Journal of Chongqing Jiaotong University(Natural Science)

Mechanical Performance Analysis and Evaluation of Suspension Footbridge with Deck Made by Innovative Materials

ZHANG Yanling1, LI Yifan1, CHENG Yi1, CUI Chunlei2

2025, 44(9): 1-7. DOI: 10.3969/j.issn.1674-0696.2025.09.01

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The application of innovative materials will cause changes in the mechanical properties of footbridges compared to those using traditional materials such as concrete or steel. Taking a suspension footbridge as the engineering background, the finite element model was established by Midas/Civil. According to different comparative working conditions, the original glass bridge deck of the bridge was replaced with CFRP, GFRP and composite concrete bridge decks, respectively. Comparative analysis and comprehensive evaluation of the static performance and natural vibration characteristics of local and overall bridge, human-induced vibration response and pedestrian comfort were conducted through finite element analysis. The results show that for both the local deck system and the overall bridge, using CFRP deck has the optimal static effect, and the vertical and lateral frequencies of each order are also the highest. When a single person generates excitation in the mid span, the lateral and vertical acceleration extremes are minimized, and pedestrian comfort is highest when using CFRP deck. CFRP deck can achieve maximum contribution to bridge stiffness with minimal mass, resulting in optimal performance, followed by GFRP deck. However, considering economic factors, GFRP deck has better applicability.

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Channel Missing Data Recovery of Bridge Monitoring System Based on Improved GRU

CHANG Jun, ZHONG Ziting, LIU Chenguang

2025, 44(9): 10-18. DOI: 10.3969/j.issn.1674-0696.2025.09.02

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In bridge health monitoring system, the data loss caused by sensor failures and external environmental interference seriously affects the reliability of the monitoring system. At present, data recovery methods focus on the problem of recovering part of the missing data in the channel, while paying less attention to the problem of recovering missing data in the entire channel. Therefore, the GRU neural network was improved to recover the missing data of the entire channel and improve the data recovery accuracy. Firstly, an improved GRU model was constructed, which was based on the denoising autoencoding model and used gated recurrent neural network to replace the fully connected layer to learn the spatiotemporal correlation between different data. Meanwhile, attention mechanism and mask mechanism were added to enhance the attention to the missing position and improve the accuracy of data recovery. Secondly, the training data was constructed to train model, and the continuous missing data of each channel was artificially constructed as the input of the model, and the corresponding complete data was used as the output, so as to improve the learning ability of the model on the missing position mechanism. Finally, the missing data of the entire channel was recovered, the effect of data recovery was evaluated by evaluation indexes, and the modal analysis was carried out. The accuracy of the proposed method was verified by numerical simulation and monitoring data of real bridges. The application results of the real bridge show that compared with the existing models, the data recovery accuracy of the proposed method is improved, the mean absolute error is reduced by 21.8%, the root mean square error is reduced by 42.7%, and the model fitting ability is improved by 9.1%.

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Measurement Method of Stay Cable Length Based on Point Cloud Model

XIE Xiaowang1, YANG Jie1, LIU Mingdong1, WU Yue1, ZHU Yanjie2

2025, 44(9): 19-25. DOI: 10.3969/j.issn.1674-0696.2025.09.03

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Advances in 3D laser scanning and point cloud processing technologies have promoted the application of point cloud-based structural dimension measurement in bridge engineering. To efficiently obtain the length of stay cables for supporting inspection and maintenance, a measurement method based on point cloud model was developed. The proposed method acquired a 3D point cloud model of the stay cables through high-precision 3D laser scanning, enabling automatic identification and precise calculation of the cable length. The point cloud model was discretized into multiple segments, and the central coordinates of each segment were calculated and used as the cable alignment coordinates. Combined with on-site tape measurements and design data, the total length of the stay cable was calculated out. Finally, tests were conducted on the Yiling Yangtze River Bridge. The research results indicate that the cross-sectional errors of the stay cables are all less than 1%. The proposed measurement method can quickly and accurately obtain the cable length, providing support for bridge health monitoring.

