![](https://scdn.x-mol.com/css/images/icon-new-link.png)
样式: 排序: IF: - GO 导出 标记为已读
-
An efficient static solver for the lattice discrete particle model Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-16 Dongge Jia, John C. Brigham, Alessandro Fascetti
The lattice discrete particle model (LDPM) has been proven to be one of the most appealing computational tools to simulate fracture in quasi‐brittle materials. Despite tremendous advancements in the definition and implementation of the method, solution strategies are still limited to dynamic algorithms, resulting in prohibitive computational costs and challenges related to solution accuracy for quasi‐static
-
A non‐contact identification method of overweight vehicles based on computer vision and deep learning Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-12 Daoheng Li, Meiyu Liu, Lu Yang, Han Wei, Jie Guo
The phenomenon of overweight vehicles severely threatens traffic safety and the service life of transportation infrastructure. Rapid and effective identification of overweight vehicles is of significant importance for maintaining the healthy operation of highways and bridges and ensuring the safety of people's lives and property. With the problems of high cost and low efficiency, the traditional vehicle
-
Multiresolution dynamic mode decomposition approach for wind pressure analysis and reconstruction around buildings Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-11 Reda Snaiki, Seyedeh Fatemeh Mirfakhar
Accurate wind pressure analysis on high‐rise buildings is critical for wind load prediction. However, traditional methods struggle with the inherent complexity and multiscale nature of these data. Furthermore, the high cost and practical limitations of deploying extensive sensor networks restrict the data collection capabilities. This study addresses these limitations by introducing a novel framework
-
Physics‐informed neural operator solver and super‐resolution for solid mechanics Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-11 Chawit Kaewnuratchadasorn, Jiaji Wang, Chul‐Woo Kim
Physics‐Informed Neural Networks (PINNs) have solved numerous mechanics problems by training to minimize the loss functions of governing partial differential equations (PDEs). Despite successful development of PINNs in various systems, computational efficiency and fidelity prediction have remained profound challenges. To fill such gaps, this study proposed a Physics‐Informed Neural Operator Solver
-
Deep learning‐based segmentation model for permeable concrete meso‐structures Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-09 De Chen, Yukun Li, Jiaxing Tao, Yuchen Li, Shilong Zhang, Xuehui Shan, Tingting Wang, Zhi Qiao, Rui Zhao, Xiaoqiang Fan, Zhongrong Zhou
The meso‐structure of pervious concrete significantly influences its overall performance. Accurately identifying the meso‐structure of pervious concrete is imperative for optimizing the design of pervious concrete, considering its mechanical properties and functionality. Therefore, to address the difficulty of recognizing the meso‐structures of pervious concrete, a method utilizing deep learning image
-
Optimizing green splits in high‐dimensional traffic signal control with trust region Bayesian optimization Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-08 Yunhai Gong, Shaopeng Zhong, Shengchuan Zhao, Feng Xiao, Wenwen Wang, Yu Jiang
Centralized traffic signal control has long been a challenging, high‐dimensional optimization problem. This study establishes a simulation‐based optimization framework and develops a novel optimization algorithm based on trust region Bayesian optimization (TuRBO), which can efficiently obtain an approximate optimal solution to the high‐dimensional traffic signal control problem. Local Gaussian process
-
Corrigendum to “Deep spatial‐temporal embedding for vehicle trajectory validation and refinement” Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-08
Zhang, T. T., Jin, P. J., Piccoli, B., & Sartipi, M. (2024). Deep spatial-temporal embedding for vehicle trajectory validation and refinement. Computer-Aided Civil and Infrastructure Engineering, 39, 1597−1615. https://doi.org/10.1111/mice.13160 In the “Methodology” section, Equation (2) “” was incorrect. The correct equation should have been written as “” In the “Methodology” section, Equation (3)
-
Automatic generation of architecture drawings from point clouds Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-07 Fengyu Zhang, Qingzhao Kong, Cheng Yuan, Peizhen Li
Traditional methods for producing architectural drawings require extensive manual labor. This paper proposes an automated method for generating a comprehensive set of three‐view drawings, including the standardized labeling of doors and annotation of dimensions and areas. The output drawings are software‐readable and editable, and the method is applicable to intricate structures with non‐orthogonal
-
Ego‐planning‐guided multi‐graph convolutional network for heterogeneous agent trajectory prediction Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-07 Zihao Sheng, Zilin Huang, Sikai Chen
Accurate prediction of the future trajectories of traffic agents is a critical aspect of autonomous vehicle navigation. However, most existing approaches focus on predicting trajectories from a static roadside perspective, ignoring the influence of autonomous vehicles’ future plans on neighboring traffic agents. To address this challenge, this paper introduces EPG‐MGCN, an ego‐planning‐guided multi‐graph
