsp;=\u0026thinsp;GPR amplitude\u003c/p\u003e \u003cp\u003eIn evaluating existing structures, the concrete strength, cover and rebar diameter may be found from as-built drawings and in-situ or laboratory testing. The GPR amplitude and TWTT can be obtained from the GPR scanning. In the absence of initial parameters for the equation input, normalization can be performed by back-calculating the GPR amplitude collected from a corrosion-free section from the same structure (where C\u003csub\u003er\u003c/sub\u003e = 0%), followed by inspection and exposing a portion of rebar. Based on this, the residual rebar area can be estimated by deducting the corrosion percentage from the uncorroded rebar area, which may be used to compute the remaining capacity of reinforced concrete structures. Knowing the residual capacity of a member can aid engineers and owners in making an informed decision to retrofit or replace building or bridge structural components.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Model Validation\u003c/h2\u003e \u003cp\u003eThe proposed model was validated by comparing with the corrosion rate of a rebar retrieved during the demolition of a 32-year-old bridge deck on IH-30 in Dallas, Texas (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). The compressive strength of the deck concrete was 20.7 MPa. The retrieved sample had 203 mm long and 12.7 mm (#4) in diameter corroded rebars embedded in 50 mm thick concrete. A GPR scan was conducted on the sample and data were obtained. The GPR scan waveform is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e. The post-processed GPR data from the sample resulted in an average value of 5,758 amplitude and 0.9 ns TWTT. Employing the proposed Eq.\u0026nbsp;1, a 6.14% corrosion rate was predicted for the rebar. The corroded rebar was extracted from the concrete sample, cleaned per ASTM G1-03 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and then weighed to determine the mass loss owing to natural corrosion. A mass loss of 6.3% was determined, considering the masses of the extracted rebar and uncorroded rebar with the same length and diameter. So, the corrosion rate from the proposed equation was around 0.16% lower than the actual value, which is an excellent match.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Limitations","content":"\u003cp\u003eAccelerated corrosion of steel rebars in concrete over a condensed period results in increased corrosion product concentrations around the rebars and diffusion of these products into the pores [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, the extent of this activity within the concrete cover is minimal. As a result, the properties of the concrete around the rebars remain unchanged. When natural corrosion occurs over a longer time, corrosion products infiltrate more deeply into the surrounding concrete pores, resulting in a significant variation in the concrete property. The current study considered concrete strength with assumed porosity as one of the degrees of freedom. However, measured porosity as a parameter would improve the accuracy of the proposed model. In addition, incorporating water content as another parameter could also enhance the model since the concrete moisture profile influences GPR readings.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe following conclusions may be made based on the findings from this study:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe present study successfully quantified the amount of steel rebar corrosion in concrete beams using GPR scanning. The research involved fabricating reinforced concrete beam specimens, exposing the beams to accelerated corrosion, scanning samples utilizing a 2.6 GHz frequency GPR, analyzing the data and developing a multivariant regression equation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLarger rebar sizes underwent more corrosion than smaller rebars due to the additional surface area and corrosion potential. As a result, the GPR reflected wave amplitudes also increased.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe reflected GPR amplitudes from Low Strength Concrete (LSC) specimens were greater than the amplitudes from Normal Strength Concrete (NSC) specimens for all corrosion durations. LSC has higher air content than NSC and the dielectric constant of air is lower than concrete. Air absorbs less radar radiation resulting in higher amplitudes for LSC.