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Manufacturing Quality Assessment of Prefabricated Steel Box Girders for Bridges Based on 3D Point Cloud

WU Zhigang1, XU Chang2, YIN Liang1, CHE Ping3, XIONG Wen2

2025, 44(9): 26-33. DOI: 10.3969/j.issn.1674-0696.2025.09.04

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Compared with concrete structures, steel structures offer advantages such as high strength, short construction periods, and low-carbon environmental benefits, making them increasingly widely used in infrastructure projects such as large bridges. To ensure structural performance, it is essential to accurately assess the manufacturing quality of steel components prior to installation. However, at present, such detection still heavily relies on manual operation, with problems such as large site occupation and low detection efficiency. To address this, 3D laser scanning technology was employed, and the detection and evaluation study of prefabricated component manufacturing quality was carried out based on high-precision 3D point cloud models. Firstly, aiming at the complex environment of prefabricated beam yards, optimization principles for 3D laser scanning measurement station positioning and on-site implementation methods were proposed. Subsequently, preprocessing of the collected point cloud data was carried out, including registration, denoising and down-sampling, so as to improve data quality and processing efficiency. Finally, automatic inspection and evaluation methods for spatial geometric dimensions and surface flatness were proposed based on algorithms including Alpha Shape, random sample consensus (RANSAC), and principal component analysis (PCA). The proposed method has been successfully applied on the standard steel box girder in the Shenzhen-Zhongshan Bridge project. The results demonstrate that 3D laser scanning and 3D point cloud model can provide strong support for the manufacturing quality assessment of prefabricated components. The proposed detection method has advantages such as good automation, high efficiency, and visualized results, which can offer effective assistance to inspectors to carry out manufacturing quality control and management for prefabricated components.

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Experiment and SPH-FEM Simulation of Dangerous Rock Impact on River Sand Cushion Layer at Tunnel Entrance

WANG Xing1, 2, LAI Xianjie1, 2, HUANG Shuai1, 2, NIE Yawei1, 2, HU Zhaoxia3

2025, 44(9): 34-41. DOI: 10.3969/j.issn.1674-0696.2025.09.05

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A design scheme for river sand cushion layer shed tunnel was proposed considering the concept of buffer travel. Based on the hazardous rock and rockfall disaster at the entrance of a certain tunnel project, an indoor test model was constructed, and a shed tunnel calculation model using the SPH-FEM coupling algorithm was established. The research results show that compared to traditional sand cushion layer, the peak stress at the central measuring point of the river sand cushion layer shed tunnel roof decreases by 42.52%, the peak compressive stress of the column decreases by 65.77%, and the peak rockfall acceleration decreases by 56.84%. Compared to the sand cushion layer, the depth of the impact pit of the falling ball in the river sand cushion layer has increased from 28.54 mm to 58.24 mm, with an increase of 50.99%. Under the condition of river sand cushion layer, the impact penetration buffering stroke of the falling stone is much higher than that of the sand cushion layer, thereby improving the energy dissipation and seismic reduction effect of the river sand cushion layer shed tunnel. The actual size shed tunnel structure model is reconstructed again. After the river sand cushion layer is laid, the peak stress of the abdominal unit of the roof decreases by 53.41%, the peak displacement of the abdominal unit of the roof decreases by 34.44%, and the peak rockfall acceleration decreases by 55.41%.

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Creep Parameter Inversion of Mudstone Tunnel Anchor Based on Multi-objective Optimization

LIU Chuncheng1, CHEN Menghai2, ZHAO Ningyu2, JIANG Haifei2, XU Kunjie3

2025, 44(9): 42-49. DOI: 10.3969/j.issn.1674-0696.2025.09.06

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To address the problem that traditional indoor and outdoor rock mechanics testing methods were difficult to accurately obtain macroscopic creep parameters of surrounding rock in tunnel-type anchorages within mudstone formations, which in turn affected the long-term stability assessment of engineering, the parameter inversion method based on the modified Burgers nonlinear viscoplastic model (Bugers-NVPB) was proposed. A backpropagation neural network (BPNN) was constructed to establish mathematical mapping between mudstone creep parameters and anchor plug body displacements. Combining with the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ), the multi-objective optimization inversion was carried out, and the effectiveness of the proposed method was verified through practical engineering. Research results demonstrate that when using the Bugers-NVPB parameters obtained from inversion for numerical simulation, the displacement curves of the front anchor surface, mid-section top, and rear anchor surface of the anchor plug are very close to those from the 1∶10 in-situ test. The proposed approach not only confirms the effectiveness of the Bugers-NVPB model in simulating long-term stability of tunnel-type anchorages in mudstone formations but also validates the engineering applicability of NSGA-Ⅱ-based parameter inversion.

