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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
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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