-
Cover Image, Volume 39, Issue 14 Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-03
-
-
Cover Image, Volume 39, Issue 14 Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-03
-
Automated signal‐based evaluation of dynamic cone resistance via machine learning for subsurface characterization Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-07-01 Samuel Olamide Aregbesola, Yong‐Hoon Byun
Dynamic cone resistance (DCR) is a recently introduced soil resistance index that has the unit of stress. It is determined from the dynamic response at the tip of an instrumented dynamic cone penetrometer. However, DCR evaluation is generally a manual, time‐consuming, and error‐prone process. Thus, this study investigates the feasibility of determining DCR using a stacked ensemble (SE) machine learning
-
A rendering-based lightweight network for segmentation of high-resolution crack images Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-06-23 Honghu Chu, Diran Yu, Weiwei Chen, Jun Ma, Lu Deng
High-resolution (HR) crack images provide detailed structural assessments crucial for maintenance planning. However, the discrete nature of feature extraction in mainstream deep learning algorithms and computational limitations hinder refined segmentation. This study introduces a rendering-based lightweight crack segmentation network (RLCSN) designed to efficiently predict refined masks for HR crack
-
Computing‐efficient video analytics for nighttime traffic sensing Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-06-28 Igor Lashkov, Runze Yuan, Guohui Zhang
The training workflow of neural networks can be quite complex, potentially time‐consuming, and require specific hardware to accomplish operation needs. This study presents a novel analytical video‐based approach for vehicle tracking and vehicle volume estimation at nighttime using a monocular traffic surveillance camera installed over the road. To build this approach, we employ computer vision‐based
-
Modeling of spatially embedded networks via regional spatial graph convolutional networks Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-06-20 Xudong Fan, Jürgen Hackl
Efficient representation of complex infrastructure systems is crucial for system‐level management tasks, such as edge prediction, component classification, and decision‐making. However, the complex interactions between the infrastructure systems and their spatial environments increased the complexity of network representation learning. This study introduces a novel geometric‐based multimodal deep learning
-
Rapid measurement method for cable tension of cable‐stayed bridges using terrestrial laser scanning Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-06-20 Yin Zhou, Hong Zhang, Xingyi Hu, Jianting Zhou, Jinyu Zhu, Jingzhou Xin, Jun Yang
This study proposes a new method for the rapid and non‐contact measurement of cable forces in cable‐stayed bridges, including a cable force calculation method based on measured cable shapes and a batch acquisition method for the true shape of cables. First, a nonlinear regression model for estimating cable forces based on measured cable shapes is established, and a Gauss–Newton‐based cable force solving
-
Collaborative optimization of intersection signals and speed guidance for buses run on overlapping route segments under connected environment Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-06-17 Chengcheng Yang, Sheng Jin, Wenbin Yao, Donglei Rong, Congcong Bai, Jérémie Adjé Alagbé
In order to reduce bus bunching in overlapping route segments and improve the efficiency of bus operation, a dynamic scheduling model is proposed to adjust bus operation states by adopting a cooperative strategy involving multi-line bus timetable optimization, arterial signal control, and speed guidance. Based on mixed integer linear programming, an arterial signal coordination model with autonomous
-
Nationwide synthetic human mobility dataset construction from limited travel surveys and open data Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-06-10 Takehiro Kashiyama, Yanbo Pang, Yuya Shibuya, Takahiro Yabe, Yoshihide Sekimoto
In recent years, the explosion of extensive geolocated datasets related to human mobility has presented an opportunity to unravel the mechanism behind daily mobility patterns on an individual and population level; this analysis is essential for solving social matters, such as traffic forecasting, disease spreading, urban planning, and pollution. However, the release of such data is limited owing to
-
Quantum-enhanced machine learning technique for rapid post-earthquake assessment of building safety Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-06-10 Sanjeev Bhatta, Ji Dang
Fast, accurate damage assessment of numerous buildings for large areas is vital for saving lives, enhancing decision-making, and expediting recovery, thereby increasing urban resilience. The traditional methods, relying on expert mobilization, are slow and unsafe. Recent advances in machine learning (ML) have improved assessments; however, quantum-enhanced ML (QML), a rapidly advancing field, offers
-
Cover Image, Volume 39, Issue 13 Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-06-09