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3D & 2D Integrated Parameterization Design of Tunnel Lining Structure Based on Geometric Semantics

QI Chenglong

2025, 44(9): 50-58. DOI: 10.3969/j.issn.1674-0696.2025.09.07

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In order to improve the efficiency of BIM modeling for railway mountain tunnel lining structures and the versatility of parameterized models, and solve the limitation of its built-in geometric constraint representation on the secondary development of parameterized lining structure modeling, the parameterized modeling of railway mountain tunnel lining structures was studied based on geometric semantics. The parameterized modeling algorithms for railway lining structures were developed and implemented in 2D & 3D environments respectively. A lining cross-section design software with real-time display of two-dimensional wireframing and output of XML results was developed, and a BIM design software for railway mountain tunnel lining was formed through the secondary development of the domestic BIMBase platform. The research results show that based on geometric semantics, the cross-sectional profile of railway lining structures is decomposed, the parameter relationship of cross-sectional profile under the segmented form is determined, and a parameterized design algorithm for cross-sectional profiles is proposed. Based on RapidXML package and Win32 API, the lining cross-section design software is developed to achieve interactive feedback design of cross-section profile in a two-dimensional environment. Based on domestic BIMBase geometry engine, 3D design software is developed, which verifies the effectiveness of cross-sectional design software and parameterization algorithm. The proposed parameterization algorithm is not limited by the built-in constraint representation of geometry engine and can directly draw the section profile, while meeting both the requirements of 2D & 3D design of lining structure, which improves the versatility of parameterized design of railway tunnel lining structures.

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Reasoning of Characteristics of Serious Traffic Accidents on Highways Considering Factor Partial Dependence

ZHAI Changxu1, 2, ZHU Jungong1, 2, XU Yinghao3, WANG Tao1, 2, QIN Wenwen4

2025, 44(9): 59-66. DOI: 10.3969/j.issn.1674-0696.2025.09.08

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In order to deduce the characteristics of severe traffic accidents on highways from a global perspective, the accidents of different severity levels were incorporated into a unified research system by use of linear programming methods, which was based on the historical accident data of a highway in a city in Southwest China since its opening 10 years ago. The conditional dependency between accident factors and accident severity was simulated from a global perspective by the use of the machine learning-based factor partial dependence analysis method, and the factorial characteristics of severe traffic accidents were reasoned. The research results show that accident time, vehicle type, accident configuration, accident location and weather conditions are pivotal features that precipitate severe accidents. There is a significant nonlinear relationship between accident time, accident location, drivers driving experience and accident severity. The time characteristics of serious accidents are 05:00 and 08:00, with the involved vehicle type predominantly being trucks, and the accident configuration is characterized by rear-end collisions, while the accident locations are typified by the 60-80 km section of the highway. The cloudy weather, drivers with less than one year of driving experience and tunnel sections are identified as distinctive features associated with severe accidents.

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Design of Real-Time Environmental Monitoring System for Trains Passing through Coal Transport Railway Tunnel

LIU Zunmin, SA Zhanyou, LI Baogang

2025, 44(9): 67-73. DOI: 10.3969/j.issn.1674-0696.2025.09.09

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The entry of coal-carrying trains into tunnels can cause turbulence in the airflow within the tunnels, causing coal dust or scattering. Designing a reliable environmental monitoring system for coal-carrying railway tunnels during train passage, which can monitor the wind field and the rules of dust changes in real time, is of significant importance for developing efficient measures to control dust and reduce the spillage of coal resources. According to the application requirements, a railway tunnel environmental information monitoring system was implemented by using gigabit ethernet and 4G network, and the overall scheme design and network architecture were completed. Control cabinets with data acquisition and support for ethernet data transmission interface were developed to meet the signal acquisition requirement of 10 Hz. The real-time storage program of on-site industrial computer and the monitoring software of upper computer based on Ali-Cloud platform were developed, which were applied to the engineering site, realizing local data storage and remote monitoring functions. By analyzing the basic laws of airflow movement and dust pollution during train passing, the basic transformation laws of wind field and dust signals during train passing were obtained. On-site applications show that the proposed system runs stably and reliably and meets the requirements of engineering applications. The application of the proposed system can provide data support for dust suppression in coal transportation tunnels.

