Table of Content

25 June 2025, Volume 53 Issue 6

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2025, 53(6):  0. 

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Architecture & Civil Engineering

Influence of Laser Remelting Treatment on the Fatigue Performance of Steel Structure Welding Joints

KANG Lan, LI Rongwen, SU Jingyu, et al

2025, 53(6):  1-11.  doi:10.12141/j.issn.1000-565X.240357

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Welding is an important means of connecting steel structures, and the fatigue performance of welded joints is related to the safety of steel structures. In order to improve the fatigue performance of welded joints in steel structures, this study proposes to use laser remelting to treat welded joints. For this purpose, this study conducted high cycle fatigue tests on the welded joints and laser remelting treated joints of Q355 steel plate butt welding. Fatigue fracture analysis was conducted using scanning electron microscopy, and the stress life (S-N) fatigue curves of the welded joints and laser remelting welded joints were fitted based on the test results, and compared with the standard fatigue formula. The experimental results show that laser remelting treatment can change the location of fatigue fracture in welded joints and significantly improve the fatigue life of welded joints; The fatigue performance design curves provided by the American Steel Structure Code ANSI/AISC360-22, European Code EN 1993-1-9:2005, and Recommended Method for Offshore Steel Structure Design DNV-RP-C203 tend to be conservative for welded joints treated with laser remelting.





Bending Characteristics of 2-Layer Spiral Strand on Typical Contact Conditions

WANG Ronghui, LIU Xiyue, ZHAO Yonglin, et al

2025, 53(6):  12-24.  doi:10.12141/j.issn.1000-565X.240061

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In order to study the mechanical behavior of 2-layer spiral strand under tension-bending coupling effect and the cooperative working mechanism of internal wires. Taking friction and slippage between wires into account, static equilibrium relationships of layer-wire segment on 2 typical conditions of inter-layer contact and coupled contact are established and derived analytically. An improved semi-refined finite element model is presented for numerical simulation and compare the results. Relative slip direction between wires on 2 contact conditions are obtained from the distribution of shear force on layer-wire, based on which the axial force limit of layer-wire after sliding is derived according to the equilibrium equation. The bending moment-local curvature relation of spiral strand is obtained by summing the bending moments contributed by each wire under tension-bending coupling effect, and a simplified bending moment-mean curvature relation is proposed to describe bending behavior of the spiral strand. The result shows that there are slip stagnation points on contact surface of adjacent wires because layer-wire rotates along the axis of spiral strand periodically, and relative slip direction on both sides of the stagnation point is opposite. The slip stagnation point and initial slip position on contact surface of layer-wire to layer-wire and layer-wire to core-wire are different. Ignoring internal slip spreading process, we have the same bending moment-mean curvature relation on 2 contact conditions of 2-layer spiral strand, the function image is a double broken line. The error of the bending deformation results before and after slipping between semi-refined FE model and analytical values is less than 4%, and the extracted relative slip results are in agreement with the analysis conclusions.

Temperature Model of Steel Box Girder of High Speed Railway Cable-Stayed Bridge Based on Machine Learning

LIU Wenshuo, ZHONG Mingfeng, ZHOU Bo, et al

2025, 53(6):  25-33.  doi:10.12141/j.issn.1000-565X.240200

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To investigate the temperature pattern of the steel box girder of high-speed railway large-span cable-stayed bridges. Based on the measured temperature data of Yuxi River Bridge on the Shangqiu-Hefei-Hangzhou High-Speed Railway and database information, a study was conducted on the influence patterns of various meteorological factors on the temperature of the steel box girder and the temporal and spatial distribution of the temperature field using machine learning methods. By establishing machine learning models that map various meteorological factors to the uniform temperature of the steel box girder, the superiority, inferiority, and applicability of each model were analyzed, and the importance ranking of meteorological factors affecting the uniform temperature of the steel box girder was obtained. A comprehensive study on the vertical distribution pattern of the temperature of the steel box girder was conducted using machine learning methods and exponential fitting. The results show that the importance ranking of meteorological factors affecting the uniform temperature of the steel box girder from high to low is: air temperature, cumulative radiation, air pressure, humidity, radiation intensity, wind direction, horizontal visibility, wind speed, and precipitation, with the temperature importance far exceeding other meteorological factors. Among them, the atmospheric temperature 2 to 3 hours ago has the greatest impact on the uniform temperature of the steel box girder, reflecting a lag of 2 to 3 hours in the impact of atmospheric temperature changes on the uniform temperature of the steel box girder. Neural networks, random forests, and XGBoost models can all accurately predict the uniform temperature of the steel box girder, with the neural network model performing better overall. The sensitivity of the negative temperature difference of the steel box girder to meteorological factors is relatively low, and it is more related to the heat transfer mode of its own structure. The exponential function can fit the distribution of the maximum positive temperature difference of the steel box girder vertically with higher accuracy, and the parameters can be determined through machine learning methods, with different parameters having practical physical meanings. The results provide reference for the prediction and distribution pattern of temperature in the steel box girder of high-speed railway large-span cable-stayed bridges.

