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Autonomous construction framework for crane control with enhanced soft actor–critic algorithm and real-time progress monitoring Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-30
Yifei Xiao, T. Y. Yang, Fan XieWith the shortage of skilled labors, there is an increasing demand for automation in the construction industry. This study presents an autonomous construction framework for crane control with enhanced soft actor–critic (SAC-E) algorithm and real-time progress monitoring. SAC-E is a novel reinforcement learning algorithm with superior learning speed and training stability for lifting path planning.
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Vehicle wheel load positioning method based on multiple projective planes Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-28
Kai Sun, Xu Jiang, Xuhong QiangComputer vision‐based vehicle load monitoring methods could obtain spatiotemporal data of vehicle loads, which is important for bridge monitoring and operation. However, during the process of vehicle detection and tracking, current research usually focuses on the vehicle as a whole, and there is a lack of research on the accurate positioning of vehicle wheel loads. For the fatigue analysis of orthotropic
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Reinforcement learning‐based trajectory planning for continuous digging of excavator working devices in trenching tasks Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-28
X. Tan, W. Wei, C. Liu, K. Cheng, Y. Wang, Z. Yao, Q. HuangThis paper addresses the challenge of real‐time, continuous trajectory planning for autonomous excavation. A hybrid method combining particle swarm optimization (PSO) and reinforcement learning (RL) is proposed. First, three types of excavation trajectories are defined for different geometric shapes of the digging area. Then, an excavation trajectory optimization method based on the PSO algorithm is
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Modeling the collective behavior of pedestrians with the spontaneous loose leader–follower structure in public spaces Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-27
Jie Xu, Dengyu Xu, Jing Wu, Xiaowei ShiGaining insights into pedestrian flow patterns in public spaces can greatly benefit decision‐making processes related to infrastructure planning. Interestingly, even pedestrians are unfamiliar with one another, they often follow others, drawing on positive information and engaging in a spontaneous collective behavior of pedestrians. To model this collective behavior, this paper proposed a social force‐based
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Enhanced three‐dimensional instance segmentation using multi‐feature extracting point cloud neural network Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-24
Hongxu Wang, Jiepeng Liu, Dongsheng Li, Tianze Chen, Pengkun Liu, Han Yan, Yadong WuPrecise three‐dimensional (3D) instance segmentation of indoor scenes plays a critical role in civil engineering, including reverse engineering, size detection, and advanced structural analysis. However, existing methods often fall short in accurately segmenting complex indoor environments due to challenges of diverse material textures, irregular object shapes, and inadequate datasets. To address these
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Issue Information Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-23
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Integrating a mortar model into discrete element simulation for enhanced understanding of asphalt mixture cracking Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-18
Gyalwang Dhundup, Jianing Zhou, Michael Bekoe, Lijun Sun, Sheng Mao, Yu YanCracks impact the performance and durability of asphalt pavements, necessitating a comprehensive understanding of the mixture cracking behavior. While discrete element modeling has been implemented, many studies oversimplify the simulation of asphalt mortar, a critical component affecting mixture cracking resistance. This study proposes a mortar model that is applicable to both two‐dimensional (2D)
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A noise‐based framework for randomly generating soil particle with realistic geometry Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-18
Chen‐Xi Tong, Jia‐Jun Li, Quan Sun, Sheng Zhang, Wan‐Huan Zhou, Daichao ShengParticle morphology influences the mechanical behavior of granular soils. Generating particles with realistic shapes for discrete element method simulations is gaining popularity. However, it is still challenging to efficiently generate very angular particles with less computational cost. Addressing this challenge, this paper introduces a novel noise‐based framework for generating realistic soil particle
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Automatic tiny crack positioning and width measurement with parallel laser line‐camera system Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-17
Chaobin Li, R. K. L. SuQuantifying tiny cracks is crucial for assessing structural conditions. Traditional non‐contact measurement technologies often struggle to accurately measure tiny crack widths, especially in hard‐to‐access areas. To address these challenges, this study introduces an image‐based, handheld parallel laser line‐camera (PLLC) system designed for automated tiny crack localization and width measurement from
