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Interfacial shear behavior in steel-concrete-steel composite structures with stud connectors after freeze-thaw cycles

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Construction and Building Materials 2025年 484卷

作者： Xudan Wei Jiachuan Yan Youzhu Lin Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin 150090 China Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology Harbin Institute of Technology Harbin 150090 China School of Civil and Transportation Engineering Northeast Forestry University Harbin 150040 China

Steel-concrete-steel (SCS) composite structures have potential applications in the northern cold region. To investigate the interfacial shear performance and failure mechanism of SCS composite structures with stud connectors subjected to freeze-thaw cycles (FTCs), freeze-thaw tests and push-out tests were conducted on 36 specimens to investigate the effects of concrete type, stud quantity, and FTCs. The test results showed that when the core concrete was changed from C30 concrete to ultra-high-performance concrete (UHPC), the shear capacity, shear stiffness, and ductility of the specimens all increased significantly, by approximately two times. Moreover, the failure mode of the specimens shifted remarkably from concrete splitting damage to stud breakage failure. Increasing the stud quantity or FTCs reduced the shear capacity and stiffness of the specimen, yet it exerted a minimal impact on the ductility. In addition, precise finite element models were established to investigate the effect of the relative strength between studs and concrete and the stud spacing on the failure modes and interfacial shear capacity. Based on experimental and finite element data analysis, a formula for calculating the interfacial shear capacity of SCS composite structures after FTCs was proposed. The calculation results were consistent with the test and simulation results, and this formula can serve as a reference for the design and application of SCS composite structures in cold regions.

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Development and application of state-dependent fractional plasticity in modeling the non-associated behavior of granular aggregates

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Acta Mechanica Solida Sinica 2017年第5期30卷 507-519页

作者： Yifei Sun Shunxiang Song Yang Xiao Jiancheng Zhang Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering College of Civil and Transportation Engineering Hohai University Nanjing 210098 China. College of Civil and Transportation Engineering Hohai University Nanjing 210098 China. State Key Laboratory of Coal Mine Disaster Dynamics and Control School of Civil Engineering Chongqing University Chongqing 400450 China School of Naval Architecture and Civil Engineering Jiangsu University of Science and Technology Jiangsu 215600 China

To characterize the constitutive behavior of granular aggregates, a non-associated plasticity model with two different yield and plastic potential surfaces was usually used. However, in this paper, a state-dependent fractional elastoplastic model is proposed by only perform- ing the first- and fractional-order differentiations of the yield function. The non-associated plastic flow is obtained without using any plastic potential functions. The state dependence is considered by correlating the fractional order with a state parameter. The model is then validated by simulating a series of test results of different granular aggregates, including sand, ballast and rockfill, under a variety of loading conditions.

关键词： Fractional plasticityGranular aggregatesNon-associated flow

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A review of vehicle speed control strategies

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Journal of Intelligent and Connected Vehicles 2023年第4期6卷 190-201页

作者： Changxi Ma Yuanping Li Wei Meng School of Traffic and Transportation Lanzhou Jiaotong UniversityLanzhou 730070China Key Laboratory of Railway Industry on Plateau Railway Transportation Intelligent Management and Control Lanzhou 730070China Gansu Longyuan Information Technology Co. Ltd.Lanzhou 730070China

Currently,traffic problems in urban road traffic environments remain severe,traffic pollution and congestion have not been effectively improved,and traffic accidents are still *** traffic signal control methods have little effect on these *** the continuous improvement of communication technology and network connections,vehicle speed guidance provides a new idea for solving the above problems and has gradually become a popular topic in academic ***,its generalization has ***,this paper summarizes the research on vehicle speed control strategies in urban road environments and provides suggestions for future *** this paper,we summarize the existing research in four ***,we categorize existing research based on vehicle ***,the vehicle speed guidance problem is divided according to the problem research ***,we summarize the existing literature regarding vehicle ***,we summarize the methods used for speed *** an analysis of the existing literature,it is concluded that there is a deficiency in the existing research,and suggestions for the future of vehicle speed guidance research are suggested.