-
Hybrid structural analysis integrating physical model and continuous-time state-space neural network model Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-06-07 Hong-Wei Li, Shuo Hao, Yi-Qing Ni, You-Wu Wang, Zhao-Dong Xu
The most likely scenario for civil engineering structures is that only some components or parts of a structure are complex, while the rest of the structure can be well physically modeled. In this case, utilizing powerful neural networks to model these complex components or parts only and embedding the neural network models into the structure might be a viable choice. However, few studies have considered
-
Urban risk assessment model to quantify earthquake-induced elevator passenger entrapment with population heatmap Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-06-07 Donglian Gu, Ning Zhang, Zhen Xu, Yongjingbang Wu, Yuan Tian
The seismic resilience of cities plays a crucial role in achieving the United Nations Sustainability Development Goal. However, despite the occurrence of elevator passenger entrapment in numerous earthquakes, there is a notable lack of studies addressing this sophisticated issue. This study aims to bridge this gap by proposing a novel urban risk assessment model designed to evaluate city-scale earthquake-induced
-
A physics-informed deep reinforcement learning framework for autonomous steel frame structure design Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-06-06 Bochao Fu, Yuqing Gao, Wei Wang
As artificial intelligence technology advances, automated structural design has emerged as a new research focus in recent years. This paper combines finite element method (FEM) and deep reinforcement learning (DRL) to establish a physics-informed framework, named FrameRL, for automated steel frame structure design. FrameRL models the design process of steel frames as a reinforcement learning (RL) process
-
A bi-level emergency evacuation traffic optimization model for urban evacuation problem Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-06-06 Yanyue Liu, Zhao Zhang, Lei Mo, Bin Yu, Zhenhua Li
This paper introduces a pioneering bi-level emergency evacuation traffic optimization model (BEETOM), crafted to expedite the evacuation process within urban road networks. The innovative upper-level model offers simultaneous optimization of evacuation departure times and routes, while the lower-level model focuses on refining traffic signal timing to mitigate delays and queue formation across intersections
-
365-day sectional work zone schedule optimization for road networks considering economies of scale and user cost Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-06-06 Yuto Nakazato, Daijiro Mizutani
This study proposes a methodology for deriving the optimal work zone schedule for the annual routine maintenance planning in an infrastructure asset management system considering the (i) economies of scale in work zone costs due to work zone synchronization and (ii) user costs across the road network with traffic assignments. A key aspect of the proposed methodology is the ability to derive in detail
-
Cover Image, Volume 39, Issue 12 Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-06-04
-
Railroad missing components detection via cascade region-based convolutional neural network with predefined proposal templates Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-06-04 Youzhi Tang, Yi Wang, Yu Qian
In the field of railway infrastructure maintenance, timely and accurate detection of component anomalies is crucial for safety and efficiency. This paper presents the Cascade Region-based convolutional neural network with Predefined Proposal Templates (CR-PPT), an innovative method for railroad components inspection in complex railway infrastructure using edge-computing devices. Unlike previous systems
-
Deep Q-network learning-based active speed management under autonomous driving environments Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-06-03 Kawon Kang, Nuri Park, Juneyoung Park, Mohamed Abdel-Aty
Efficient traffic safety management necessitates real-time crash risk prediction using expressway characteristics. With the emergence of autonomous vehicles (AVs), the development and evaluation of variable speed limit (VSL) strategies, a key active traffic management technique, become crucial for enhancing safety and mobility in mixed traffic flows. This underscores the need for optimized VSL strategies
-
Deep neural network based time–frequency decomposition for structural seismic responses training with synthetic samples Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-06-03 Xiaohang Zhou, Qixuan Li, Ranting Cui, Xuan Zhu
Time–frequency decomposition is a powerful tool in assessing the dynamic behaviors of structures. Traditional time–frequency decomposition methods struggle with adaptability, and are limited in handling the structural responses with strong nonlinearity and closely spaced modes. In this study, a cutting-edge approach based on deep neural network (DNN) is proposed to achieve a precise and adaptive time–frequency
-
Attention-optimized 3D segmentation and reconstruction system for sewer pipelines employing multi-view images Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-06-03 Duo Ma, Niannian Wang, Hongyuan Fang, Weiwei Chen, Bin Li, Kejie Zhai
