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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
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A multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-02-23 Weidong Wang, Jun Peng, Wenbo Hu, Jin Wang, Xinyue Xu, Qasim Zaheer, Shi Qiu
Three‐dimensional displacement monitoring over long distances has been a long‐standing concern in the structural health monitoring industry. In this study, a multi‐degree‐of‐freedom slope displacement monitoring method is developed by fusing computer vision and the 3D point triangulation method. Attributed to this method, the problems of outdoor binocular camera calibration, multi‐target mismatching
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Structural performance‐based anomaly detection for velocity pulse Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-02-22 Lu Han, Zhengru Tao
Pulse‐like ground motion can cause extreme damage to long‐period structures. An automatic algorithm is proposed to identify pulse‐like ground motions, in which improved anomaly detection is applied and the structural performance is considered. To characterize the intrinsic pulse‐like features, the distance‐based anomaly detection algorithm is improved, and the relative cumulative energy is added to
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A rapid simplified method for determining tsunami inundation extent based on energy conservation Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-02-22 Tate Kimpton, Pablo Higuera, Colin Whittaker, Liam Wotherspoon, Conrad Zorn
This paper develops a tsunami inundation model, filling the current void between industry applied simplified methods (bathtub and attenuation) and comprehensive numerical modeling. The proposed model utilizes two‐dimensional equations established on hydraulic principles (energy conservation and friction loss) to produce the finite‐difference, two‐dimensional model. While the sophistication of depth‐averaged
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Flutter performance simulation on streamlined bridge deck with active aerodynamic flaps Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-02-19 Lin Zhao, Zilong Wang, Genshen Fang, Jie Zheng, Ke Li, Yaojun Ge
Active aerodynamic flaps can effectively improve the aerodynamic stability of bridges; however, the determination of optimal control parameters often requires a large number of experiments. This study proposes a method for determining the optimal control parameters of active flaps based on the surrogate model and computational fluid dynamics (CFD) simulation technology. The computational fluid dynamics
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Aftershock probabilistic seismic hazard analysis based on enhanced Bayesian network considering frequency information Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-02-14 Chang Liu, Dagang Lu
Bayesian network (BN) is an important tool in probabilistic seismic risk analysis (PSRA) due to its holistic nature and powerful probabilistic inference capabilities. However, while the information that can be stored by BN includes variable values, probability distributions, and relationships between variables, it does not involve event quantities. The results obtained from PSRA and probabilistic seismic
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Virtual-real-fusion simulation framework for evaluating and optimizing small-spatial-scale placement of cooperative roadside sensing units Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-02-06 Y. Ma, Y. B. Zheng, S. Y. Wang, Y. D. Wong, S. M. Easa
Roadside sensing units’ (RSUs) perception capability may be substantially impaired by occlusion issue even they work cooperatively. However, the joint influence of static and dynamic occlusions in real-life situations remains inadequately considered in optimizing RSUs’ placement. This study proposes a virtual-real-fusion simulation (VRFS) framework that combines traffic simulation and point clouds
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Vision-based fatigue crack automatic perception and geometric updating of finite element model for welded joint in steel structures Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-02-03 Tian Gao, Zhiyuan Yuanzhou, Bohai Ji, Zaipeng Xie
Digital twin requires establishing a self-updated model to simulate the structural damage perceived onsite. Despite the great success in damage identification and quantification, the difficulty in registration still limits the efficiency of model updating. This study presented a framework that enables a finite element (FE) model of welded joints to remesh itself for updating the geometric changes caused
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Deep reinforcement learning-based active mass driver decoupled control framework considering control–structure interaction effects Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-02-01 Hongcan Yao, Ping Tan, T. Y. Yang, Fulin Zhou
Control–structure interaction (CSI) plays a significant role in active control systems. Popular methods incorporate actuator dynamics into an integrated control system to account for CSI, leading to a situation where existing structural control algorithms that ignore CSI cannot be applied directly. To address this issue, this study proposes a deep reinforcement learning (DRL) based active mass driver
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Large-scale seismic soil–structure interaction analysis via efficient finite element modeling and multi-GPU parallel explicit algorithm Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-02-01 Mi Zhao, Qingpeng Ding, Shengtao Cao, Zhishan Li, Xiuli Du
As urban population increases, integrated underground–aboveground complexes are being constructed at growing paces in major cities. The seismic analysis of such complexes is crucial for the safety and functionality in the threat of potential earthquake disasters. However, fine-grained numerical modeling and analysis of such large and complex structures are still inefficient due to the consideration
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Improving single-stage activity recognition of excavators using knowledge distillation of temporal gradient data Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-01-29 Ali Ghelmani, Amin Hammad