关键词： speed guidance vehicle control vehicle trajectory planning intelligent transportation

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Inhibition of lithium dendrites and dead lithium by an ionic liquid additive toward safe and stable lithium metal anodes

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Chinese Chemical Letters 2022年第8期33卷 3951-3954页

作者： Shengjie Zhang Bin Cheng Yanxiong Fang Dai Dang Xin Shen Zhiqiang Li Ming Wu Yun Hong Quanbing Liu Guangzhou Key Laboratory of Clean Transportation Energy Chemistry Guangdong Provincial Key Laboratory of Plant Resources BiorefinerySchool of Chemical Engineering and Light IndustryGuangdong University of TechnologyGuangzhou 510006China Department of Chemical Engineering Beijing Key Laboratory of Green Chemical Reaction Engineering and TechnologyTsinghua UniversityBeijing 100084China

The uncontrolled growth of lithium dendrites and accumulation of"dead lithium"upon cycling are among the main obstacles that hinder the widespread application of lithium metal ***,an ionic liquid(IL)consisting of 1-methyl-1-propylpiperidinium cation(Pp_(13)^+) and bis(fluorosulfonyl)imide anion(FSI^(-)),was chosen as the additive in propylene carbonate(PC)-based liquid electrolytes to circumvent the shortcoming of lithium metal *** optimal 1%Pp_(13) FSI acts as the role of electrostatic shielding,lithiophobic effect and participating in the formation of solid electrolyte interface(SEI)layer with enhanced *** in-situ optical microscopy records that the addition of IL can effectively inhibit the growth of lithium dendrites and the corrosion of lithium *** study delivers an effective modification to optimize electrolytes for stable lithium metal batteries.

关键词： Ionic liquid Piperidinium Lithium metal anode Solid electrolyte interface Lithium dendrites Dead lithium

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STGSA:A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 226-238页

作者： Zebing Wei Hongxia Zhao Zhishuai Li Xiaojie Bu Yuanyuan Chen Xiqiao Zhang Yisheng Lv Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the Shandong Key Laboratory of Smart Transportation(Preparation) Shandong Jiao Tong UniversityShanghai 200240China the College of Science China University of PetroleumQingdao 266580China the Department of Transportation Engineering School of Transportation Science and TechnologyHarbin Institute of TechnologyHarbin 150090China IEEE

The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the *** existing frameworks typically utilize separate modules for spatial and temporal correlations ***,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some ***,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between *** overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies ***,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution *** each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting ***,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven ***,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global *** projects the embedding input repeatedly with multiple different ***,the predicted values are generated by stacking several multi-stream *** experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA

关键词： Deep learning graph neural network(GNN) multistream spatial-temporal feature extraction temporal graph traffic prediction

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Real-time 3D Object Detection based on Dynamic Sparse Voxel Transformer in Mining Area 22

Real-time 3D Object Detection based on Dynamic Sparse Voxel ...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Sun, Shengdi Wang, Zhangyu Liu, Runsen Yang, Songyue Wang, Liyun Liu, Wentao School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

To improve the safety and efficiency of mining operation, the development of unmanned transportation technologies for mining trucks is extraordinarily rapid. The application of LiDAR in autonomous mining trucks has also become increasingly popular. However, due to the significant difference in the size of objects and the complex noise, the problem of point cloud object detection in mining scenarios remains a major challenge. Existing methods, such as those based on point cloud clustering algorithms, have proved to be difficult to meet practical application requirements. In this paper, a real-time point cloud object detection based on Dynamic Sparse Voxel Transformer (DSVT) is introduced. A series of experiments were conducted to examine the efficacy of the proposed methodology. The experimental results demonstrated that the proposed method attained a mean average precision of 75%. Additionally, the detection latency of the proposed method was observed to be 62 ms. Collectively, the detection speed and accuracy both align with the practical application requirements. © 2024 IEEE.

关键词： Trucks

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Intersection trajectory prediction by integrating graph neural networks and candidate lane intent probabilities 3

Intersection trajectory prediction by integrating graph neur...

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3rd International Conference on Electronic Information engineering, Big Data, and Computer technology, EIBDCT 2024

作者： Zhang, Chuanying Xu, Guoyan Chen, Zhifa Li, Lei Zhou, Bin School of Transportation Science and Engineering Buaa Beihang University Beijing China Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beijing China

