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23 June 2025, Volume 44 Issue 6 Previous Issue   
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Highway & Railway Engineering
Transportation+Big Data & Artificial Intelligence
Bridge and Tunnel Engineering


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Highway & Railway Engineering

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A Review of Asphalt Pavement Uniformity Evaluation Based on Digital Image Processing QIN Min1，DOU Lihong1，XU Diping2，WU Zhou2 2025, 44(6): 1-14.  DOI: 10.3969/j.issn.1674-0696.2025.06.01 Abstract ( )   PDF (2791KB) ( )   Asphalt pavement uniformity is an important factor affecting its road performance and service life. In order to evaluate the uniformity of asphalt pavement quickly, accurately and efficiently, the principle of digital image processing technology, system composition and the influencing factors of image acquisition were described, the asphalt pavement uniformity evaluation methods based on digital image processing technology at home and abroad were analyzed, and the future development direction were looked forward to, including the optimization of image processing algorithms, the enhancement of the efficiency of data processing and the wider application of deep learning technology. The research results show that the digital image processing process focuses on image enhancement and image segmentation, and its accuracy and applicability need to be further improved. The acquisition of digital images is affected by factors such as the acquisition height, light intensity and shooting angle. The existing evaluation indexes for the uniformity of the pavement are mainly from the perspectives of the aggregate distribution state and the texture structure of the pavement to establish the corresponding evaluation methods, but the unified standard has not yet been formed. Comprehensive use of digital image processing technology can provide new perspectives and solutions for the evaluation of asphalt pavement uniformity, and powerful support for real-time monitoring of road engineering quality and construction guidance. References | Related Articles | Metrics

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Damage Evaluation of Subgrade-Asphalt Pavement Based on QFD-AR WU Xuelian1, SHI Xiaoli1, HUANG Yue2, GONG Xiaotian1 2025, 44(6): 15-25.  DOI: 10.3969/j.issn.1674-0696.2025.06.02 Abstract ( )   PDF (1333KB) ( )   The evaluation of highway technical conditions is the basic way to understand the performance of highways, and it is also an important basis for performance evaluation of highway maintenance management units and contractors. However, the current evaluation standards for highway technology status lack sufficient consideration of the mutual influence of each type of damage, and the scoring of some indicators exhibits excessive subjectivity. To address these issues, a comprehensive damage evaluation model for the subgrade-asphalt pavement system based on quality function deployment (QFD) and association rules (AR) was proposed. Firstly, Pearson correlation coefficient was used to analyze the correlation between the quantities of various types of damage in the subgrade-asphalt pavement system, clarifying the mutual influence of each type of damage. Subsequently, an evaluation indicator system for the overall damage status of the subgrade-pavement was established, consisting of 6 categories and 29 indicators. By deploying quality functions to systematically consider the subgrade and pavement and embedding AR theory into the QFD model at the same time, an evaluation method for the damage status of subgrade-asphalt pavement was designed by a combination of subjective and objective weighting methods. Finally, based on routine maintenance, major and medium repair maintenance data and highway inspection data of a highway group from 2020 to 2022, the evaluation results of the QFD -AR model were compared with the subgrade condition index (SCI) and pavement condition index (PCI). Sensitivity analysis of the model parameters (damage level standard values) was also carried out. The results indicate that the QFD -AR model is affected by the standard value of damage level, and the degree of impact is related to the proportion of damage caused by that level. The distribution range of QFD -AR model evaluation results is relatively large, which is conducive to distinguishing the damage level of subgrade-asphalt pavement of different regions or companies. The research findings can be applied to the evaluation of the overall damage status of the subgrade-asphalt pavement system in highway networks, providing new ideas for highway technical condition evaluation and offering support for highway maintenance management units to carry out the evaluation and do comprehensive maintenance decision-making. References | Related Articles | Metrics