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Theoretical analysis, simulation, and field experiment for vibration mitigation of suspender cables/hangers using the four‐wire pendulum tuned mass damper Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-15
Yonghui An, Siyuan Gong, Zhongzheng Wang, Wei Shen, Zhihao Wang, Jinping OuSuspender cables/hangers occupy a crucial role during the whole service life of suspension bridges/arch bridges/space structures, and their long‐term repeated vibration under corrosion and high‐stress service state will cause fatigue damage and even induce fatigue failure. To mitigate the vibration of the vertical suspender cables/hangers, a four‐wire pendulum tuned mass damper (FWPTMD) is proposed
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Automatic determination of 3D particle morphology from multiview images using uncertainty‐evaluated deep learning Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-14
Hongchen Liu, Huaizhi Su, Brian SheilParticle morphology is a crucial factor influencing the mechanical properties of granular materials particularly in infrastructure construction processes where accurate shape descriptors are essential. Accurately measuring three‐dimensional (3D) morphology has significant theoretical and practical value for exploring the multiscale mechanical properties of civil engineering materials. This study proposes
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A structure‐oriented loss function for automated semantic segmentation of bridge point clouds Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-13
Chao Lin, Shuhei Abe, Shitao Zheng, Xianfeng Li, Pang‐jo ChunFocusing on learning‐based semantic segmentation (SS) methods for bridge point cloud data (PCD), this study proposes a structure‐oriented concept (SOC) with training focused on the spatial distribution patterns of bridge components, including both the horizontally absolute location of each component and its vertically relative position compared with other components. Then a structure‐oriented loss
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Semi‐supervised pipe video temporal defect interval localization Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-10
Zhu Huang, Gang Pan, Chao Kang, YaoZhi LvIn sewer pipe closed‐circuit television inspection, accurate temporal defect localization is essential for effective pipe assessment. Industry standards typically do not require time interval annotations, which are more informative but lead to additional costs for fully supervised methods. Additionally, differences in scene types and camera motion patterns between pipe inspections and temporal action
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Evidential transformer for buried object detection in ground penetrating radar signals and interval‐based bounding box Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-08
Zheng Tong, Yiming Zhang, Tao MaThree‐dimensional (3D) buried object detection using ground penetrating radar (GPR) benefits from the powerful capacity of image‐wise deep neural networks. However, it still faces the challenge of information loss from raw GPR signals to two‐ and three‐dimensional images, such as the frequency‐domain information loss when normalizing GPR signals into gray‐scale images and spatial information loss when
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Evolution of clogging of porous asphalt concrete in the seepage process through integration of computer tomography, computational fluid dynamics, and discrete element method Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-08
Bo Li, Yunpeng Zhang, Dingbang Wei, Tengfei Yao, Yongping Hu, Hui DouThe longevity of porous asphalt pavement is inevitably compromised by the clogging of voids by various particles, leading to a degradation in its drainage function. Numerical simulations with real pore structures were used to investigate the clogging behavior of porous asphalt concrete (PAC) to clearly and intuitively understand its void clogging process. In this study, a three‐dimensional model of
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Generalization of anomaly detection in bridge structures using a vibration‐based Siamese convolutional neural network Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-08
Alireza Ghiasi, Zhen Zhang, Zijie Zeng, Ching Tai Ng, Abdul Hamid Sheikh, Javen Qinfeng ShiCorrosion is one of the main damages in steel bridges, which appears as a loss of material and sectional area and causes member failure over time. A reliable bridge management system not only should help in preventing catastrophic structural failure by employing an in‐time anomaly detection approach for all the bridges within a network but also should reduce overall network costs commonly raised by
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Privacy‐preserving awareness in sensor deployment for traffic flow surveillance Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-08
Ruru Hao, Shixiao Liang, Ziyang Zhai, Hang Zhou, Xin Wang, Xiaopeng Li, Tianhao GuanThe deployment of sensors to monitor traffic flow between origin–destination (OD) pairs, within a specified budget, remains a critical concern for both academic researchers and transportation managers. While these technologies are essential for capturing traffic data, the aspect of privacy has often been overlooked. To bridge this gap, this paper introduced the concept of privacy distance and then
