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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 9.6) 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
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Earthquake damage detection and level classification method for wooden houses based on convolutional neural networks and onsite photos Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) 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
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A lightweight feature attention fusion network for pavement crack segmentation Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) 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
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Constraint‐aware optimization model for plane truss structures via single‐agent gradient descent Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) 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
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Displacement sensing based on microscopic vision with high resolution and large measuring range Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) 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
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Crack pattern–based machine learning prediction of residual drift capacity in damaged masonry walls Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) 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
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A causal discovery approach to study key mixed traffic‐related factors and age of highway affecting raveling Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) 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
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Cover Image, Volume 39, Issue 10 Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-05-02
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A smoothness control method for kilometer‐span railway bridges with analysis of track characteristics Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) 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
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A dynamic graph deep learning model with multivariate empirical mode decomposition for network‐wide metro passenger flow prediction Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) 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
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Component‐level point cloud completion of bridge structures using deep learning Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-26 Gen Matono, Mayuko Nishio
Point cloud of existing bridges provides important applications in their maintenance and management, such as to the three‐dimensional (3D) model creation. However, point cloud data acquired in actual bridges are caused missing parts due to occlusions and limitations in sensor placements. This study proposes a learning method to realize the point cloud completion of such structure: the component‐wise
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Aggregation formulation for on‐site multidepot vehicle scheduling scenario Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-26 Yi Gao, Yuanjie Tang, Rengkui Liu
The multidepot vehicle scheduling problem (MDVSP) is a fundamental public transport challenge. To address the large‐scale model and inherent solution symmetry associated with the traditional trip‐to‐trip connection‐based approach for MDVSP, a new trip‐to‐route (T2R) connection‐based approach is proposed. Considering real‐world problem characteristics with numerous trips sharing common origin–destination
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Rapid pedestrian‐level wind field prediction for early‐stage design using Pareto‐optimized convolutional neural networks Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-26 Alfredo Vicente Clemente, Knut Erik Teigen Giljarhus, Luca Oggiano, Massimiliano Ruocco
Traditional computational fluid dynamics (CFD) methods used for wind field prediction can be time‐consuming, limiting architectural creativity in the early‐stage design process. Deep learning models have the potential to significantly speed up wind field prediction. This work introduces a convolutional neural network (CNN) approach based on the U‐Net architecture, to rapidly predict wind in simplified
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Lightweight object detection network for multi‐damage recognition of concrete bridges in complex environments Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-26 Tianyong Jiang, Lingyun Li, Bijan Samali, Yang Yu, Ke Huang, Wanli Yan, Lei Wang
To solve the challenges of low recognition accuracy, slow speed, and weak generalization ability inherent in traditional methods for multi‐damage recognition of concrete bridges, this paper proposed an efficient lightweight damage recognition model, constructed by improving the you only look once v4 (YOLOv4) with MobileNetv3 and fused inverted residual blocks, named YOLOMF. First, a novel lightweight
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Multistage charging facility planning on the expressway coordinated with the power structure transformation Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-25 Tian‐yu Zhang, En‐jian Yao, Yang Yang, Hong‐Ming Yang, Dong‐bo Guo, David Z. W. Wang