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLarger concrete covers for rebars resulted in smaller reflected GPR wave amplitudes. This is because the radar waves travel longer distances for larger cover depths, causing most of the radar energy to be absorbed by the concrete.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIt was found that the corrosion stages on the embedded rebars had a significant impact on the maximum amplitude and the Two Way Travel Time (TWTT) of the GPR signal. For a given cover depth, rebar diameter and concrete strength, the amplitude remained constant prior to corrosion commencement. The reflected amplitude decreased at the commencement of the accelerated corrosion but increased after a certain period. This may be attributed to increased corrosion within the concrete pores with time. Restoration of the original TWTT and increased value was noticed at latter stages due to concrete cover drying and corrosion product dispersion inside the concrete pores, suggesting an increase in the concrete dielectric property.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe available chloride ions, water content and current flow were kept constant throughout the accelerated corrosion process. Therefore, the rebar corrosion mass loss was directly related to the corrosion levels. The mass loss was highest in LSC samples with the smallest rebar diameter and cover, and under longest corrosion duration.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eA reliable multivariant linear regression model was developed to determine the corrosion amount in consideration of the parametric effects. A user-friendly step by step approach is presented for estimating or finding the various input parameters, such as concrete strength, concrete cover, rebar diameter, GPR amplitude, GPR TWTT.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe proposed regression model was validated by comparing with the actual corrosion rate of an embedd rebar retrieved during the demolition of an old concrete bridge deck in Dallas, TX. The model corrosion percent output was only 0.16% lower than the actual rebar mass loss, yielding a very high accuracy of 97.5%.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cp\u003e\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowlegement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was performed under a grant from the Texas Department of Transportation (TxDOT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;KBJ: Experimental work, Numerical modeling, manuscript drafting; NY: Study conceptualization, study coordination, manuscript review and updating; EB: Numerical modeling, Experimental work; \u0026nbsp;MMR: Experimental work, manuscript drafting.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors declare that they have no competing interests as defined by Springer or other interests that might be perceived to influence the results and or discussion reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEthics Approval and Concent to Participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for Publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHong S, Chen D, Dong B (2022) Numerical simulation and mechanism analysis of GPR-based reinforcement corrosion detection. Construction and Building Materials\u003cem\u003e \u003c/em\u003e317\u003cem\u003e. \u003c/em\u003e https://doi.org/10.1016/j.conbuildmat.2021.125913,\u003c/li\u003e\n\u003cli\u003eZaki A, Johari MAM, Hussin WMAW, Jusman Y (2018) Experimental Assessment of Rebar Corrosion in Concrete Slab Using Ground Penetrating Radar (GPR). International Journal of Corrosion. https://doi.org/10.1155/2018/5389829.\u003c/li\u003e\n\u003cli\u003eKoch G, Varney J, Thompson N, Moghissi O, Gould M, Payer J (2016) International measures of prevention, application, and economics of corrosion technologies study,\u0026rdquo; Report No. OAPUS310GKOCH (AP110272), NACE international Houston, USA.\u003c/li\u003e\n\u003cli\u003eHasan MI, Yazdani N (2016) An experimental study for quantitative estimation of rebar corrosion in concrete using ground penetrating radar. Journal of Engineering (United Kingdom). https://doi.org/10.1155/2016/8536850.