Study on Maintenance Strategy of Existing Concrete Bridge Considering Reinforcement Time Interval

WANG Xiaoming, LI Pengfei, WU Runhan, YANG Wenjie LI Chenxi

2025, 53(6):  34-43.  doi:10.12141/j.issn.1000-565X.240176

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In response to the issue of subjective uncertainty in the formulation and implementation of maintenance and strengthening strategies for in-service bridges, this paper proposes a bridge maintenance decision framework that considers the time interval it takes. This framework introduces interval numbers to quantify subjective uncertainty that cannot be described by probabilities. It achieves a direct mapping of the most unfavorable reliability index under probability-interval mixed uncertainty based on surrogate models. Thus, the multi-objective optimization algorithm NSGA-II can be used to make the framework efficient. Taking a typical assembled simply supported T-beam bridge as an example, a probability model of vehicle load effects is established based on WIM system measured data. Subsequently, a time-dependent resistance degradation model is introduced to optimize the maintenance strategy for the T-beam bridge and formulate a maintenance decision library for typical T-beam bridges. The results indicate that strategies with smaller time intervals correspond to smaller Life Cycle Cost (LCC) and lower permissible subjective uncertainty. Strategies with larger time intervals, although resulting in higher LCC, provide more flexibility for construction, decision-making, and other aspects. For simply supported T-beam bridges with spans ranging from 20 to 40 meters, various reinforcement strategies can be employed to meet the service reliability index requirements while minimizing LCC.

Vehicle Engineering

Design of Acceleration Slip Regulation Multi-Mode Control Strategy of Distributed Drive Electric Vehicle

ZHU Shaopeng, MAO Jingyang, LIU Dongqing, et al

2025, 53(6):  44-55.  doi:10.12141/j.issn.1000-565X.240330

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Distributed drive electric vehicles can independently and accurately control the driving torque of each wheel to achieve acceleration slip regulation control. However, a single acceleration slip regulation strategy is difficult to meet the requirements of various complex driving conditions, and cannot guarantee the optimal comprehensive driving performance of the vehicle. Therefore, an acceleration slip regulation multi-mode control strategy that responds quickly and controls accurately is proposed to adapt to various complex working conditions. Firstly, addressing the performance requirements under different driving conditions, corresponding drive modes and switching strategy are designed based on the seven-degree-of-freedom distributed drive vehicle model. Secondly, based on the adhesion characteristic curves of six standard road surfaces using the Burckhardt tire model, and utilizing an optimized linear interpolation algorithm, a road surface recognition fusion algorithm is proposed to calculate the optimal slip ratio, which is used as the control target to design a PID controller with nonlinear parameter tuning for power distribution control and switching. Finally, a CarSim vehicle model and an acceleration slip regulation control model in Matlab/Simulink are established and co-simulation verification was conducted on low adhesion road, joint road, bisectional road, low adhesion slope, and bisectional slope. Simulation results show that the road surface recognition strategy can accurately identify the adhesion coefficient of the road, the acceleration slip regulation control strategy can quickly respond and accurately switch between different modes under different working conditions, balancing dynamics performance and stability performance, and effectively improve anti-skid performance.