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Resilience assessment of urban rail transit stations considering disturbance and time‐varying passenger flow Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-08
Xiaowei Liu, Jinqu Chen, Bo Du, Xu Yan, Qiyuan Peng, Jun ShenUnlike most urban rail transit (URT) resilience studies on URT lines or networks under major disturbances, this paper focuses on the resilience assessment of URT stations under high‐frequency daily disturbances with minor impacts. A resilience assessment metric with different resilience levels is proposed, which is calculated based on multiple criteria, including the number of delayed passengers, degree
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A universal geography neural network for mobility flow prediction in planning scenarios Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-07
Jifu Guo, Shengguang Bai, Xun Li, Kai Xian, Erjian Liu, Wenting Ding, Xizhi MaThis study primarily focuses on generating mobility flow in regions and cities, which plays an important role in urban planning and management. The majority of existing mobility flow models, including conventional statistical models and deep learning‐based models, are heavily dependent on historical data to predict future mobility flows. The application of these models poses significant challenges
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Issue Information Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-07
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Traffic estimation in work zones using a custom regression model and data augmentation Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-06
Ali Hassandokht Mashhadi, Abbas Rashidi, Masoud Hamedi, Nikola MarkovićAccurately estimating traffic volumes in construction work zones is crucial for effective traffic management. However, one of the key challenges transportation agencies face is the limited coverage of continuous count station (CCS) sensors, which are often sparsely located and may not be positioned directly on roads where construction work zones are present. This spatial limitation leads to gaps in
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Hybrid‐data‐driven bridge weigh‐in‐motion technology using a two‐level sequential artificial neural network Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-06
Wangchen Yan, Hao Ren, Xin Luo, Shaofan LiFor existing bridge weigh‐in‐motion technologies, the main challenge in accurate weight estimation is to overcome the difficulty of identifying the closely spaced axles. To do so, many field test data are generally required for each bridge in application. To address such a challenge, a novel two‐level sequential artificial neural network (ANN) model trained by the hybrid simulated‐experimental data
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Multi‐stage detection of warped ceiling panel using ensemble vision models for automated localization and quantification Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-04
Qinghua Guo, Weihang Gao, Qingzhao Kong, Xilin LuSuspended ceiling systems constitute a pivotal non‐structural component in buildings, and the warping of panels not only compromises the seismic performance but also affects the functional integrity. This paper proposes a novel multi‐stage warped panel detection (MWPD) method to automatically locate warped panels from two‐dimensional images and quantify their deformation. First, the Deep Hough Transform
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Geometry physics neural operator solver for solid mechanics Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-04
Chawit Kaewnuratchadasorn, Jiaji Wang, Chul‐Woo Kim, Xiaowei DengThis study developed Geometry Physics neural Operator (GPO), a novel solver framework to approximate the partial differential equation (PDE) solutions for solid mechanics problems with irregular geometry and achieved a significant speedup in simulation time compared to numerical solvers. GPO leverages a weak form of PDEs based on the principle of least work, incorporates geometry information, and imposes
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A graph attention reasoning model for prefabricated component detection Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-02
Manxu Zhou, Guanting Ye, Ka‐Veng Yuen, Wenhao Yu, Qiang JinAccurately checking the position and presence of internal components before casting prefabricated elements is critical to ensuring product quality. However, traditional manual visual inspection is often inefficient and inaccurate. While deep learning has been widely applied to quality inspection of prefabricated components, most studies focus on surface defects and cracks, with less emphasis on the
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Issue Information Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2025-01-01
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A feature‐based pavement image registration method for precise pavement deterioration monitoring Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-12-31
Zhongyu Yang, Mohsen Mohammadi, Haolin Wang, Yi‐Chang (James) TsaiOver the past decade, pavement imaging systems, particularly 3D laser technology, have been widely adopted by transportation agencies for network‐level pavement condition evaluations. State Highway Agencies, including Georgia Department of Transportation (DOT), Florida DOT, and Texas DOT, have been collecting pavement images for over 5 years. However, these multi‐year pavement images have not been