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Vehicle Routing Problem in Reverse Logistics Based on Dynamic Demand and Resource Sharing

YANG Xiaohua, WANG Yong, GOU Mengyuan, LUO Siyu, ZHU Li

2025, 44(9): 74-83. DOI: 10.3969/j.issn.1674-0696.2025.09.10

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Aiming at the deficiency of combining the research of reverse logistics vehicle routing optimization with dynamic customer demand processing and vehicle sharing scheduling, the strategies of transportation resource sharing and dynamic insertion were proposed, and the reverse logistics vehicle routing optimization problem based on dynamic demand and resource sharing was studied. Firstly, a dual objective optimization model that minimized the operating costs of reverse logistics and the number of collected vehicles was constructed. Secondly, a hybrid heuristic algorithm combining multi-objective particle swarm optimization algorithm and taboo search algorithm (MOPSO-TS) was designed to solve the model. The effectiveness of the proposed model and algorithm was verified through comparative analysis with NSGA-II, MOGA, and MOACO algorithms. Finally, based on the actual case of Chongqing, various operational indicators before and after optimization were compared and analyzed. The results show that the proposed model and algorithm can effectively reduce the total operating costs of reverse logistics and the number of collected vehicles. The research results can provide methodological references for optimizing reverse logistics vehicle paths based on dynamic demand and resource sharing and provide decision support for building efficient and low-cost reverse logistics networks.

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Mobile ViT Network Road Defect Detection Model Integrating Multi-task Learning

LIU Yunfei1, LI Shuang1, MA Jianxiao2

2025, 44(9): 84-92. DOI: 10.3969/j.issn.1674-0696.2025.09.11

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With the widespread application of deep learning technology in the field of computer vision, significant progress has been made in road defect detection based on deep learning. Addressing the issues of insufficient detection accuracy, high miss detection rates and difficulties in detecting small objects when dealing with complex road scenes with existing methods, an innovative multi-task learning road defect detection model (MTL-RDD) was proposed, which enhanced the detection performance by simultaneously optimizing object detection and semantic segmentation tasks. In the proposed model, a lightweight MobileViT architecture based on Transformer was adopted as the backbone network for efficient feature extraction, and the GELAN structure was used to realize the multi-scale information integration, effectively reducing inference time. Through fine-grained supervision of segmentation tasks, MTL-RDD improved the robustness and generalization ability of the proposed model, demonstrating excellent performance especially in complex scenarios. Experimental results demonstrate that MTL-RDD achieves a 2.9% and 3.5% improvement in mAP@0.5-0.95 and mAP@0.5, respectively, compared to YOLOv8-s, outperforming existing mainstream methods in terms of accuracy, speed and small object detection. The proposed detection model provides a more precise and efficient solution for road defect detection.

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Integrated Cognitive System Model for Drivers Based on Multi-panel Traffic Signs

DENG Chao1, 2, ZENG Yingxuan1

2025, 44(9): 93-101. DOI: 10.3969/j.issn.1674-0696.2025.09.12

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The reaction time of drivers reading traffic signs on highways is a key factor for determining the number and information capacity of traffic signs installed on highways. Based on the queueing network-adaptive control of thought-rational (QN-ACTR) theory, a cognitive computational model was developed. By quantifying and predicting the reading response time of single/multiple traffic signs, the adjustment mechanism of driving experience on response performance was elucidated. A production system incorporating traffic sign screening rules was established, and an integrated experimental platform combining the QN-ACTR model with TORCS driving simulator was constructed. Three hypotheses for driver visual search and response strategies were proposed and validated, and the effectiveness of the proposed model was verified using parallel dual task experimental data. The research results show that the proposed model can accurately predict reaction time variations under different numbers of traffic signs and road name information, with a mean absolute percentage error (EMAP) of 2.47% and a root mean square error (ERMS) of 0.06 s.

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Law of Heavy Hammer Breaking Reef under the Influence of Hammer Shape Change