Consider Fuel Cell Vehicle Energy Management Strategies for Air Conditioning Systems

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ZHAO Youqun, XU Zhou, YU Zhihao, et al

2025, 53(6):  56-65.  doi:10.12141/j.issn.1000-565X.240396

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In the actual operation of fuel cell hybrid electric vehicles, the air conditioning system provides a comfortable environment for drivers and passengers. However, the operation effect of the air conditioning system interacts with the energy distribution of the actual operation of the vehicle, so it is necessary to consider the air conditioning system into the energy management strategy, and design an energy management strategy that takes into account the hydrogen consumption economy of the vehicle while meeting the comfort requirements of the cabin temperature. Firstly, based on the vehicle dynamics model, the heat balance equation is used to establish the heat pump air-conditioning system model and heat load model. Then, the dual delay depth deterministic strategy gradient (TD3-PER) algorithm combining the double Q network and the depth deterministic strategy gradient is used to establish the energy management strategy considering the energy consumption of the air conditioning system and the vehicle operation demand. Finally, the simulation results under typical NEDC working conditions show that the air conditioning system under the TD3-PER energy management strategy can rapidly reach and maintain the cabin temperature within a comfortable range of 22℃ to 26℃ within 100 seconds, satisfying the cooling/heating effect and ensuring the cabin temperature comfort, which verifies the feasibility of the TD3-PER energy management strategy when considering the air conditioning system. In cooling/heating of air conditioning system, the strategy based on TD3-PER algorithm can prolong the service life of fuel cell and battery compared with the strategy based on the traditional depth Deterministic strategy gradient (DDPG) algorithm, and improve the economy of hydrogen consumption in cooling/heating by 2.59% and 3.58%, respectively. It is verified that the energy management strategy based on TD3-PER algorithm has more advantages than the traditional algorithm in reducing hydrogen consumption and improving vehicle economy.

Application of Subjective and Objective Comprehensive Evaluation Method of Drivability in Starting Conditions

WU Fei, SUN Xiankui, WANG Pengcheng

2025, 53(6):  66-76.  doi:10.12141/j.issn.1000-565X.240243

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In order to improve the accuracy and reliability of drivability evaluation method, a subjective and objective comprehensive evaluation method combining limit gradient lifting algorithm and Shapley interpretation algorithm is proposed. In this paper, the starting condition of the vehicle is taken as the research goal, and 9 objective evaluation indexes are defined under the starting condition. In this paper, the limit gradient enhancement algorithm is used to evaluate the objective index value and subjective score of bidirectional mapping, and Shapley interpretation algorithm is used to attribute the features of the mapping model, and a comprehensive evaluation model of driving performance is constructed with both prediction accuracy and interpretability. This method is applied to many road tests, and several comprehensive driving evaluation methods are compared and analyzed. The results show that: Compared with mainstream driving evaluation algorithms such as BP neural network, random forest and extreme learning machine, the mapping accuracy of the proposed method is significantly improved. Moreover, the proposed comprehensive evaluation method is interpretable to a certain extent, and has reference significance for subjective and objective comprehensive evaluation of driving evaluation.




Intelligent Transportation System

Dynamic Scheduling of Demand Responsive Transit Based on Model Predictive Control

JIN Wenzhou, ZHANG Yong, SUN Jie

2025, 53(6):  77-90.  doi:10.12141/j.issn.1000-565X.240331

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As a typical representative of the new mode of shared public transport, demand responsive transit is facing the challenge of efficiently processing travel demand and real-time planning of vehicle routes. Traditional research on demand responsive transit dynamic scheduling mainly focuses on real-time adjustment of vehicle routes after dynamic demand is known, which often struggles to comprehensively adapt to changes in travel demands. Therefore, this study introduces model predictive control methods to establish a dynamic scheduling model for demand responsive transit, utilizing potential future-stage passenger flow information to optimize current-stage scheduling decisions and timely re-planning according to the latest disclosed information. In this study, an adaptive large neighborhood search strategy is adopted to optimize the iterative vehicle scheduling sequence through a two-stage heuristic method. Numerical experimental results demonstrate that in ideal scenarios without prediction deviation, compared to traditional dynamic scheduling methods, this study's approach significantly reduces the total cost of the system by 14.54%; even in a pessimistic scenario with a 30% prediction deviation, it still achieves a cost optimization of 5.27%. Moreover, various passenger service indicators exhibit superior performance, indicating strong universal applicability in different stochastic environments.