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Coarse Aggregate Angularity and Its Influence on the Performance of Asphalt Mixture YU Hao1, YANG Jie2, HE Zhaoyi2, QIN Min3, YANG Zhiqiang2 2025, 44(6): 26-34.  DOI: 10.3969/j.issn.1674-0696.2025.06.03 Abstract ( )   PDF (3174KB) ( )   In order to evaluate the angularity of coarse aggregate and study its influence on the performance of the mixture, Image-pro plus digital image processing software was used to analyze the rationality of the number of aggregate samples and the placement surface. The angularity of coarse aggregate with different material sources and different processing techniques was evaluated, and on this basis, the road performance test of asphalt mixture was carried out. The results show that the angularity of coarse aggregate is closely related to the processing characteristics and material source characteristics of stone. High-strength stone can better resist crushing and wear and maintain good angularity. The multi-stage crushing process can increase the crushing surfaces of the aggregate particles and form more edges, thereby improving the angularity of the coarse aggregate. The angularity of coarse aggregate has a significant effect on the performance of AC-20 asphalt mixture. With the increase of the average angular parameters from 1.076 4 to 1.082 5,1.083 2 and 1.129 1, the dynamic stability of the mixture is increased by 12.9%, 15.8% and 55.6% respectively, and the bending strain is increased by 6.3%, 8.5% and 17.5% respectively. Improving the angularity of coarse aggregates is beneficial for enhancing the high-temperature and low-temperature performance of the mixture. However, under the same compaction work, aggregates with strong angular properties are more difficult to compact, resulting in the increase of voids and reduction of water stability of the mixture. Therefore, for asphalt mixtures with high angularity of coarse aggregates, the compaction process should be optimized during construction to achieve good compaction. References | Related Articles | Metrics

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Road surface texture is a key factor affecting the skid resistance performance. To further study its influence mechanism and to solve the problem of limited accuracy of traditional prediction methods under multi-feature data conditions, a pavement skid resistance performance assessment model based on the fusion of Bayesian optimization algorithm (BOA) and extreme gradient boosting (XGBoost) was proposed. Firstly, specimens phalt mixtures of different gradation types were prepared. Friction data and 3D texture data of the specimen surface were respectively obtained by using a pendulum friction meter and a 3D laser scanning device. Secondly, the height, the wavelength and shape parameters were extracted to describe the texture structure, and the texture feature importance analysis was carried out to clarify the factors that significantly affected the skid resistance. Then, the optimal key parameters were improved by introducing of the model BOA-XGBoost and the prediction model of skid resistance performance was established. The research results show that compared with the comparative models, the proposed model has higher accuracy, with a correlation coefficient R2 of 0.890 6, which is 25.2%, 13.0%, and 15.1% higher than those of the comparative models, respectively. The proposed model can effectively correlate texture features with pavement skid resistance performance. XU Xinquan1, HU Yuanjiao2, WENG Yuhan3, HE Weijie1 2025, 44(6): 35-44.  DOI: 10.3969/j.issn.1674-0696.2025.06.04 Abstract ( )   PDF (3718KB) ( )   Road surface texture is a key factor affecting the skid resistance performance. To further study its influence mechanism and to solve the problem of limited accuracy of traditional prediction methods under multi-feature data conditions, a pavement skid resistance performance assessment model based on the fusion of Bayesian optimization algorithm (BOA) and extreme gradient boosting (XGBoost) was proposed. Firstly, specimens phalt mixtures of different gradation types were prepared. Friction data and 3D texture data of the specimen surface were respectively obtained by using a pendulum friction meter and a 3D laser scanning device. Secondly, the height, the wavelength and shape parameters were extracted to describe the texture structure, and the texture feature importance analysis was carried out to clarify the factors that significantly affected the skid resistance. Then, the optimal key parameters were improved by introducing of the model BOA-XGBoost and the prediction model of skid resistance performance was established. The research results show that compared with the comparative models, the proposed model has higher accuracy, with a correlation coefficient R2 of 0.890 6, which is 25.2%, 13.0%, and 15.1% higher than those of the comparative models, respectively. The proposed model can effectively correlate texture features with pavement skid resistance performance. References | Related Articles | Metrics