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Automatic steel girder inspection system for high‐speed railway bridge using hybrid learning framework Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-12-26
Tao Xu, Yunpeng Wu, Yong Qin, Sihui Long, Zhen Yang, Fengxiang GuoThe steel girder of high‐speed railway bridges requires regular inspections to ensure bridge stability and provide a safe environment for railway operations. Unmanned aerial vehicle (UAV)‐based inspection has great potential to become an efficient solution by offering superior aerial perspectives and mitigating safety concerns. Unfortunately, classic convolutional neural network (CNN) models suffer
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Infrared thermography and 3D pavement surface unevenness measurement algorithm for damage assessment of concrete bridge decks Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-12-25
Mikiko Yamashita, Koichi Kawanishi, Kenji Hashizume, Pang‐jo ChunDeterioration of the concrete deck surface, including disintegration and delamination between the deck slab and pavement, presents significant challenges in bridge maintenance due to its hidden nature and the risk it poses to the deck's durability as damage progresses. Early detection is critical for preventing issues such as pothole formation and ensuring long‐term durability. However, traditional
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Automatic classification of near‐fault pulse‐like ground motions Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-12-25
Hongwu Yang, Yingmin Li, Weihao Pan, Lei Hu, Shuyan JiThis study presents an automated, quantitative classification method for near‐fault pulse‐like ground motions, distinguishing between forward‐directivity and fling‐step (FS) motions. The method introduces two novel parameters—the pulse velocity ratio and pulse area ratio—which transform the classification standard from a qualitative to a quantitative framework. Combined with an enhanced pulse extraction
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Issue Information Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-12-23
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Research on autonomous path planning and tracking control methods for unmanned electric shovels Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-12-21
Xiaodan Tan, Guoqiang Wang, Guohua Wu, Zongwei Yao, Yongpeng Wang, Qingxue HuangAchieving fully unmanned operations in large‐scale excavating machinery relies on robust autonomous driving capabilities. Electric shovels, with their steering limitations and reversing difficulties, present unique challenges, compared to lighter, high‐speed‐tracked vehicles. This paper explores these operational and technical challenges and introduces a trajectory planning scheme combining the Guidance‐Hybrid
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Uncertainty‐informed regional deformation diagnosis of arch dams Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-12-20
Xudong Chen, Wenhao Sun, Shaowei Hu, Liuyang Li, Chongshi Gu, Jinjun Guo, Bowen Wei, Bo XuAccurately predicting dam deformation is crucial for understanding its operational status. However, existing models struggle to effectively capture the spatiotemporal correlations in monitoring data and quantify uncertainty within dam systems. This paper presents an innovative uncertainty quantification model for evaluating regional deformation in arch dams. First, a method to extract the spatiotemporal
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Two‐step rapid inspection of underwater concrete bridge structures combining sonar, camera, and deep learning Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-12-17
Weihao Sun, Shitong Hou, Gang Wu, Yujie Zhang, Luchang ZhaoUnderwater defects in piers pose potential hazards to the safety and durability of river‐crossing bridges. The concealment and difficulty in detecting underwater defects often result in their oversight. Acoustic methods face challenges in directly achieving accurate measurements of underwater defects, while optical methods are time‐consuming. This study proposes a two‐step rapid inspection method for
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A semi‐supervised approach for building wall layout segmentation based on transformers and limited data Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-12-14
Hao Xie, Xiao Ma, Qipei Mei, Ying Hei ChuiIn structural design, accurately extracting information from floor plan drawings of buildings is essential for building 3D models and facilitating design automation. However, deep learning models often face challenges due to their dependence on large labeled datasets, which are labor and time‐intensive to generate. And floor plan drawings often present challenges, such as overlapping elements and similar
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Training of construction robots using imitation learning and environmental rewards Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-12-13
Kangkang Duan, Zhengbo Zou, T. Y. YangConstruction robots are challenging the paradigm of labor‐intensive construction tasks. Imitation learning (IL) offers a promising approach, enabling robots to mimic expert actions. However, obtaining high‐quality expert demonstrations is a major bottleneck in this process as teleoperated robot motions may not align with optimal kinematic behavior. In this paper, two innovations have been proposed