This study presents a novel multistage expressway fast charging station (EFCS) planning problem coordinated with the dynamic regional power structure (PS) transformation. Under the prerequisite of the EFCS network's sustainable operation, network accessibility, and orderly construction, a three‐step planning method oriented to the enhancement of energy saving and emission reduction (ESER) benefits
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An intelligent optimization method for the facility environment on rural roads Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-24 Weixi Ren, Bo Yu, Yuren Chen, Kun Gao, Shan Bao, Zhixuan Wang, Yuting Qin
This study develops an intelligent optimization method of the facility environment (i.e., road facilities and surrounding landscapes) from drivers’ visual perception to adjust operation speeds on rural roads. Different from previous methods that heavily rely on expert experience and are time‐consuming, this method can rapidly generate optimized visual images of the facility environment and promptly
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Virtual trial assembly of large steel members with bolted connections based on multiscale point cloud fusion Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-24 Zeyu Zhang, Dong Liang, Haibin Huang, Lu Sun
Virtual trial assembly (VTA) using 3D laser scanning as the digital carrier can overcome the shortcomings of time‐consuming and costly physical preassembly. However, its application in large steel structures with bolted connections remains limited. First, this study introduces a novel approach for acquiring multiscale point cloud data of large steel members using terrestrial laser scanners (TLSs) and
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Transformer‐based framework for accurate segmentation of high‐resolution images in structural health monitoring Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-21 M. Azimi, T. Y. Yang
High‐resolution image segmentation is essential in structural health monitoring (SHM), enabling accurate detection and quantification of structural components and damages. However, conventional convolutional neural network‐based segmentation methods face limitations in real‐world deployment, particularly when handling high‐resolution images producing low‐resolution outputs. This study introduces a
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Material augmented semantic segmentation of point clouds for building elements Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-17 Houhao Liang, Justin K. W. Yeoh, David K. H. Chua
Point clouds are utilized to enable automated engineering applications for their ability to represent spatial geometry. However, they inherently lack detailed surface textures, posing challenges in differentiating objects at the texture level. Hence, this study introduces a 2D–3D fusing approach, leveraging material properties recognized from registered images as an augmented feature to enhance deep
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An efficient Bayesian method with intrusive homotopy surrogate model for stochastic model updating Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-16 Hui Chen, Bin Huang, Heng Zhang, Kaiyi Xue, Ming Sun, Zhifeng Wu
This paper proposes a new stochastic model updating method based on the homotopy surrogate model (HSM) and Bayesian sampling. As a novel intrusive surrogate model, the HSM is established by the homotopy stochastic finite element (FE) method. Then combining the advanced delayed‐rejection adaptive Metropolis–Hastings sampling technology with HSM, the structural FE model can be updated by uncertain measurement
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Integrated corridor management by cooperative traffic signal and ramp metering control Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-16 Abdullah Al Farabi, Rasool Mohebifard, Ramin Niroumand, Ali Hajbabaie, Mohammed Hadi, Lily Elefteriadou
This paper formulates a cooperative traffic control methodology that integrates traffic signal timing and ramp metering decisions into an optimization model to improve traffic operations in a corridor network. A mixed integer linear model is formulated and is solved in real time within a model predictive controller framework, where the cell transmission model is used as the system state predictor.
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Traffic prediction via clustering and deep transfer learning with limited data Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-15 Xiexin Zou, Edward Chung
This paper proposes a method based on the clustering algorithm, deep learning, and transfer learning (TL) for short‐term traffic prediction with limited data. To address the challenges posed by limited data and the complex and diverse traffic patterns observed in traffic networks, we propose a profile model based on few‐shot learning to extract each detector's unique profiles. These profiles are then
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A lightweight Transformer‐based neural network for large‐scale masonry arch bridge point cloud segmentation Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-15 Yixiong Jing, Brian Sheil, Sinan Acikgoz
Transformer architecture based on the attention mechanism achieves impressive results in natural language processing (NLP) tasks. This paper transfers the successful experience to a 3D point cloud segmentation task. Inspired by newly proposed 3D Transformer neural networks, this paper introduces a new Transformer‐based module, which is called Local Geo‐Transformer. To alleviate the heavy memory consumption