Single-stage activity recognition methods have been gaining popularity within the construction domain. However, their low per-frame accuracy necessitates additional post-processing to link the per-frame detections. Therefore, limiting their real-time monitoring capabilities is an indispensable component of the emerging construction of digital twins. This study proposes knowledge DIstillation of temporal
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Chained machine learning model for predicting load capacity and ductility of steel fiber–reinforced concrete beams Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-01-31 Torkan Shafighfard, Farzin Kazemi, Faramarz Bagherzadeh, Magdalena Mieloszyk, Doo-Yeol Yoo
One of the main issues associated with steel fiber–reinforced concrete (SFRC) beams is the ability to anticipate their flexural response. With a comprehensive grid search, several stacked models (i.e., chained, parallel) consisting of various machine learning (ML) algorithms and artificial neural networks (ANNs) were developed to predict the flexural response of SFRC beams. The flexural performance
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Deep spatial-temporal embedding for vehicle trajectory validation and refinement Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-01-30 Tianya Terry Zhang, Peter J. Jin, Benedetto Piccoli, Mina Sartipi
High-angle cameras are commonly used for trajectory data collection in transportation research. However, without refinement and validation, trajectory data obtained through video processing software may be unreliable, inaccurate, or incomplete. This paper focuses on a critical issue in the field of trajectory data acquisition and analysis—there is still no reliable and fully vetted trajectory dataset
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A method for suspenders tension identification of bridges based on the spatio-temporal correlation between the girder strain and suspenders tension Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-01-30 Qianen Xu, Qingfei Gao, Yang Liu
In the actual structural health monitoring system of suspension bridges, only part of suspenders tension can be monitored, but not all the suspenders tension can be obtained. To solve this problem, a method for suspenders tension identification of bridges based on the spatio-temporal correlation between the girder strain and suspenders tension is proposed. By using actual monitoring data of vehicle
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Automated flatness assessment for large quantities of full-scale precast beams using laser scanning Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-01-26 Chang Xu, Wen Xiong, Pingbo Tang, C. S. Cai
Prefabrication has been widely used in bridge construction, for which precast beams are produced from a beam yard and constructed with a cast-in-suit bridge deck. The developments recently are focusing on large dimensions or large quantities of beam units, which leads to the inevitable challenge of beam quality control. Among them, beam surface flatness as one of the important indicators for manufacturing
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Experimentally informed modeling of the early-age stress evolution in cementitious materials using exponential conversion from creep to relaxation Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-01-26 Minfei Liang, Giovanni Di Luzio, Erik Schlangen, Branko Šavija
This study presents comprehensive numerical modeling methods for simulating early-age stress (EAS) relaxation in cementitious materials, based on the autogenous deformation (AD), elastic modulus, creep, and stress continuously tested by a mini temperature stress testing machine (Mini-TSTM) and a mini AD testing machine from a very early age (i.e., from a few hours to a week). Four methods for converting
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Damage index based on the strain-to-displacement relation for health monitoring of railway bridges Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-01-26 Said Quqa, Antonio Palermo, Alessandro Marzani
This paper proposes a novel damage index for railway bridges based on synchronous strain and displacement data collected at the passage of trains. The approach identifies a transformation operator that converts strains into displacements in a data-driven fashion without prior structural knowledge and with no parameter selection. The displacement prediction error is proposed as a robust damage index
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Community-level post-hazard functionality methodology for buildings exposed to floods Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-01-19 Omar Nofal, Nathanael Rosenheim, Sabarethinam Kameshwar, Jayant Patil, Xiangnan Zhou, John W. van de Lindt, Leonardo Duenas-Osorio, Eun Jeong Cha, Amin Endrami, Elaina Sutley, Harvey Cutler, Tao Lu, Chen Wang, Hwayoung Jeon
This paper presents a building-level post-hazard functionality model for communities exposed to flood hazards including the interdependencies between the population, buildings, and infrastructure. An existing portfolio of building archetypes is used to model the post-hazard physical flood functionality of different building typologies within the community with the goal of supporting resilience-informed
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Railway sleeper vibration measurement by train-borne laser Doppler vibrometer and its speed-dependent characteristics Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-01-11 Y. Zeng, A. Núñez, Z. Li
A train-borne laser Doppler vibrometer (LDV) directly measures the dynamic response of railway track components from a moving train, which has the potential to complement existing train-borne technologies for railway track monitoring. This paper proposes a holistic methodology to characterize train-borne LDV measurements by combining computer-aided approaches and real-life measurements. The focus is
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A hybrid virtual–real traffic simulation approach to reproducing the spatiotemporal distribution of bridge loads Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-01-10 Junyong Zhou, Wenrong Wu, Colin C. Caprani, Zeyin Tan, Bin Wei, Junping Zhang
Current traffic simulation approaches analyze vehicle loads and load effects from a statistical perspective; however, they fail to reproduce the spatiotemporal distribution of bridge loads and resultant load effects at every moment, hindering real-time bridge health management. This paper proposes a two-step hybrid virtual–real traffic simulation (HvrTS) approach to reproduce the spatiotemporal distribution