Joint Optimization of Circular Line Electric Bus Vehicle Scheduling and Driver Scheduling

HU Baoyu, QI Yue, JIA Dianjing, et al

2025, 53(6):  91-103.  doi:10.12141/j.issn.1000-565X.240440

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In order to address the issue of imbalanced task distribution between electric bus vehicles and drivers in the loop line, a joint optimal scheduling model is presented in this paper. It mainly allocates vehicles and drivers in the clockwise and counterclockwise directions to enhance the overall utilization rate. Given the circular route and the non-fixed passengers and vehicles, an orderly charging management plan and the vehicle and driver scheduling scheme are formulated with the aim of minimizing the total operating cost and schedule adjustment by comprehensively considering the constraints of vehicle mileage, workload, the number of charging piles, vehicle charging time, driver working time and rest time. Regarding the solution, the mixed integer nonlinear programming model is transformed into a linear programming model through linear transformation, and the scheduling scheme is obtained using the CPLEX solver. Secondly, the multi-objective particle swarm algorithm (MOPSO) and the improved multi-objective particle swarm algorithm (ε-MOPSO) based on the ε constraint processing mechanism are respectively employed to solve the scheduling scheme, and the convergence and uniformity of the external file set are ensured through the grid method. Finally, this paper takes Bus 200 (inner and outer rings) of Beijing Ring Line as an example to verify and compare the calculation results of the CPLEX solver, the traditional multi-objective particle swarm optimization (MOPSO), and the improved multi-objective particle swarm optimization (ε-MOPSO) based on the ε constraint processing mechanism proposed herein. The results validate the efficacy of the enhanced algorithm. The optimized scheduling plan reduces the number of vehicles from 28 to 23, amounting to a total of 17.86%. The number of drivers drops from 28 to 25, with a total reduction of 10.71%. The reduced fleet size and the number of drivers thereby lead to a decrease in total operating costs. The timetable is adjusted by an average of 4.13 minutes per departure time, and the departures are more evenly distributed to guarantee the demand of passengers. It enhances the operational efficiency of public transport and holds significant practical significance.

A Review of The Multi-Level Urban Impacts of Shared Autonomous Vehicles

ZHONG Shaopeng, LIU Ao, ZHAI Junnuo, et al

2025, 53(6):  104-118.  doi:10.12141/j.issn.1000-565X.240119

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To gain a deeper understanding of the potential impacts of Shared Autonomous Vehicles (SAVs) on urban and promote the sustainable development of urban transportation systems, a comprehensive review and systematic analysis of the multi-level impacts of SAVs was conducted. The aim was to summarize the main contributions and shortcomings of previous studies and propose possible directions for future research. The review findings indicate that existing studies primarily focus on the short-term impacts of SAVs on the transportation system, including residents' travel behavior and road traffic flow. However, there is relatively little research on the long-term impacts of SAVs, particularly concerning urban accessibility, environment, and energy. While some studies have revealed potential negative effects of SAVs, such as adverse impacts on the environment or accessibility, few have proposed targeted and effective development strategies. Additionally, in terms of methods, existing studies mainly rely on qualitative analysis or independent transportation demand models for projections and simulations, which have certain limitations regarding the reliability of the results. Future research should develop the integrated land use and transportation model and combine the integrated model with data-driven methods to more accurately, comprehensively, and systematically analyze the long-term (negative) impacts of SAV introduction on urban land use, environment, and energy. This approach should also aim to propose targeted development strategies and countermeasures to optimize the application of SAVs, minimize their potential negative impacts, and promote the development of urban transportation systems towards efficiency, intelligence, and sustainability.

A Carpooling Matching Method Considering Passengers’ Time-Price Elasticity

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LONG Xueqin, ZHAI Manrong, WANG Yuanze, et al

