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Human-centered intelligent construction for sustainable cities Autom. Constr. (IF 9.6) Pub Date : 2024-09-21 Hyuna Kang, Hakpyeong Kim, Juwon Hong, Jaewon Jeoung, Minhyun Lee, Taehoon Hong
Automatic technologies are a developing trend in the construction industry and have emerged by leveraging intelligent technologies. In automated construction, a human-centered approach to construction management is crucial as it improves productivity, safety, and sustainability by focusing on the needs of construction workers and building occupants. Therefore, focusing on sustainable construction,
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Automatic assessment of concrete cracks in low-light, overexposed, and blurred images restored using a generative AI approach Autom. Constr. (IF 9.6) Pub Date : 2024-09-21 Pengwei Guo, Xiangjun Meng, Weina Meng, Yi Bao
Deep learning-based computer vision techniques have high efficiency in assessing concrete cracks from images, and the assessment can be automated using robots for higher efficiency. However, assessment accuracy is often compromised by low-quality images. This paper presents a Conditional Generative Adversarial Network (CGAN)-based approach to restore low-light, overexposed, and blurred images. The
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Tracking multiple construction workers using pose estimation and feature-assisted re-identification model Autom. Constr. (IF 9.6) Pub Date : 2024-09-21 Nasrullah Khan, Syed Farhan Alam Zaidi, Muhammad Sibtain Abbas, Doyeop Lee, Dongmin Lee
Tracking construction workers is crucial for ensuring worker safety, productivity, appropriate resource allocation, and regulatory compliance. However, when multiple workers resemble each other or temporary obstructions occur, maintaining accurate identification of individual workers with computer-vision-based tracking techniques is challenging. This paper proposes a multi-worker tracking framework
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Anomaly detection of cracks in synthetic masonry arch bridge point clouds using fast point feature histograms and PatchCore Autom. Constr. (IF 9.6) Pub Date : 2024-09-21 Yixiong Jing, Jia-Xing Zhong, Brian Sheil, Sinan Acikgoz
Management of ageing masonry arch bridges entails periodic site inspections to identify signs of potential structural degradation. Previous research has focused on detecting surface cracks from images. This paper develops an alternative approach where cracks are identified from point clouds via geometric distortions. An image-based anomaly detection method called PatchCore is customized for 3D applications
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Intelligent design of key joints in aerial building machine using topology optimization and generative adversarial network Autom. Constr. (IF 9.6) Pub Date : 2024-09-21 Zhuang Xia, Jiaqi Wang, Yongsheng Li, Limao Zhang, Changyong Liu
Joints are crucial connections in an aerial building machine (ABM), yet they often undergo experience-based local optimization design. This paper presents an intelligent design method for key joints in the ABM using a generative adversarial network (GAN), aiming to achieve new and superior global optimization schemes. A database of topology-optimized structures is fed into the boundary equilibrium
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Bolt loosening angle measurement along full range of screw exposure length based on 3D point cloud Autom. Constr. (IF 9.6) Pub Date : 2024-09-20 Shengyuan Li, Yushan Le, Jiachen Gao, Xian Li, Xuefeng Zhao
The existing two-dimensional (2D) vision-based bolt loosening measurement range is generally limited to 0–60°. To overcome this limitation, a bolt loosening angle measurement method along full range of screw exposure length based on three-dimensional (3D) point cloud is proposed. Initially, 3D point clouds of bolt groups were reconstructed using 2D images under 18 working conditions, and the 3D point
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Multi-scalar robotic fabrication system for on-site press gluing in multi-storey timber buildings Autom. Constr. (IF 9.6) Pub Date : 2024-09-20 Nils Opgenorth, Daniel Nunes Locatelli, Samuel Leder, Hans Jakob Wagner, Achim Menges
The amount of timber construction has increased significantly in recent decades due to the development of digital processing technologies in prefabrication. However, on site, timber components are still commonly assembled manually and connected with low performance joint types. This paper presents a multi-storey timber building system with a co-designed heterogeneous multi-scalar robotic construction
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Effectiveness of alarm sounds in preventing operator habituation to auditory warnings in construction equipment Autom. Constr. (IF 9.6) Pub Date : 2024-09-19 Jeonghyeun Chae, Sungjoo Hwang, Youngcheol Kang
A proximity auditory warning (AW) system has been developed to mitigate accidents involving construction equipment blind spots. However, frequent AW sounds can lead to habituation among construction equipment operators, potentially reducing the system's effectiveness. This paper assessed the effectiveness of AW sounds in mitigating habituation. Twenty-one participants underwent a driving simulation