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Machine learning–informed soil conditioning for mechanized shield tunneling Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-01-10 Shuying Wang, Xiao Yuan, Tongming Qu
Effective soil conditioning is critical for mechanized shield tunneling, yet the selection of conditioning parameters remains experience-oriented. This study presents a machine learning–informed soil conditioning strategy, aiming at enabling automatic decision-making for soil conditioning during tunneling. The proposed procedure includes feature engineering to process raw data, the selection of an
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Enhancing point cloud semantic segmentation in the data-scarce domain of industrial plants through synthetic data Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-01-09 Florian Noichl, Fiona C. Collins, Alexander Braun, André Borrmann
Digitizing existing structures is essential for applying digital methods in architecture, engineering, and construction. However, the adoption of data-driven techniques for transforming point cloud data into useful digital models faces challenges, particularly in the industrial domain, where ground truth datasets for training are scarce. This paper investigates a solution leveraging synthetic data
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Biobjective optimization for railway alignment fine-grained designs with parallel existing railways Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-01-09 Yan Gao, Tianlong Zhang, Caiyiyi Zhu, Shusheng Yang, Paul Schonfeld, Kai Zou, Jialing Zhang, Ying Zhu, Ping Wang, Qing He
Urban high-speed railway construction is complex due to limited land resources, high population density, and potential construction risks, especially when new tracks are parallelly aligned to operational railways. Addressing a gap in current literature on fine optimization of manual alignment in such scenarios, this paper introduces a biobjective approximate fine-grained optimization model for railway
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Multi-view stereo for weakly textured indoor 3D reconstruction Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-01-06 Tao Wang, Vincent J. L. Gan
A 3D reconstruction enables an effective geometric representation to support various applications. Recently, learning-based multi-view stereo (MVS) algorithms have emerged, replacing conventional hand-crafted features with convolutional neural network-encoded deep representation to reduce feature matching ambiguity, leading to a more complete scene recovery from imagery data. However, the state-of-the-art
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Optimization-improved thermal–mechanical simulation of welding residual stresses in welded connections Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-01-03 Le Wang, Xudong Qian
This paper presents a novel computer-aided computational framework to determine the optimum shape parameters in a welding heat source model using a coupled supervised Gaussian process regression (GPR) and genetic algorithm (GA) approach in estimating the welding residual stresses. The experimental X-ray diffraction (XRD) approach validates the optimization-improved thermal–mechanical simulation method
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Urban wind field prediction based on sparse sensors and physics-informed graph-assisted auto-encoder Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-01-02 Huanxiang Gao, Gang Hu, Dongqin Zhang, Wenjun Jiang, K. T. Tse, K. C. S. Kwok, Ahsan Kareem
The urban flow wind field is a critical element for downstream research, such as mitigation of urban wind disasters, assessment of urban wind environment, and urban drone route planning. However, it is impractical to arrange a large number of sensors to monitor an urban wind flow field. Hence, acquiring the entire urban wind flow field via sparse sensors would be highly valuable. To date, no scheme
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Smart dynamic evacuation planning and online management using vehicular communication system Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2023-12-30 Hassan Idoudi, Mostafa Ameli, Cyril Nguyen Van Phu, Mahdi Zargayouna, Abderrezak Rachedi
During disasters, swiftly and efficiently evacuating populations in hazardous situations is crucial to minimize losses. This study proposes a novel framework to address dynamic population evacuation (DPE) problems, which includes planning and online evacuation management phases facilitated by vehicular communication. In the planning phase, a shelter allocation problem (SAP) is solved dynamically for
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Heterogeneity-oriented ensemble learning for rail monitoring based on vehicle-body vibration Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2023-12-28 Yong Zhuang, Rengkui Liu, Yuanjie Tang
In this study, railway vehicle-body vibration was applied to rail detection for convenient sensor deployment and cost-effectiveness. However, the waveform is difficult to analyze due to damping and interference. Data-driven methods can help concatenate multidimensional signals and complex rail-surface irregularities but are impressionably uncertain. This study proposes a method in which a deep learning
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Field-tested signal controller to mitigate spillover using trajectory data Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2023-12-28 Yu Han, Zhe Han, Fan Ding, Fuliang Li, Hao Wang, Xingmin Wang
Trajectory data from connected vehicles (CVs) provide a continuous and reliable means of obtaining information that can be leveraged to optimize traffic signals. This paper proposes a real-time traffic signal control method using CV trajectory data as the sole input. The primary goal of the proposed signal control method is to prevent queue spillover, which may significantly decrease the traffic efficiency
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Design and development of robotic collaborative system for automated construction of reciprocal frame structures Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2023-12-25 Cheav Por Chea, Yu Bai, Zhuomin Zhou