ABI Erdi1, TAN Song1, LIU Mingwei1, CHEN Changbin2, DU Jianru2, HAN Yafeng1

2025, 44(9): 102-109. DOI: 10.3969/j.issn.1674-0696.2025.09.13

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With the in-depth implementation of the concept of “ecological priority and green development”, the underwater heavy hammer rock-drilling technology with smaller ecological impact has been applied in the construction of navigation channels. Due to the large difference of hammer shape in different projects, the impact of hammer shape changes on reef breaking efficiency varies, which restricts the extensive promotion of heavy hammer rock-drilling technology. Aiming at the problem of the reef breaking and penetration laws of super-large tonnage heavy hammer with different hammer shapes, the movement laws of underwater heavy hammer with different drop distances were analyzed through the on-site test of breaking reef with heavy hammer. Moreover, based on 3DEC discrete element, the dynamic response calculation model of reef under the impact of heavy hammer with different shapes, and the laws of breaking reef with heavy hammer under the influence of hammer shape changes were analyzed. The research results show that the dynamic response model of the heavy hammer penetration considering the drag and water resistance can better simulate the reef breaking process with underwater heavy hammer. The maximum stress of the reef under the impact of the circular heavy hammer is 1.16~1.75 times that of other hammer shapes, and the bullet shaped heavy hammer has the best penetration effect. When the drop distance is less than 13 m, the single breaking volume of the circular heavy hammer is the largest; when the drop distance is more than 13 m, the breaking volume of the bullet shaped heavy hammer is the largest.

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Temperature Control and Influencing Factors Analysis of Early-Age Sidewall Based on Water Cooling System

CHEN Chunchao1,2, CHEN Shihai2, CHEN Jianfu3, ZHANG Hanwu4, LUO Xiaofeng5

2025, 44(9): 110-120. DOI: 10.3969/j.issn.1674-0696.2025.09.14

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In order to explore the influence of the cooling pipe system on the temperature field of the mass concrete sidewall at an early age and reduce the cracking risk of the early-age sidewall, a heat flow coupling model considering the change of concrete behavior and the effect of cooling pipe was constructed. On this basis, Comsol Multiphysics software was used for the secondary development of the proposed model to solve the temperature field of the early-age sidewall under the action of the cooling pipe, and the early-age temperature monitoring of the sidewall was used to verify the reliability of the proposed model. Finally, the effects of cooling pipe parameters such as water temperature, flow rate, pipe spacing, and thermal conductivity on the temperature field of the early-age sidewall were analyzed. The research results show that the difference between the calculation results of the heat flow coupling model and the measured values is small, and the calculation results are reliable. The cooling water temperature gradually increases along with the direction of water flow, and the cooling pipe should change the direction of water flow regularly to improve cooling efficiency. The temperature difference in the area near the cooling pipe exhibits a trend of first increasing and then decreasing with the increase of age, reaching its maximum at the peak temperature age. Reducing the water temperature and increasing the flow rate and thermal conductivity can reduce temperature peak and cooling rate of the early-age sidewall, while it will increase the temperature difference in the area near the cooling pipe. When the flow rate exceeds 0.6 m3/h, increasing the flow rate has no significant effect on the temperature field. Reducing the pipe spacing can reduce the temperature peak, cooling rate, and temperature difference of the early-age sidewall. In practical engineering, the water temperature of the cooling pipes should be strictly controlled, with the spacing between cooling pipes kept below 100 cm and the cooling water flow rate ensured to be no less than 0.6 m3/h. Meanwhile, it is recommended to use PVC pipes, as cooling pipes, which can not only reduce the temperature peak of the sidewalls and the temperature gradient around the cooling pipes, but also lower the cost of the cooling pipes.

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Research Progress on Corrosion Mechanism and Corrosion Prevention of Water Pipeline

WANG Kui1, SHI Yichao1, ZHAO Mingjie1, 2, LIU Peng3, ZHU Linchen1

2025, 44(9): 121-130. DOI: 10.3969/j.issn.1674-0696.2025.09.15

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Long-distance water pipelines are the core components of water resources allocation project. The corrosion behavior of water pipelines is crucial for the reliability of water resources transmission and supply systems, aiming to sort out the relevant research findings on corrosion behavior and its corrosion prevention measures of the existing water pipelines, and provide ideas for corrosion prevention research on long-distance water pipelines. By deeply summarizing the impact of three factors such as chemical environment, fluid characteristics and material properties on the corrosion of water pipelines, the different corrosion mechanisms of electrochemical corrosion, chemical corrosion, and microbial corrosion were analyzed, and the applicability of three commonly used anti-corrosion methods, namely anti-corrosion coating, cathodic protection and pipeline material, was discussed. Finally, the related research on corrosion behavior and corrosion prevention measures of water pipelines were prospected. Existing studies show that the research on factors and behaviors of water pipeline corrosion is mostly focused on a single form of corrosion under strong corrosive media. It is necessary to further carry out the corrosion mechanism research on different corrosion behaviors under multi-influential factors and explore more economical and practical corrosion prevention measures at the same time.

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