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Numerical Simulation of the Working Performance of Embedded Geosynthetic-Reinforced Soil Abutment ZUO Binli1, XU Chao1,2, WANG Qingming1, XU Xinyi1, DU Chunxue1 2025, 44(6): 45-52.  DOI: 10.3969/j.issn.1674-0696.2025.06.05 Abstract ( )   PDF (4354KB) ( )   The embedded geosynthetic-reinforced soil abutment is one type of non-load-bearing abutments and has good economic benefits in engineering. However, the presence of piles can change the internal stress characteristics of the geosynthetic-reinforced soil abutment, thereby affecting its working performance. Based on a prototype of an embedded geosynthetic-reinforced soil abutment project and its monitoring results, a 1∶1 three-dimensional numerical model was established using FLAC3D to analyze the stress and deformation performance of the embedded bridge abutment under working conditions. Guided by design requirements, the effects of horizontal clearance between piles and abutment facing, pile diameter, and reinforcement around pile method on the working performance of the embedded geosynthetic-reinforced soil abutment were studied. The research results show that there is a significant interaction between the pile (column abutment) and the geosynthetic-reinforced soil abutment, and there is negative frictional resistance on the surface of the pile. At the same time, the pile acts as a lateral barrier to the reinforced soil, thereby reducing the horizontal soil pressure acting on the back of the facing. The reduction of the horizontal clearance of the pile will decrease the range of small stress zones in front of the pile and increase the lateral displacement of the abutment facing. Increasing the pile diameter will increase the bearing width of the pile, reduce the lateral displacement of the pile, and enhance the ability of the pile to resist lateral soil pressure. The use of reinforcement around pile method with rigid casing can improve the integrity of geosynthetic-reinforced soil abutment and reduce lateral displacement of the abutment. References | Related Articles | Metrics

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Microwave Warming Performance of Ferrite-Silicon Carbide Asphalt Mixture WANG Lijun, FANG Qingtian 2025, 44(6): 53-63.  DOI: 10.3969/j.issn.1674-0696.2025.06.06 Abstract ( )   PDF (5405KB) ( )   Asphalt undergoes rheological behavior at high temperatures, which enables it to fill cracks in the mixture to achieve self-healing. The use of microwave-induced heating will accelerate its self-healing process; however, asphalt mixtures have slow heating rates and uneven temperature distribution under microwave action. Therefore, ferrite and silicon carbide powder were added into asphalt according to a certain proportion, and research on the effect of microwave on the heating law of the mixture was carried out by numerical simulation and indoor test respectively. The research results show that ferrite and silicon carbide affect both the heating rate and temperature uniformity of the mixture. When the mixing ratio of ferrite: silicon carbide is 6∶4, the asphalt mixture has the best warming performance, and the warming rate is 0.6 ℃/s. Under the action of microwave, a temperature concentration zone will appear at the center of the specimen, with a temperature of 173 ℃ and a maximum temperature difference of 103 ℃ between the inside and outside. The mixture undergoes heat conduction during microwave heating, and its heat transfer rate is inversely proportional to the temperature difference, with an average rate of 0.95 ℃/s. References | Related Articles | Metrics

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Simulation Study on Construction Progress Risks of Subway “Tunnel-Four Electricity” Technology Interface YAN Linjun1，LIU Jingjing1，LI Fei2，WANG Yani1，CHEN Huixin1 2025, 44(6): 64-72.  DOI: 10.3969/j.issn.1674-0696.2025.06.07 Abstract ( )   PDF (4538KB) ( )   In order to realize the optimal control of the construction progress of the subway tunnel project, a construction progress simulation model based on the coupling optimization-related progress risk analysis model (CSRAM) and Bayesian belief networks (BBNs) was built for the uncertain risks in the construction process of the subway “tunnel-four electricity” technical interface. Firstly, the risk factors affecting the construction schedule were identified, and BBNs were used to quantify the non-overlapping effects of the risk factors on the construction schedule of the technical interface activities, and the probability boundary of risk factor performance was calculated out. Secondly, the hierarchical-entropy weight method was used to optimize the traditional CSRAM, with the consideration of the interrelationships between risk factors, the interrelationships between technical interface task activities, and the interrelationships between risk factors and technical interface task activities. Finally, the coupled optimization CSRAM and BBNs models were constructed. The northern section of Beijing Metro Line 17 was taken as an example to carry out simulation analysis by Monte Carlo simulation method. The completion probability of the construction progress was calculated, and sensitivity analysis on the risk factors of subway “tunnel-four electricity” technical interface was carried out by adjusting the probability boundary of risk factors. The results show that the completion period of subway “tunnel-four electricity” technical interface under the influence of risk factors is 576 days, and the completion probability on schedule is 83.43%. Insufficient technical management of the interface process and poor communication between interface participants are the most sensitive risk factors affecting the construction progress of the technical interface. The research results verify the applicability of the simulation model of the construction process of the subway “tunnel-four electricity” technical interface, which can provide a theoretical basis for the construction progress control and risk management of subway “tunnel-four electricity” technical interface. References | Related Articles | Metrics