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Data‐driven out‐of‐order model for synchronized planning, scheduling, and execution in modular construction fit‐out management Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-12 Yishuo Jiang, Mingxing Li, Benedict Jun Ma, Ray Y. Zhong, George Q. Huang
Fit‐out operations in modular construction exhibit unique features, such as limited room space and diversly distributed operations in the building. These features pose significant challenges to planning, scheduling, and execution (PSE) of fit‐out activities due to operational parallelism, distributional diversity, and narrower constrained time window in modular construction. Hence, logistics‐operation
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Estimation of load for tunnel lining in elastic soil using physics‐informed neural network Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-11 G. Wang, Q. Fang, J. Wang, Q. M. Li, J. Y. Chen, Y. Liu
A reverse calculation method termed soil and lining physics‐informed neural network (SL‐PINN) is proposed for the estimation of load for tunnel lining in elastic soil based on radial displacement measurements of the tunnel lining. To achieve efficient and accurate calculations, the framework of SL‐PINN is specially designed to consider the respective displacement characteristics of surrounding soil
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Smartphone‐based method for measuring maximum peak tensile and compressive strain Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-10 Xixian Chen, Huan Li, Chenhao Zhao, Guangyi Zhou, Weijie Li, Xue Zhang, Xuefeng Zhao
This paper proposes an innovative smartphone‐based strain sensing method (named MaxCpture) for measuring maximum peak tensile and compressive strains. The MaxCpture method is able to record the maximum peak strain of a structure without continuous power supply and real‐time monitoring. This method combines the maximum peak strain sensor, a smartphone, and the microimage sensing algorithm. Crucially
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Context‐aware hand gesture interaction for human–robot collaboration in construction Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-08 Xin Wang, Dharmaraj Veeramani, Fei Dai, Zhenhua Zhu
Construction robots play a pivotal role in enabling intelligent processes within the construction industry. User‐friendly interfaces that facilitate efficient human–robot collaboration are essential for promoting robot adoption. However, most of the existing interfaces do not consider contextual information in the collaborative environment. The situation where humans and robots work together in the
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AI‐enabled airport runway pavement distress detection using dashcam imagery Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-05 Arman Malekloo, Xiaoyue Cathy Liu, David Sacharny
Maintaining airport runways is crucial for safety and efficiency, yet traditional monitoring relies on manual inspections, prone to time consumption and inaccuracy. This study pioneers the utilization of low‐cost dashcam imagery for the detection and geolocation of airport runway pavement distresses, employing novel deep‐learning frameworks. A significant contribution of our work is the creation of
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Parallel heterogeneous data‐fusion convolutional neural networks for improved rail bridge strike detection Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-04 Hussam Khresat, Jase D. Sitton, Brett A. Story
Low clearance rail bridges provide vital crossings for freight and passenger trains and are susceptible to frequent strikes from overheight vehicles or equipment. Impact detection systems can help ensure the safety of railroad bridges and their users; such systems streamline monitoring efforts by providing near real‐time strike notifications to rail managers responsible for assessing a bridge after
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A response‐compatible ground motion generation method using physics‐guided neural networks Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-01 Youshui Miao, Hao Kang, Wei Hou, Yang Liu, Yixin Zhang, Cheng Wang
Selecting or generating ground motions (GMs) that elicit seismic responses matching specific standards or expected benchmarks for nonlinear time‐history analysis (NLTHA) is crucial for ensuring the rationality of structural seismic design and analysis. Typical GM inputs for NLTHA, either natural or artificial, are normally spectrum‐compatible, which may produce significant variations in analysis results
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Automated building damage assessment and large‐scale mapping by integrating satellite imagery, GIS, and deep learning Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-29 Abdullah M. Braik, Maria Koliou
Efficient and accurate building damage assessment is crucial for effective emergency response and resource allocation following natural hazards. However, traditional methods are often time consuming and labor intensive. Recent advancements in remote sensing and artificial intelligence (AI) have made it possible to automate the damage assessment process, and previous studies have made notable progress