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Genetic algorithm optimized frequency‐domain convolutional blind source separation for multiple leakage locations in water supply pipeline Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-12-13
Hongjin Liu, Hongyuan Fang, Xiang Yu, Yangyang XiaIn the realm of using acoustic methods for locating leakages in water supply pipelines, existing research predominantly focuses on single leak localization, with limited exploration into the challenges posed by multiple leak scenarios. To address this gap, a genetic algorithm‐optimized frequency‐domain convolutional blind source separation algorithm is proposed for the precise localization of multiple
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Integrating spatial and channel attention mechanisms with domain knowledge in convolutional neural networks for friction coefficient prediction Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-12-10
Zihang Weng, Chenglong Liu, Yuchuan Du, Difei Wu, Zhen LengThe pavement skid resistance is crucial for ensuring driving safety. However, the reproducibility and comparability of field measurements are constrained by various influencing factors. One solution to these constraints is utilizing laser‐based 3D pavement data, which are notably stable and can be employed to estimate pavement skid resistance indirectly. However, the integration of tire–road friction
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A K‐Net‐based deep learning framework for automatic rock quality designation estimation Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-12-10
Sihao Yu, Louis Ngai Yuen WongRock quality designation (RQD) plays a crucial role in the design and analysis of rock engineering. The traditional method of measuring RQD relies on manual logging by geologists, which is often labor‐intensive and time‐consuming. Thus, this study presents an autonomous framework for expeditious RQD estimation based on two‐dimensional corebox photographs. The scale‐invariant feature transform (SIFT)
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Event‐based supervisor control for a cyber‐physical waterway lock system Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-12-09
D. G. Fragkoulis, F. N. Koumboulis, M. P. Tzamtzi, P. G. TotomisAn event‐based supervisory control scheme, in the Ramdage–Wonham framework, will be proposed for the cyber‐physical Waterway Lock system, known as Lock III, in Tilburg, the Netherlands. The proposed control scheme imposes desired behavior, by appropriately disabling controllable events, so as to avoid activation of actuator commands that may lead to undesired and potentially hazardous operating states
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Uncertainty‐guided U‐Net for soil boundary segmentation using Monte Carlo dropout Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-12-09
X. Zhou, B. Sheil, S. Suryasentana, P. ShiAccurate soil stratification is essential for geotechnical engineering design. Owing to its effectiveness and efficiency, the cone penetration test (CPT) has been widely applied for subsurface stratigraphy, which relies heavily on empiricism for correlations to soil type. Recently, deep learning techniques have shown great promise in learning the relationship between CPT data and soil boundaries automatically
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Computational modeling of reinforced concrete dapped‐end beams Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-12-04
Danilo D'Angela, Gennaro Magliulo, Chiara Di Salvatore, Edoardo CosenzaThe structural response of reinforced concrete dapped‐end beams is simulated through finite element analysis. The case study consists in experimental tests performed in the framework of an Italian research project on bridges. The study assesses both the local and global behavior of the beam and characterizes the damage patterns. A blind prediction is initially performed inputting the main basic material
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Cover Image, Volume 39, Issue 24 Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-12-04
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Cover Image, Volume 39, Issue 24 Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-12-04
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Issue Information Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-12-04
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Coupled lattice discrete particle model for the simulation of water and chloride transport in cracked concrete members Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-11-30
Yingbo Zhu, Dongge Jia, John C. Brigham, Alessandro FascettiA novel coupled mechanical and mass transport lattice discrete particle model is developed to quantitatively assess the impact of cracks on the mass transport properties in concrete members subjected to short‐ and long‐term loading conditions. In the developed approach, two sets of dual lattice networks are generated: one to resolve the mechanical response and another for mass transport analysis. The
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Aeroelastic force prediction via temporal fusion transformers Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-11-28