Transportation+Big Data & Artificial Intelligence

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Bus Service Satisfaction Evaluation Based on Bounded Rationality ZHANG Bing1, XING Yu1, HU Qizhou2, Xue Yunqiang1 2025, 44(6): 73-81.  DOI: 10.3969/j.issn.1674-0696.2025.06.08 Abstract ( )   PDF (918KB) ( )   In order to truly reflect the level of bus service, improve service quality and enhance passenger satisfaction, the applicable conditions of passenger satisfaction research were analyzed from the perspective of bounded rationality, and the necessity of considering passenger bounded rationality factors in satisfaction research was explained, according to the main deficiencies of current passenger satisfaction research. On the basis of the conventional satisfaction evaluation model, the latent variables “passenger cognition” and “attitude preference” reflecting passengers' bounded rationality factors were added, and a partial least squares structural equation measurement model of “bus service satisfaction based on bounded rationality” was established. Subsequently, the questionnaire data of bus passengers in Nanchang was used to evaluate the satisfaction level, and the satisfaction scores were corrected according to the bounded rationality factors. Finally, a grouping study was conducted according to passenger characteristics, and the importance-performance analysis (IPA) method was used to obtain improvement strategies for different groups. The research results show that both passenger perception and attitude preference have a significant effect on satisfaction, indicating that the bounded rationality factors of passengers will affect the evaluation results of satisfaction. Meanwhile, perceived value and perceived quality are also important factors affecting satisfaction. The passenger satisfaction score after calcalation revision is 68.0%,indicating a low overall satisfaction rate. Priority should be given to improving information services, convenience, safety, social security, and other aspects to enhance satisfaction with public transportation services. References | Related Articles | Metrics

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Routing Optimization Strategy of Time-Dependent Simultaneous Pickup and Delivery Vehicle CHEN Shijun1, LUO Wei2,3, WU Huawei2,3, XIA Liangcai1, WANG Hongyu1 2025, 44(6): 82-96.  DOI: 10.3969/j.issn.1674-0696.2025.06.09 Abstract ( )   PDF (1896KB) ( )   The study on the time-dependent vehicle routing problem with simultaneous delivery and pickup (TDVRPSDP) has two defects, that is, using “step time-varying speed” that deviates from reality and ignoring the impact of vehicle speed on energy consumption. To address these issues, a mathematical model considering continuous time-varying speeds and impact of load on fuel consumption was established, aiming to minimize total costs including vehicle usage, fuel consumption and carbon emissions, and a hybrid artificial bee colony (HABC) algorithm was proposed for solution. The proposed algorithm used an improved nearest-neighbor method for generating high-quality initial honey sources, designed multiple adaptive large neighborhood search operators to replace random search mechanisms in standard artificial bee colony (ABC) algorithm, added an inferior solution acceptance criterion, and adopted a series of optimization strategies to enhance search capability. The effectiveness of the proposed algorithm was verified by multiple computational experiments and case studies. The research results show that for test case of TDVRPSDP sub-problems, HABC algorithm outperforms comparative algorithms. For TDVRPSDP test case, the proposed algorithm reduces average distribution costs by 18.3% and 1.7% respectively compared with standard ABC algorithm and ABC algorithm combined with large neighborhood search. In practical case solving, HABC has also demonstrated strong optimization ability and convergence speed, which can effectively reduce distribution costs for enterprises. References | Related Articles | Metrics