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Multi‐network coordinated charging infrastructure planning for the self‐sufficient renewable power highway Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-28 Tian‐Yu Zhang, En‐Jian Yao, Yang Yang, Hong‐Ming Yang, David Z. W. Wang
Developing a self‐sufficient renewable power (RP) road transport (SRPRT) system is an important future direction for transport–energy integration. More well‐developed studies must be conducted on the coordinated planning of transport, power supply, and power generation networks. This paper carries out the joint operation and planning of highway charging networks with the wind‐photovoltaic‐energy storage
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Autoencoder‐based method to assess bridge health monitoring data quality Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-25 Bowen Xiao, Jin Di, Jie Wang, Guanliang Wu, Jiapeng Shi, Xiaohai Wang, Jiuhong Fan
The data quality determines the reliability of big data‐based bridge condition assessments. However, rapidly discerning data conditions and identifying low‐quality data segments pose considerable challenges. This study introduces a transformer‐based autoencoder neural network for rapid data quality assessment in bridge health monitoring. The average Euclidean distance was used to quantify the dispersion
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Shovel point optimization for unmanned loader based on pile reconstruction Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-20 Guanlong Chen, Yakun Wang, Xue Li, Qiushi Bi, Xuefei Li
This study details an advanced shovel point optimization system for unmanned loaders, crucial for efficient shovelling operations. First, the shovel point evaluation index is established with reference to the driver's experience. Second, a novel method for pile profile reconstruction is proposed, utilizing a trained neural network to detect piles and extracting the point cloud using LiDAR and camera
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Prior knowledge‐infused neural network for efficient performance assessment of structures through few‐shot incremental learning Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-12 Shi‐Zhi Chen, De‐Cheng Feng, Ertugrul Taciroglu
Structural seismic safety assessment is a critical task in maintaining the resilience of existing civil and infrastructures. This task commonly requires accurate predictions of structural responses under stochastic intensive ground accelerations via time‐costly numerical simulations. While numerous studies have attempted to use machine learning (ML) techniques as surrogate models to alleviate this
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Autonomous flight strategy of an unmanned aerial vehicle with multimodal information for autonomous inspection of overhead transmission facilities Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-12 Munsu Jeon, Joonhyeok Moon, Siheon Jeong, Ki‐Yong Oh
This study proposes an innovative method for achieving autonomous flight to inspect overhead transmission facilities. The proposed method not only integrates multimodal information from novel sensors but also addresses three essential aspects to overcome the existing limitations in autonomous flights of an unmanned aerial vehicle (UAV). First, a novel deep neural network architecture titled the rotational
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In‐fleet structural health monitoring of roadway bridges using connected and autonomous vehicles’ data Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-11 Hoofar Shokravi, Mohammadreza Vafaei, Bijan Samali, Norhisham Bakhary
Drive‐by structural health monitoring (SHM) is a cost‐efficient alternative to the direct SHM of short‐ to medium‐size bridges requiring no sensors to be installed on the structure. However, drive‐by SHM is generally known as a short‐term monitoring technique due to the challenges associated with using multiple passages of instrumented vehicles for a long time. This paper proposes combining the potentiality
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Advancing the white phase mobile traffic control paradigm to consider pedestrians Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-11 Ramin Niroumand, Leila Hajibabai, Ali Hajbabaie
Current literature on joint optimization of intersection signal timing and connected automated vehicle (CAV) trajectory mostly focuses on vehicular movements paying no or little attention to pedestrians. This paper presents a methodology to safely incorporate pedestrians into signalized intersections with CAVs and connected human‐driven vehicles (CHVs). The movements of vehicles are controlled using
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A multiscale model for wood combustion Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-08 H. L. Hao, R. Y. Qin, C. L. Chow, D. Lau
Understanding wood combustion has become increasingly critical as fire safety engineering moves toward a performance‐based approach to building design. Although different kinetic models have been developed for wood burning, chemical kinetics remains a significant challenge for accurate prediction. This work has developed a novel multiscale model by implementing kinetic parameters calculated from molecular