ISBN: (纸本)9781510680449

In intersection scenarios, agents exhibit diverse intent choices, making trajectory prediction problems fraught with significant uncertainty. This study proposes a cross-intersection trajectory prediction method that considers the probability of agent intent. It integrates a vehicle speed model, an intent predictor based on agent kinematics, and a trajectory prediction method based on graph neural networks to enhance the accuracy of vehicle agent trajectory predictions by precisely capturing the intent of vehicle agents at intersections. Through training and validation on a large dataset of real-world driving data, experiments have demonstrated the method's capability to predict the behavior of traffic agents in intersection scenarios accurately. Specific experimental results on the nuScenes dataset show MinADE_5, MinADE_10, MissRate_5,2, and MissRate_10,2 values of 1.70, 1.45, 0.63, and 0.48, respectively. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Forecasting

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Prediction of Driving Departure of Mining Autonomous Transport Vehicles Based on GRU Network 22

Prediction of Driving Departure of Mining Autonomous Transpo...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Li, Lecong Yu, Guizhen Li, Han Xia, Qi Zhang, Xiangyu Cai, Han School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

Open-pit mining areas have special geological structures, complex road networks, multi-rotation sections and poor road conditions, which bring many challenges to the operation of mining autonomous transport vehicles. Due to the large size and high control difficulty of mining autonomous transport vehicles, poor control effect and inaccurate steering mechanism implementation are prone to occur during the driving process, which leads to the vehicle departure from the reference path. To ensure the safety of autonomous operation in intelligent mine, this paper proposes a method for predicting driving departure of mining autonomous vehicles based on Gated Recurrent Unit (GRU). Firstly, the vehicle's historical trajectory data is obtained via on-board sensors, followed by data cleaning and normalization processes. key features are extracted, and a vehicle driving scene recognition module based on a GRU-based network is designed using deep learning. Subsequently, a GA-seq2seqGRU trajectory prediction module is constructed utilizing the scene recognition results, and the Genetic Algorithms (GA) is employed to tune the network hyper-parameters. Based on the predicted trajectories, the overall departure risk is calculated using two departure judgment methods based on cross-lane time and predicted lateral deviation, which are mapped to the departure level. Simulation experiments demonstrate that the accuracy of the driving scene recognition model in the proposed method in this paper reaches 0.9721, which is better than the 0.9585 of the Long Short Memory Neural Network (LSTM) model, and the trajectory prediction model based on the results of the driving scene recognition has a smaller RMSE value than the LSTM, GRU, and GA-seq2seqLSTM models when the prediction time domains are 1s, 2s, 3s, 4s, and 5s, and the departure detection module The average time consumed is 0.142ms. © 2024 IEEE.

关键词： Mine transportation

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Anomaly Detection and Fault Diagnosis Method for Autonomous Transport Vehicles on Unstructured Roads 22

Anomaly Detection and Fault Diagnosis Method for Autonomous ...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zhang, Yifang Yu, Guizhen Li, Han Zhang, Chaoqi Li, Lecong Zhang, Chuanying School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

Autonomous vehicles in mining areas undertake substantial production tasks and are prone to various faults during operation. Early detection of abnormalities, along with timely fault warnings and diagnoses, can enhance transportation safety and increase vehicle turnout rates. This study utilizes driving data from autonomous vehicles in mining areas and considers the characteristics of unstructured road scenes. The driving area is segmented into distinct intervals, and Kullback-Leibler (KL) divergence is applied within each interval to detect anomalies in the vehicle's lateral deviation during operation. Experimental results demonstrate that the proposed method achieves an anomaly detection accuracy of 91.4%, with a false negative rate of 8.3% and a false positive rate of 8.7%. © 2024 IEEE.

关键词： Vehicle detection

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Cable segmentation based on Mask2Former in open-pit mining area 22

Cable segmentation based on Mask2Former in open-pit mining a...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Wang, Liyun Zhou, Bin Yang, Songyue Liu, Wentao Li, Huazhi Sun, Shengdi School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

The development of unmanned transportation technology has improved operational efficiency and safety in open-pit mining areas. However, there remain significant challenges to be addressed. One pressing issue is the need for mining trucks to pass through the area where the cable is laid on the ground. Accidentally crushing or damaging these cables would lead to significant risk to the mining area operation. However, due to the complexity of the mining environment and the characteristics of cables being thin and curved, the existing methods such as edge segmentation are difficult to meet the requirements for cable segmentation. This paper introduces a cable segmentation method based on Mask2Former and carries out comprehensive experiments to examine the effectiveness of the method. Experimental results show that the proposed method achieves IoU of 66.04% and PA of 80.08%, which can meet the requirements of practical applications. © 2024 IEEE.

关键词： Mine trucks

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