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Scheduling Optimization for Sea-Rail Transport Equipment Based on Improved Immune Genetic Algorithm HUANG Pengfei1,2, TAN Fangjiao1, WANG Hao1, JIANG Yuyue1, CAI Jinfen1 2025, 44(6): 97-107.  DOI: 10.3969/j.issn.1674-0696.2025.06.10 Abstract ( )   PDF (1130KB) ( )   Container transshipment, as a critical link connecting maritime and railway transportation, whose efficiency directly influences the smooth operation of the whole logistics chain. Shortening container dwell time at ports, optimizing equipment operation sequences and improving transshipment efficiency are essential for achieving efficient sea-rail intermodal transportation. However, the existing studies often overlook the consideration of the complete container transshipment process and the impact of equipment idle time. In response, aiming at the whole process from ship unloading to storage yard and then to railway line, a mathematical model was constructed with the objective function of minimizing the total operation completion time, to address practical issues such as continuous operation constraints, no-load waiting time and specific operation position. The improved immune genetic algorithm (especially the clonal antibody selection mechanism and adaptive parameter adjustment strategy) was used to solve the problem. After a series of optimization and comparison, it is demonstrated that the proposed method can more effectively find the optimal or near-optimal solutions, achieving the shortest total operational completion time and the corresponding equipment scheduling plans. The research results not only help to significantly reduce the processing cycle of containers in ports, but also promote energy conservation and emission reduction. References | Related Articles | Metrics

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Diagnosis of Severity of Hazardous Chemical Transportation Accidents in Inland Waterways Based on Credal Network Model CHENG Zhiyou1，ZHU Cheng2，WU Rui2 2025, 44(6): 108-114.  DOI: 10.3969/j.issn.1674-0696.2025.06.11 Abstract ( )   PDF (931KB) ( )   To address the problems of data scarcity, cognitive uncertainty and expert subjectivity in the study of inland waterways hazardous chemical transportation accidents, a Credal network model was proposed to analyze and diagnose the severity of inland waterways hazardous chemical transportation accidents, with a view to preventing the risk of accidents from the perspective of safety management. Accident case reports and fault tree analysis were used to identify the risk factors of inland waterways hazardous chemical transportation accidents, and Credal network model based on the parameter learning method of imprecise Dirichlet model (IDM) was constructed to diagnose the severity of inland waterways hazardous chemical transportation accidents. Then, the probability of accidents of different severity levels was predicted through forward reasoning, and the main influencing factors to cause such incidents were identified through diagnostic reasoning. Finally, corresponding safety management measures were proposed according to the results of diagnostic reasoning. The results show that the interval probabilities of accidents occurring in the transportation of hazardous chemicals in inland waterways, categorized as general or lower, moderate, and major or higher, are ［0.711, 0.789］, ［0.172, 0.246］ and ［0.043, 0.069］, respectively. Human factors and management factors are the main factors to cause accidents in the transportation of hazardous chemicals in inland waterways, vessel factors and cargo factors are significant factors exacerbating the severity of these accidents. References | Related Articles | Metrics

Bridge and Tunnel Engineering

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Pier Crack Detection Based on Modular Wall-Climbing Robots and Improved DeepLabv3+ DONG Shaojiang，YIN Yuzhu，LYU Zhenming，ZHANG Jiawei 2025, 44(6): 115-122.  DOI: 10.3969/j.issn.1674-0696.2025.06.12 Abstract ( )   PDF (6379KB) ( )   Aiming at the problems such as poor continuity of cracks on the surface of large-scale concrete bridge piers, large background interference, and large number of parameters of general deep learning crack detection model, a detection scheme combined with the improved lightweight DeepLabv3+ crack segmentation model and modular wall-climbing robot was proposed to achieve safe, fast and accurate detection of wall crack. The modular wall-climbing robot was used as the carrier to realize the crawling drive in complex environment through the self-group connection of each module, and the image acquisition equipment was equipped to collect the data of pier apparent disease. Meanwhile, based on DeepLabv3+ framework, a lightweight detection model of aggregation of multi-scale information was constructed by improving part of the network structure and adding various detection modules, and was deployed to the upper computer system. The final test results show that the average detection accuracy of the proposed model on the Crack-wall crack dataset reaches 86.96%, an improvement of 6.26% compared to the original model, an increase of 8.44% in intersection to union ratio, an increase of 8.76% in recall rate, and a model size of only 10.613M, with high detection accuracy and real-time detection effect. At the same time, the proposed detection scheme is feasible and successfully applied to the actual project. References | Related Articles | Metrics