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Thermal contraction coordination behavior between unbound aggregate layer and asphalt mixture overlay based on the finite difference and discrete element coupling method Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-05 Tongtong Wan, Hainian Wang, Xu Yang, Yu Chen, Lian Li, Aboelkasim Diab
The constraint action of the unbound aggregate layer underneath plays an important role in affecting the temperature strains in the top asphalt layer. The focus of the present paper is to investigate the interactive thermal contraction mechanisms between the asphalt mixture and granular base layers to offer a new perspective in promoting the understanding of the thermal cracking disease. In this paper
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Federated learning–based global road damage detection Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-05 Poonam Kumari Saha, Deeksha Arya, Yoshihide Sekimoto
Deep learning is widely used for road damage detection, but it requires extensive, diverse, and well‐labeled data. Centralized model training can be difficult due to large data transfers, storage needs, and computational resources. Data privacy concerns can also hinder data sharing among clients, leaving them to train models on their own data, leading to less robust models. Federated learning (FL)
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A controllable generative model for generating pavement crack images in complex scenes Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-04 Hancheng Zhang, Zhendong Qian, Wei Zhou, Yitong Min, Pengfei Liu
Existing crack recognition methods based on deep learning often face difficulties when detecting cracks in complex scenes such as brake marks, water marks, and shadows. The inadequate amount of available data can be primarily attributed to this factor. To address this issue, a controllable generative model of pavement cracks is proposed that can generate crack images in complex scenes by leveraging
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Augmented reality-based method for road maintenance operators in human–robot collaborative interventions Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-02 A. C. Bavelos, E. Anastasiou, N. Dimitropoulos, G. Oikonomou, S. Makris
Road maintenance operators often work in dangerous environments and are in need of a support system to enhance their safety and efficiency. Augmented reality (AR) has proven to be useful in providing support to operators in various industrial sectors. However, the vast majority of the existing applications focus mainly on static, controlled environments, such as industrial shopfloors, although the
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A traffic state prediction method based on spatial–temporal data mining of floating car data by using autoformer architecture Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-02-29 Shuangzhi Yu, Jiankun Peng, Yuming Ge, Xinlian Yu, Fan Ding, Shen Li, Charlie Ma
Floating car data (FCD), characterized by wide spatiotemporal coverage, low collection cost, and immunity to adverse weather conditions, are one of the key approaches for intelligent transportation systems to obtain real‐time urban road network traffic information. The research aims to utilize GPS data from taxis in Shanghai and vector geographic information data of the road network, with urban expressways
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Unmanned aerial vehicle–human collaboration route planning for intelligent infrastructure inspection Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-02-28 Yue Pan, Linfeng Li, Jianjun Qin, Jin‐Jian Chen, Paolo Gardoni
Motivated by the strengths of unmanned aerial vehicle (UAV), the UAV–human collaboration route planning (UHCRP) for intelligent infrastructure inspection is a problem worthy of discussion to help reduce human costs and minimize the risk of noninspected infrastructures under limited resources. To facilitate UHCRP, this paper proposes a novel deep reinforcement learning (DRL)‐based approach to well handle
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Image segmentation using Vision Transformer for tunnel defect assessment Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-02-24 Shaojie Qin, Taiyue Qi, Tang Deng, Xiaodong Huang
Existing tunnel detection methods include crack and water‐leakage segmentation networks. However, if the automated detection algorithm cannot process all defect cases, manual detection is required to eliminate potential risks. The existing intelligent detection methods lack a universal method that can accurately segment all types of defects, particularly when multiple defects are superimposed. To address
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Real‐time displacement measurement for long‐span bridges using a compact vision‐based system with speed‐optimized template matching Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-02-23 Miaomin Wang, Fuyou Xu, Ki‐Young Koo, Pinqing Wang
This paper introduces a new accelerating algorithm, efficient match slimmer (EMS), specifically designed to lighten computational loads of sophisticated template matching algorithms, enabling these algorithms to be effectively run on single‐board computers. Utilizing EMS in conjunction with a robust template matching algorithm, we have developed Raspberry Vision—a compact, cost‐effective, and real‐time