Existing deep learning-based defect inspection results on images lack depth information to fully demonstrate the sewer, despite their high accuracy. To address this limitation, a novel attention-optimized three-dimensional (3D) segmentation and reconstruction system for sewer pipelines is presented. First, a real-time sewer segmentation method called AM-Pipe-SegNet is developed to inspect defects (i
-
Snow- or ice-covered road detection in winter road surface conditions using deep neural networks Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-06-03 Yuya Moroto, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama
Traffic accidents occur frequently in cold and snow- or ice-covered regions due to weather changes that occur during the winter season. To detect the snow- or ice-covered roads in road surface conditions, road surface images captured using fixed-point cameras installed along the route are sufficient. This paper proposes a snow- or ice-covered road detection method that uses the deep convolutional autoencoding
-
A learning‐based method for optimal dynamic privileged parking permit policy Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-31 Yun Yuan, Yitong Li, Xin Li, Xin Wang
The privileged permit service can be provided as an alternative to the conventional meter and reserved services in the off‐street parking lots. In view of the unbalanced demand and the simplistic off‐street parking lot management, this paper proposes a novel parking management problem for setting up and withdrawing the temporary permit‐only policy. To optimize the access rule regarding uncertainty
-
A Lagrangian relaxation approach for resource allocation problem with capacity constraints Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-31 Demetra Protogyrou, Leila Hajibabai
This study evaluates a capacitated facility location model enhanced with distance constraints for an emergency response problem, ensuring certain neighborhoods remain within an accessible range from facilities following a hurricane. The proposed model takes into account the capacity constraints for drones and vehicles. The model determines optimal locations for facilities and the distribution of supplies
-
A visual inspection and diagnosis system for bridge rivets based on a convolutional neural network Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-30 Tengjiao Jiang, Gunnstein T. Frøseth, Anders Rønnquist, Xuan Kong, Lu Deng
Rivets are critical mechanical fasteners in steel bridges, and rivet defects may cause catastrophic failure. This study proposes a convolutional neural network (CNN)‐based inspection system for fast rivet identification and diagnosis. Rivet states are classified as normal, rusted, loose, and missing. A CNN‐based training workflow was introduced to develop a reliable rivet diagnosis system. A multiscale
-
Optimizing multiple equipment scheduling for U‐shaped automated container terminals considering loading and unloading operations Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-29 Xiang Zhang, Ziyan Hong, Haoning Xi, Jingwen Li
U‐shaped automated container terminals (ACTs) represent a strategic design in port infrastructure that facilitates simultaneous loading and unloading operations. This paper addresses the challenges of scheduling multiple types of equipment, such as dual trolley quay cranes (DTQCs), automated guided vehicles (AGVs), double cantilever rail cranes (DCRCs), and external trucks (ETs) in U‐shaped ACTs. This
-
Sidewalk‐based bicycle path network design incorporating equity in cycling time Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-28 Yutong Cai, Ghim Ping Ong, Qiang Meng
Many cities find it difficult to claim enough land to build dedicated bicycle lanes. In response, this study proposes a novel framework to design a bicycle path network based on the existing sidewalks where selected sidewalk links are converted into eligible bicycle paths. The output will be a subset of the sidewalk links chosen to be converted to eligible bicycle paths with minimum cost such that
-
Integrated urban land cover analysis using deep learning and post‐classification correction Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-28 Lapone Techapinyawat, Aaliyah Timms, Jim Lee, Yuxia Huang, Hua Zhang
The quantification of urban impervious area has important implications for the design and management of urban water and environmental infrastructure systems. This study proposes a deep learning model to classify 15‐cm aerial imagery of urban landscapes, coupled with a vector‐oriented post‐classification processing algorithm for automatically retrieving canopy‐covered impervious surfaces. In a case
-
A simulation‐based approach for optimizing the placement of dedicated lanes for autonomous vehicles in large‐scale networks Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-28 Ehsan Kamjoo, Alireza Rostami, Fatemeh Fakhrmoosavi, Ali Zockaie
This study introduces a framework to maximize societal benefits associated with the autonomous vehicle (AV)‐dedicated lane implementation at large‐scale transportation networks, considering the travel time savings and the required investments to prepare the infrastructure for their deployment. To this end, a bi‐level optimization problem is formulated. The upper level determines the links for dedicated
-