Robotic technologies have shown their potential to improve efficiency, precision, and safety for construction tasks. In this paper, the concept of design for robotic construction (DfRC) is introduced, and robotic collaborative systems are developed for the construction of load-carrying structures. An automated structural assembly was achieved and demonstrated through robotics with a preference for
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Monocular 3D object detection for construction scene analysis Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2023-12-20 Jie Shen, Lang Jiao, Cong Zhang, Keran Peng
Three-dimensional (3D) object detection, that is, localizing and classifying all critical objects in a 3D space, is essential for downstream construction scene analysis tasks. However, accurate instance segmentation, few 2D object segmentation and 3D object detection data sets, high-quality feature representations for depth estimation, and limited 3D cues from a single red-green-blue (RGB) image pose
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Inverse analysis of deformation moduli for high arch dams using the displacement reconstruction technique and multi-objective optimization Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2023-12-18 Zefa Li, Zhenyu Wu, Jiankang Chen, Yanling Li, Huibao Huang, Yu Lu, Xiang Lu, Junru Li
The inverse analysis of the deformation moduli of high arch dams based on displacement monitoring data is essential for structural safety assessment. In traditional inverse analysis methods, the deformation moduli are identified based on the single-objective optimization and the hydrostatic component derived from the statistical model. This type of method has two main shortcomings: First, it treats
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Real-time ergonomic risk assessment in construction using a co-learning-powered 3D human pose estimation model Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2023-12-18 Wang Chen, Donglian Gu, Jintao Ke
Work-related musculoskeletal disorders pose significant health risks to construction workers, making it essential to monitor their postures and identify physical exposure to mitigate these risks. This study presents a novel framework for real-time ergonomic risk assessment of workers in construction environments. Specifically, this study develops a lightweight human pose estimation (HPE) model with
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Generating network representations of small-scale infrastructure using generally available data Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2023-12-15 Aaron Dunton, Paolo Gardoni
Risk analysis (including resilience analysis) of infrastructure requires models that describe the connection of components and subsequent flow dynamics. However, the detailed information needed to define these models may not be available, especially for small-scale infrastructure that connect to every building. In this paper, we generate location-specific small-scale networks using detailed data that
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High-resolution model reconstruction and bridge damage detection based on data fusion of unmanned aerial vehicles light detection and ranging data imagery Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2023-12-12 Hongze Li, Yanli Chen, Jia Liu, Changtong Che, Ziyao Meng, Hang Zhu
Damage detection is essential for the maintenance of transportation infrastructure that experiences high daily traffic levels in potentially extreme environments and changes in use patterns. However, traditional physical inspection is always labor-intensive, subjective, and biased, lacking the objective perspective required for a comprehensive and reliable assessment. Recently, unmanned aerial vehicles
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A hierarchical control approach for virtual coupling in metro trains Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2023-12-11 Hongjie Liu, Xiaolin Luo, Tao Tang, Yang Zhang, Ming Chai
As an emerging technology, virtual coupling improves the efficiency and flexibility of metro services by forming multiple trains (units) as a virtually coupled train set (VCTS) without mechanical couplers. However, to realize the desired VCTS operation in practical metro services, a significant gap to be filled is that the implicit and nonlinear safety constraints are hard to be addressed in real-time
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Road crack detection interpreting background images by convolutional neural networks and a self-organizing map Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2023-12-08 Takahiro Yamaguchi, Tsukasa Mizutani
The presence of road cracks is an important indicator of damage. Deep learning is a prevailing method for detecting cracks in road surface images because of its detection ability. Previous research works focused on supervised convolutional neural networks (CNNs) without non-crack features or unsupervised crack analysis with limited accuracies. The novelty of this study is the addition of background
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On the role of time-of-use electricity price in charge scheduling for electric bus fleets Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2023-12-07 Le Zhang, Yadong Wang, Weihua Gu, Yu Han, Edward Chung, Xiaobo Qu
As electric buses become increasingly popular, it is imperative to optimize the schedules of electric buses with explicit consideration of their charging requirements. Unfortunately, existing studies failed to properly model the impacts of essential operating factors, including the time-of-use (TOU) electricity price, partial charging, and limited chargers. Our paper proposes a mixed-integer nonlinear
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A deep marked graph process model for citywide traffic congestion forecasting Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2023-12-07 Tong Zhang, Jianlong Wang, Tong Wang, Yiwei Pang, Peixiao Wang, Wangshu Wang
Forecasting citywide traffic congestion on large road networks has long been a nontrivial research problem due to the challenge of modeling complex evolution patterns of congestion in highly stochastic traffic environments. Arguing that purely data-driven methods may not perform well for congestion forecasting, we propose a deep marked graph process model for predicting the congestion indices and the