Miguel Cid Montoya, Ashutosh Mishra, Sumit Verma, Omar A. Mures, Carlos E. Rubio‐MedranoAero‐structural shape design and optimization of bridge decks rely on accurately estimating their self‐excited aeroelastic forces within the design domain. The inherent nonlinear features of bluff body aerodynamics and the high cost of wind tunnel tests and computational fluid dynamics (CFD) simulations make their emulation as a function of deck shape and reduced velocity challenging. State‐of‐the‐art
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Machine learning-aided prediction of windstorm-induced vibration responses of long-span suspension bridges Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-11-25
Alireza Entezami, Hassan SarmadiLong-span suspension bridges are significantly susceptible to windstorm-induced vibrations, leading to critical challenges of field measurements along with multicollinearity and nonlinearity between wind features and bridge dynamic responses. To address these issues, this article proposes an innovative machine learning-assisted predictive method by integrating a predictor selector developed from regularized
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A bridge point cloud databank for digital bridge understanding Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-11-25
Hongwei Zhang, Yanjie Zhu, Wen Xiong, C. S. CaiDespite progress in automated bridge point cloud segmentation based on deep learning, challenges persist. For instance, the absence of a public point cloud dataset specifically designed for bridge instances, and the existing bridge point cloud datasets display a lack of diversity in bridge types and inconsistency in component labeling. These factors may hinder the further improvement of accuracy in
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Automated seismic event detection considering faulty data interference using deep learning and Bayesian fusion Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-11-21
Zhiyi Tang, Jiaxing Guo, Yinhao Wang, Wei Xu, Yuequan Bao, Jingran He, Youqi ZhangStructural health monitoring (SHM) aims to assess civil infrastructures' performance and ensure safety. Automated detection of in situ events of interest, such as earthquakes, from extensive continuous monitoring data, is important to ensure the timeliness of subsequent data analysis. To overcome the poor timeliness of manual identification and the inconsistency of sensors, this paper proposes an automated
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Reinforcement learning‐based approach for urban road project scheduling considering alternative closure types Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-11-20
S. E. Seilabi, M. Saneii, M. Pourgholamali, M. Miralinaghi, S. LabiGrowth in urban population, travel, and motorization continue to cause an increased need for urban projects to expand road capacity. Unfortunately, these projects also cause travel delays, emissions, driver frustration, and other road user adversities. To alleviate these ills, road agencies often face two work zone design choices: close the road fully and re‐reroute traffic or implement partial closure
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Smartphone-based high durable strain sensor with sub-pixel-level accuracy and adjustable camera position Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-11-19
Pengfei Wu, Bo Lu, Huan Li, Weijie Li, Xuefeng ZhaoComputer vision strain sensors typically require the camera position to be fixed, limiting measurements to surface deformations of structures at pixel-level resolution. Also, sensors have a service term significantly shorter than the designed service term of the structures. This paper presents research on a high durable computer vision sensor, microimage strain sensing (MISS)-Silica, which utilizes
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Cover Image, Volume 39, Issue 23 Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-11-18
The cover image is based on the Article A multi-phase mechanical model of biochar–cement composites at the mesoscale by Muduo Li et al., https://doi.org/10.1111/mice.13307.
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Issue Information Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-11-18
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A multi‐perspective fusion model for operating speed prediction on highways using knowledge‐enhanced graph neural networks Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-11-18
Jianqiang Gao, Bo Yu, Yuren Chen, Kun Gao, Shan BaoThis study proposes a multi‐perspective fusion model for operating speed prediction based on knowledge‐enhanced graph neural networks, named RoadGNN‐S. By utilizing message passing and multi‐head self‐attention mechanisms, RoadGNN‐S can effectively capture the coupling impacts of multi‐perspective alignment elements (i.e., two‐dimensional design, 2.5‐dimensional driving, and three‐dimensional spatial
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Adaptive compensation using long short‐term memory networks for improved control performance in real‐time hybrid simulation Comput. Aided Civ. Infrastruct. Eng. (IF 8.5) Pub Date : 2024-11-16
Zhenfeng Lai, Yanhui Liu, Zhipeng Zhai, Jiajun ZhangReal‐time hybrid simulation (RTHS) divides structural systems into numerical and experimental substructures, providing a cost‐effective solution for analyzing structural systems, especially those that are large or complex. However, the actuation systems between these substructures inevitably introduce delays, affecting the stability and accuracy of RTHS. To address this issue, this study proposes an