Deep learning framework with Local Sparse Transformer for construction worker detection in 3D with LiDAR Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-27 Mingyu Zhang, Lei Wang, Shuai Han, Shuyuan Wang, Heng Li
Autonomous equipment is playing an increasingly important role in construction tasks. It is essential to equip autonomous equipment with powerful 3D detection capability to avoid accidents and inefficiency. However, there is limited research within the construction field that has extended detection to 3D. To this end, this study develops a light detection and ranging (LiDAR)‐based deep‐learning model
-
Two‐stage nonparametric framework for missing data imputation, uncertainty quantification, and incorporation in system identification Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-27 Wen‐Jing Zhang, Ka‐Veng Yuen, Wang‐Ji Yan
In many engineering applications, missing data during system identification can hinder the performance of the identified model. In this paper, a novel two‐stage nonparametric framework is proposed for missing data imputation, uncertainty quantification, and its integration in system identification with reduced computational complexity. The framework does not require functional forms for both the imputation
-
A geometric‐identification–free mathematical model for recreating nonsymmetric horizontal railway alignments Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-22 Miguel E. Vázquez‐Méndez, Gerardo Casal, Alberte Castro, Duarte Santamarina
The constant passage of trains on the railways tracks causes, in the course of time, deviations that must be corrected periodically by means of a track calibration process. It consists of designing a new layout, called recreated horizontal alignment (RHA), as close as possible to the deformed center track fulfilling also the technical constraints according to the operational requirements of the railway
-
-
Cover Image, Volume 39, Issue 11 Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-21
-
A generative adversarial network approach for removing motion blur in the automatic detection of pavement cracks Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-20 Yu Zhang, Lin Zhang
Advancements in infrastructure management have significantly benefited from automatic pavement crack detection systems, relying on image processing enhanced by high‐resolution imaging and machine learning. However, image and motion blur substantially challenge the accuracy of crack detection and analysis. Nevertheless, research on mitigating motion blur remains sparse. This study introduces an effective
-
A multisource data‐driven monitoring model for assessing concrete dam behavior Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-17 Kefu Yao, Zhiping Wen, Chenfei Shao, Jiaquan Yang, Huaizhi Su
The pivotal role of dam infrastructure necessitates continuous health monitoring, which results in extensive sets of data. Most monitoring data‐based models in dam engineering concentrate on predicting dam behavior. However, little attention has been systematically paid to the processing of extensive monitoring data, modeling of comprehensive dam behavior, and assessment of overall dam operation status
-
A multi‐agent reinforcement learning model for maintenance optimization of interdependent highway pavement networks Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-17 L. Yao, Z. Leng, J. Jiang, F. Ni
Pavement segments are functionally interdependent under traffic equilibrium, leading to interdependent maintenance and rehabilitation (M&R) decisions for different segments, but it has not received significant attention in the pavement management community yet. This study developed a maintenance optimization model for interdependent pavement networks based on the simultaneous network optimization (SNO)
-
Intelligent design of shear wall layout based on diffusion models Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-17 Yi Gu, Yuli Huang, Wenjie Liao, Xinzheng Lu
This study explores artificial intelligence (AI) for shear wall layout design, aiming to overcome challenges in data feature sparsity and the complexity of drawing representations in existing AI‐based methods. We pioneer an innovative method leveraging the potential of diffusion models, establishing a suitable drawing representation, and examining the impact of various conditions. The proposed image‐prompt
-
Wear diagnosis for rail profile data using a novel multidimensional scaling clustering method Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-15 D. Shang, Shuai Su, Y. K. Sun, F. Wang, Y. Cao, W. F. Yang, P. Li, J. H. Zhou
The diagnosis of railway system faults is significant for its comfort, efficiency, and safety. The rail surface wear is the key impact factor when considering the health conditions of rails. This paper accomplishes contactless rail wear diagnosis by using multidimensional scaling based on a novel informational dissimilarity measure (IDM) to cluster intact and different worn rail profile data. The IDM
-
A hybrid method for intercity transport mode identification based on mobility features and sequential relations mined from cellular signaling data Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-14 Fan Ding, Yongyi Zhang, Jiankun Peng, Yuming Ge, Tao Qu, Xingyuan Tao, Jun Chen