2025, 53(6):  119-130.  doi:10.12141/j.issn.1000-565X.240365

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In order to improve the carpooling matching probability and satisfaction, this paper proposes a dynamic carpooling matching method that considering passengers’ time-price elasticity. RP+SP questionnaire survey method was applied to collect passengers’ individual attributes and carpooling choosing behavior under different travel scenarios. Clustering passengers based on the carpooling preferences, passengers were divided into three categories. A discrete elasticity analysis model was established to obtain the time-price elasticity of carpooling and non-carpooling  passengers for the three categories. Incorporating time-price elasticity into travel costs, a generalized cost function was established for all passengers and drivers. A two-level planning model was constructed, for which, the upper-level model considered the benefits of drivers and passengers, while the lower-level model aimed to maximize the carpooling probability of all passengers. Considering route and capacity constraints, a carpooling matching algorithm was designed. Finally, taxi trajectory data of Xi'an was took as an example, different carpooling matching schemes were solved when considering different time-price elasticity, and the differences between all matching schemes were compared. The results indicate that when passengers have high time-price elasticity (2.22, 0.99), the non-carpooling costs are lower than carpooling costs, and passengers cannot effectively match to each other. When passengers have low time-price elasticity (0.12), multiple passengers can be matched to implement carpooling. The conclusion of this study demonstrates the necessity of time-price elasticity and the results can be used to guide government for carpooling order allocation and taxi dispatching.

Workplace and Residence Identification and Travel Activity Classification Driven by Trajectory Data

XIE Kun, XING Xinyuan, DONG Honghui, et al

2025, 53(6):  131-139.  doi:10.12141/j.issn.1000-565X.240196

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The vehicle trajectories contain enrich travel characteristic information. Analyzing the characteristics of travel activities and identifying the location of work, residence, and travel interests from the trajectories could provide the support for the layout plans of transportation facilities and the reduction plans of transportation carbon emission. The research takes the point set that composed of the first starting point and the last ending point of daily travel activities as the possible point set for residence, while the other starting and ending points form the possible point set for workplace and place of interest. On the basis of possible point sets, a method that is based on mean shift clustering and spatiotemporal dual constraints is proposed to determine the location of residence. Combining three conditions: cluster density, average residence time of points within the cluster, and travel time range, the coordinates of residence, workplace, and place of interest are determined. Based on the KD Tree algorithm, adjacent POI data is matched for each type of location coordinate to obtain the specific location and name of the workplace and residence. Based on the identification of workplace and travel interests, the travel activity is characterized by the number of trips, travel distance, and travel time. The stability and difference of travel are used to characterize the regularity of travel. The K-means++ clustering analysis algorithm is used to analyze the characteristic categories of travel activities. Taking 1708 vehicles with travel activities for 34 days in Beijing as an example, the empirical research is conducted based on the driving trajectory data. The research results indicate that the distribution characteristics of workplace and residential areas determined by the proposed method is consistent with practical laws, with high accuracy and reliability. The travel activity feature classification method based on K-means++ algorithm classifies all 1708 travelers into four categories: inactive, active multiple trips, active long-term trips, and active long-distance trips. According to the two indicators of travel regularity (travel stability and work rest travel difference), travelers with changes in travel patterns are classified into four categories: stability active on workdays, stability active on rest days, non-stability active on workdays, and non-stability active on rest days. (Supplement the proportion of each type).

Accuracy and Its Comparison of Road Surface Roughness predicted by Different Intelligent Devices

ZHANG Jinxi, PING Xinying, GUO Wangda, et al

2025, 53(6):  140-150.  doi:10.12141/j.issn.1000-565X.240350

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Even though the detection of road surface roughness has reached standardization, the rapid, high-frequency and low-cost intelligent detection method of IRI has also been widely studied for constructing the smart cities and the intelligent transportation facilities. However, the detection accuracy and detection effectiveness of IRI based on different intelligent devices have not been deeply studied. Firstly, this paper conducted road driving experiments using two intelligent IRI detection devices that was developed by the authors’ research group. One device is called driving data collection Smartphone APP, and another one is called road driving data collection Intelligent Terminal Device. The data of driving test vehicle, such as vibration acceleration, speed, GPS location and so on, was collected during the driving experiment, and four vibration acceleration indicators that can best reflect the impact of IRI were determined by using a random forest model. Next, three prediction models of IRI were established using three neural networks: recurrent neural network RNN, gated recurrent unit GRU, and long short-term memory network LSTM. The detection accuracy of IRI using different devices and different prediction models was compared. The results showed that, LSTM model achieved the best robustness and highest prediction accuracy among three neural network models. The R² values of predicted IRI is 0.864 and 0.789 for the Intelligent Terminal Device and Smartphone APP respectively, which means that the detection accuracy of Intelligent Terminal Device was higher than that of Smartphone APP. The results of this paper have theoretical significance and application value for improving the informatization level of IRI detection and monitoring, as well as enhancing the scientific level of road maintenance decision-making.