\u003c/li\u003e\n\u003cli\u003eHong SX, Wiggenhauser H, Helmerich R, Dong BQ, Dong, Xing F (2017) Long-term monitoring of reinforcement corrosion in concrete using ground penetrating radar. Corrosion Science 114: 123\u0026ndash;132.. \u003cu\u003ehttps://doi.org/10.1016/j. corsci.2016.11.003.\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eNarayanan RM, Hudson SG, Kumke CJ, Beacham MW, Hall DD (1998) Detection of Rebar Corrosion in Bridge Decks using Statistical Variance of Radar Reflected Pulses. Seventh International Conference on Ground Penetrating Radar 601-605.\u003c/li\u003e\n\u003cli\u003eHubbard SS; Zhang J; Monteiro P.M; Peterson JE; Rubin Y (2003) Experimental Detection of Reinforcing Bar Corrosion Using Nondestructive Geophysical Techniques. ACI Materials Journal 100(6): 501-510.\u003c/li\u003e\n\u003cli\u003eLai WWL, Kind T, Wiggenhauser H (2011) Using ground penetrating radar and time-frequency analysis to characterize construction materials. NDT E International 44: 111\u0026ndash;120.\u003c/li\u003e\n\u003cli\u003eHong S, Lai WWL, Wilsch G, Helmerich R, Helmerich R, G\u0026uuml;nther T, Wiggenhauser H (2014) Periodic mapping of reinforcement corrosion in intrusive chloride contaminated concrete with GPR. Construction of Building Materials 66: 671\u0026ndash;684.\u003c/li\u003e\n\u003cli\u003eHong S, Lai WWL, Helmerich R (2015) Experimental monitoring of chloride-induced reinforcement corrosion and chloride contamination in concrete with ground-penetrating radar. Structure Infrastructucture Engineering 11: 15\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eLai WWL, Kind T, Stoppel M, Wiggenhauser H (2013) Measurement of Accelerated Steel Corrosion in Concrete Using Ground- Penetrating Radar and a Modified Half-Cell Potential Method. Journal of Infrastructure System 19: 205\u0026ndash;220.\u003c/li\u003e\n\u003cli\u003eZhan BJ, Lai WWL, Kou SC, Poon CS, Tsang WF (2011) Correlation between accelerated steel corrosion in concrete and ground penetrating radar parameters. In Proceedings of the International RILEM Conference on Advances in Construction Materials Through Science and Engineering, Hong Kong, China.\u003c/li\u003e\n\u003cli\u003eRaju RK, Hasan MI, Yazdani N (2018) Quantitative relationship involving reinforcing bar corrosion and ground-penetrating radar amplitude. ACI Materials Journals 115: 449\u0026ndash;457.\u003c/li\u003e\n\u003cli\u003eSossa V, P\u0026eacute;rez-Gracia V, Gonz\u0026aacute;lez-Drigo R, Rasol MA (2019) Lab nondestructive test to analyze the effect of corrosion on ground penetrating radar scans. Remote Sen. 11: 2814. \u003c/li\u003e\n\u003cli\u003eSenin SF, Hamid R, Ahmad J, Rosli MIF, Yusuff A, Rohim R, Abdul Ghani KD, Mohamed Noor S (2019) Damage detection of artificial corroded rebars and quantification using non-destructive methods on reinforced concrete structure. J. Phys. Conf. Ser. 1349(1): 012044. https://doi.org/10.1088/1742-6596/ 1349/1/012044.\u003c/li\u003e\n\u003cli\u003eAhmad S (2009) Techniques for inducing accelerated corrosion of steel in concrete. Arabian Journal for Science and Engineering 34(2C): 95\u0026ndash;104.\u003c/li\u003e\n\u003cli\u003eAhmed SF, Maalej M, Paramasivam P, Mihashi H (2006) Assesment of Corrosion-Induced Damage and its Effect on the Structural Behavior of RC Beams Containing Supplementary Cementitious Materials. Progress in Structural Engineering and Materials 8(2): 69\u0026ndash;77.\u003c/li\u003e\n\u003cli\u003eHa TH, Muralidharan S, Bae JH, Ha YC, Lee HG, Park KW, Kim DK (2007) Accelerated Short-Term Techniques to Evaluate the Corrosion Performance of Steel in Fly Ash Blended Concrete. Building and Environment 42: 78\u0026ndash;85.\u003c/li\u003e\n\u003cli\u003eAlmusallam A (2001) Effect of degree of corrosion on the properties of reinforcing steel bars. Construction and Building Materials 15(8):, 361\u0026ndash;368.\u003c/li\u003e\n\u003cli\u003eShahar N, Renan S, Speyer E, Gueta T, Alan R, David S (2012) A factorial design experiment as a pilot study for noninvasive genetic sampling. Molecular Ecology Resources. https://doi.org/10.1111/j.1755-0998.2012.03170. \u003c/li\u003e\n\u003cli\u003eMinitab statistical software (1998). \u003c/li\u003e\n\u003cli\u003eACI Committee 318 (2018) Building Code Requirements for Structural Concrete: (ACI 318-19); and Commentary (ACI 318R-19). Farmington Hills, MI: American Concrete Institute.