The proliferation of mobile phones has generated vast quantities of cellular signaling data (CSD), covering extensive spatial areas and populations. These data, containing spatiotemporal information, can be employed to identify and analyze intercity transport modes, providing valuable insights for understanding travel distribution and behavior. However, CSD are primarily intended for communication
-
Earthquake damage detection and level classification method for wooden houses based on convolutional neural networks and onsite photos Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-13 Kai Wu, Masashi Matsuoka, Haruki Oshio
The results of earthquake damage certification (EDC) surveys are the basis of support measures for improving the lives of disaster victims. To address issues such as a limited workforce to perform EDC surveys and difficulties in judging the level of damage, a damage detection and level classification method for wooden houses using multiple convolutional neural network models is proposed. The proposed
-
A lightweight feature attention fusion network for pavement crack segmentation Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-08 Yucheng Huang, Yuchen Liu, Fang Liu, Wei Liu
The occurrence of pavement cracks poses a significant potential threat to road safety, thus the rapid and accurate acquisition of pavement crack information is of paramount importance. Deep learning methods have the capability to offer precise and automated crack detection solutions based on crack images. However, the slow detection speed and huge model size in high‐accuracy models are still the main
-
Constraint‐aware optimization model for plane truss structures via single‐agent gradient descent Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-08 Jun Su Park, Taehoon Hong, Dong‐Eun Lee, Hyo Seon Park
This study introduces the constraint‐aware optimization model (CAOM), a novel optimization framework designed to optimize the size, shape, and topology of plane truss structures simultaneously. Unlike traditional optimization models, which rely on gradient descent and frequently struggle with managing various constraints due to their dependence on a single optimization agent, CAOM effectively addresses
-
Displacement sensing based on microscopic vision with high resolution and large measuring range Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-07 Pengfei Wu, Weijie Li, Xuefeng Zhao
Microimage strain sensing (MISS) is a novel piston‐type sensor based on microscopic vision. In this study, optical disc slice is used as information carriers to improve MISS. There are multiple pits on the surface of an optical disc. By using machine vision algorithms, the pits can be converted into digital information, making them scales for recording displacements. By this means, we proposed a sensing
-
-
Crack pattern–based machine learning prediction of residual drift capacity in damaged masonry walls Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-02 Mauricio Pereira, Antonio Maria D'Altri, Stefano de Miranda, Branko Glisic
In this paper, we present a method based on an ensemble of convolutional neural networks (CNNs) for the prediction of residual drift capacity in unreinforced damaged masonry walls using as only input the crack pattern. We use an accurate block‐based numerical model to generate mechanically consistent crack patterns induced by external actions (earthquake‐like loads and differential settlements). For
-
A causal discovery approach to study key mixed traffic‐related factors and age of highway affecting raveling Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-02 Zili Wang, Panchamy Krishnakumari, Kumar Anupam, Hans van Lint, Sandra Erkens
The relationship between real‐world traffic and pavement raveling is unclear and subject to ongoing debates. This research proposes a novel approach that extends beyond traditional correlation analyses to explore causal mechanisms between mixed traffic and raveling. This approach incorporates the causal discovery method, and is applied to five Dutch porous asphalt (PA) highway sites that have substantial
-
Cover Image, Volume 39, Issue 10 Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-05-02
-
A smoothness control method for kilometer‐span railway bridges with analysis of track characteristics Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-04-30 Yuxiao Zhang, Jin Shi, Shehui Tan, Yingjie Wang
Significant dynamic deformations during the operation of kilometer‐span high‐speed railway bridges adversely affect track maintenance. This paper proposes a three‐stage smoothness control method based on a comprehensive analysis of track alignment characteristics to address this issue. In the method, historical measured data are grouped into multicategories, and reference alignments for each category
-
A dynamic graph deep learning model with multivariate empirical mode decomposition for network‐wide metro passenger flow prediction Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-04-30 Hao Huang, Jiannan Mao, Leilei Kang, Weike Lu, Sijia Zhang, Lan Liu
Network‐wide short‐term passenger flow prediction is critical for the operation and management of metro systems. However, it is challenging due to the inherent non‐stationarity, nonlinearity, and spatial–temporal dependencies within passenger flow. To tackle these challenges, this paper introduces a hybrid model called multi‐scale dynamic propagation spatial–temporal network (MSDPSTN). Specifically