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Deformation Monitoring of Bridge Anti-ship-collision Equipment Based on Nap-of-the-Object Photogrammetry CHEN Zhengfei1,2，CHEN Hengchi1,2，CHEN Bin1,2 2025, 44(6): 123-130.  DOI: 10.3969/j.issn.1674-0696.2025.06.13 Abstract ( )   PDF (5960KB) ( )   Bridge anti-ship-collision equipment can effectively reduce the incidence of ship collision accidents, mitigate the consequences of collision accidents, and ensure the safety of bridges, people and property. In response to the deformation monitoring needs of bridge anti-ship-collision equipment, a deformation monitoring method of bridge anti-ship-collision equipment based on nap-of-the-object photogrammetry reconstructed point cloud was proposed. By using UAV to collect images and combining with structure from motion-multi-view stereo vision (SfM-MVS) algorithm, the three-dimensional point cloud model reconstruction of the ship collision prevention equipment was realized. The cloud to cloud (C2C) algorithm was used to detect changes in 3D point cloud data. The three-dimensional point cloud data of anti-ship-collision equipment at different time or scenarios were compared and analyzed, realizing automatic detection of deformation and damage of anti-ship-collision equipment. The proposed monitoring method was able to identify structural deformation above 0.4 cm. The research results show that the deformation monitoring method of bridge anti-collision devices based on nap-of-the-object photogrammetry is suitable for the automated detection of bridge anti-ship-collision equipment and has high practical value. References | Related Articles | Metrics

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Multi-step Bridge Temperature Prediction Based on ANN-KF-BiLSTM YAN Wenjia1，JIANG Ou1，LI Hongxian2，XU Jialu3 2025, 44(6): 131-138.  DOI: 10.3969/j.issn.1674-0696.2025.06.14 Abstract ( )   PDF (1150KB) ( )   An ANN-KF-BiLSTM model that could both utilize meteorological temperature and memorize the time series of the bridge itself temperature was developed by utilizing the artificial neural network (ANN) with great feature learning ability and the bidirectional long short-term memory network (Bi-LSTM) with strong time series learning ability in deep learning, while the Kalman filter (KF) was supplemented to adjust the output of ANN dynamically, which was based on the stacking ensemble strategy, combining ANN and BiLSTM neural networks. The effectiveness of the proposed method was verified by taking the temperature prediction of a continuous rigid bridge in Yunnan Province as an example. The research results show that the ANN-KF-BiLSTM model has obvious advantages in the multi-step prediction of bridge temperature, with a fitting degree of more than 0.89 when the number of prediction time steps is less than 96, and when the number of prediction steps reaches 168, the average fitting degree can still reach nearly 0.76. Compared with the benchmark model, the fitting degree of the proposed model is higher, and the model prediction stability is better. The proposed model improves the current situation that using deep learning models to predict bridge temperature concentrates on a single step prediction, providing an effective method for multi-step prediction of bridge temperature. References | Related Articles | Metrics

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Calculation Method of Flexural-Torsional Coupling Capacity of Composite Box Girder with Corrugated Steel Webs ZHANG Hao1，ZHAO Qiu1，TONG Zhaojie2，DONG Jucan3，CHEN Yiyan1 2025, 44(6): 139-144.  DOI: 10.3969/j.issn.1674-0696.2025.06.15 Abstract ( )   PDF (1020KB) ( )   According to the structural characteristics of the corrugated steel web (CSW), a calculation method of flexural-torsional coupling capacity of composite box girder with CSW was established, which took into account the interaction relationship between bending moment and torque. In order to verify the accuracy and applicability of the proposed calculation method of flexural-torsional coupling, the parameterized analysis was conducted using experimentally validated numerical analysis methods and compared with the calculation results. The results indicate that the torque-twist curve and bending moment-displacement curve obtained by the numerical analysis method are in good agreement with the test results. Under the flexural-torsional coupling action, there is a mutual weakening effect between the ultimate bending moment and torque of the composite box girder with CSW. The results of flexural-torsional coupling capacity of composite box girders with CSW obtained through existing specifications deviate significantly from the parameterized analysis results and are considered to be unsafe. The proposed calculation method of flexural-torsional coupling capacity takes into account the interaction between flexural-torsional coupling and can accurately predict the bearing capacity of composite box girder with CSW under the action of flexural-torsional coupling. References | Related Articles | Metrics