\u003c/li\u003e\n\u003cli\u003eASTM A615/A615M-15a. (2015) Standard specification for deformed and plain carbon-steel bars for concrete reinforcement.\u003c/li\u003e\n\u003cli\u003eAbouhussien AA, Hassan AAA (2014) Experimental and empirical time to corrosion of reinforced concrete structures under different curing conditions.Advances in Civil Engineering. https://doi.org/10.1155/2014/595743.\u003c/li\u003e\n\u003cli\u003eEl Maaddawy T, Soudki K (2003) Effectiveness of impressed current technique to simulate corrosion of steel reinforcement in concrete. Journal of Materials in Civil Engineering 15(1): 41\u0026ndash;47.\u003c/li\u003e\n\u003cli\u003eBroomfield JP (1997) Corrosion of steel in concrete: Understanding, investigation, and repair. London\u003c/li\u003e\n\u003cli\u003eMalumbela G, Moyo P, Alexander M (2012) A step towards standardising accelerated corrosion tests on laboratory reinforced concrete specimens. J. South African Inst. Civ. Eng. 54: 78\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eGPR-SLICE (2021) Ground Penetrating Radar Imaging Software, Version 7 MT, Geophysical Archaeometry Laboratory Inc.\u003c/li\u003e\n\u003cli\u003eASTM G1-03 (2011) Standard Practice for Preparing, Cleaning, and Evaluating Corrosion Test Specimens, ASTM International, West Conshohocken, PA.\u003c/li\u003e\n\u003cli\u003ePython Software Foundation. (2021) Python Language Reference, version 3.10. Available at http://www.python.org. \u003c/li\u003e\n\u003cli\u003eIBM Corp. (2020) IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY: IBM Corp.\u003c/li\u003e\n\u003cli\u003eASTM C-31 (2019) Standard Practice for Making and Curing Concrete Test Specimens in the Field, ASTM International, West Conshohocken, PA.\u003c/li\u003e\n\u003cli\u003eSethuraman VS, Suguna K (2016) Regression based analysis and visualization of for identifying Flexural Behaviour of M60 Beams under repeated Compressive Load based on observational data sets. Procedia Computer Science 87: 264 \u0026ndash; 269..\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Accelerated Corrosion, Amplitude, Ground Penetrating Radar, Multivariant Regression Model, Two-way Travel Time","lastPublishedDoi":"10.21203/rs.3.rs-4366218/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4366218/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReinforced concrete structures have been extensively investigated using Ground Penetrating Radar (GPR) for qualitatively analyzing corrosion-induced degradation. However, no reliable quantitative model exists for the corrosion evaluation of the embedded steel rebars. Such corrosion may significantly impact the strength, serviceability and long-term durability of concrete structures. This study involved an experimental work to determine the relationship between the rebar corrosion quantity and the maximum amplitude as well as the two-way travel time (TWTT) of the GPR electromagnetic wave. A direct current was impressed into embedded steel rebars in concrete beams immersed in a 5% saltwater solution to induce accelerated corrosion. GRP data were collected before saline submersion and at 10-day intervals. A multivariate regression equation with high reliability was developed to estimate corrosion-indued rebar mass loss with independent variables, including concrete cover, rebar diameter, age of the reinforced concrete, concrete strength and GPR scanning parameters.\u003c/p\u003e","manuscriptTitle":"Experimental Detection of Embedded Rebar Corrosion in Concrete with Ground Penetrating Radar","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-09 12:27:57","doi":"10.21203/rs.3.rs-4366218/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ccfc6df6-39b6-4cda-bde8-0cf3d424d1e9","owner":[],"postedDate":"May 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-06T02:24:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-09 12:27:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4366218","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4366218","identity":"rs-4366218","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}