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Vision-based construction robot for real-time automated welding with human-robot interaction Autom. Constr. (IF 9.6) Pub Date : 2024-09-19 Doyun Lee, Kevin Han
The construction industry is a major consumer of steel, with welding being a crucial aspect of steel fabrication. However, a shortage of welders has emerged as a significant issue. Therefore, the ultimate goal of this study is to develop a fully automated mobile robotic welding system. The preliminary paper presented a method for the automatic detection and alignment of various welding joints. Building
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Automatic geometric digital twin of box girder bridge using a laser-scanned point cloud Autom. Constr. (IF 9.6) Pub Date : 2024-09-19 Jiangpeng Shu, Ziyue Zeng, Wenhao Li, Shukang Zhou, Congguang Zhang, Caie Xu, He Zhang
Geometric modeling is a pivotal step in creating a digital twin for existing bridge structures. Its deficiency of automation makes geometric modeling step time-consuming and laborious. This paper presents a solution for automatically modeling box girder bridges, including external and internal structures, based on laser-scanned point cloud. The solution includes three vital methods: component segmentation
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Voxel-based path-driven 3D concrete printing process simulation framework embedding interlayer behavior Autom. Constr. (IF 9.6) Pub Date : 2024-09-18 Baixi Chen, Xueqi Zhao, Xiaoping Qian
This paper introduces a numerical framework to model the 3D concrete printing process, considering critical factors, particularly the print path and interlayer interactions. Within this framework, a finite element model is automatically generated for an arbitrary 3D-printed object. This is achieved by voxelizing the bounding space, incorporating a zero-thickness interlayer cohesive zone, and pinpointing
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BIM- and blockchain-enabled Automatic Procurement System (BBAPS) removing relationship bias Autom. Constr. (IF 9.6) Pub Date : 2024-09-17 Jong Han Yoon, Istiqlal Aurangzeb, Sean McNamara
In the subcontractor (Sub) procurement process, the General Contractor (GC) can seek potential Subs from a limited pool based on their past relationships. This challenges new subcontractors who are qualified for the project but lack prior relationships with the GC. Furthermore, it creates a relationship bias that impedes the creation of a constructive business environment where potential Subs are encouraged
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Deep learning-based intelligent detection of pavement distress Autom. Constr. (IF 9.6) Pub Date : 2024-09-17 Lele Zheng, Jingjing Xiao, Yinghui Wang, Wangjie Wu, Zhirong Chen, Dongdong Yuan, Wei Jiang
The intelligent detection of pavement distress using deep learning methods has consistently been a hotspot in pavement maintenance. This paper aims to offer new insights to promote research and application in this field through bibliometric analysis. Utilizing publications from the Web of Science Core Collection spanning from 2016 to 2024 as the database, this paper conducts a systematic analysis of
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Techniques and strategies in extrusion based 3D concrete printing of complex components to prevent premature failure Autom. Constr. (IF 9.6) Pub Date : 2024-09-17 Abdallah Kamhawi, Mania Aghaei Meibodi
Extrusion based 3D Concrete Printing (E-3DCP) is a rapidly growing method of construction due to its ability to manufacture bespoke architectural and structural elements without incurring the additional time and costs typically associated with the manufacturing of the formwork of these components. However, Complex geometries such as overhangs, bridges, and cantilevers pose significant challenges to
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Intelligent ergonomic optimization in bimanual worker-robot interaction: A Reinforcement Learning approach Autom. Constr. (IF 9.6) Pub Date : 2024-09-17 Mani Amani, Reza Akhavian
Robots have the potential to enhance safety on construction job sites by assuming hazardous tasks. While existing safety research on physical human-robot interaction (pHRI) primarily addresses collision risks, ensuring inherently safe collaborative workflows is equally important. For example, ergonomic optimization in co-manipulation is an important safety consideration in pHRI. While frameworks such
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Digital twin non-fungible token (DT-NFT): Enabling data ownership in the AEC industry Autom. Constr. (IF 9.6) Pub Date : 2024-09-16 Hossein Naderi, Alireza Shojaei
The Architecture, Engineering, and Construction (AEC) industry has experienced an unprecedented surge in data growth, primarily propelled by the widespread adoption of digitalization technologies, including Digital Twins (DTs). This surge poses challenges in terms of data control and ownership. To address this, this paper presents a decentralized platform using blockchain-enabled dynamic Non-Fungible
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BIM-based mixed reality application for bridge inspection Autom. Constr. (IF 9.6) Pub Date : 2024-09-16 Ana Carolina Pereira Martins, Isabele Rocha Castellano, Kléos Magalhães Lenz César Júnior, José Maria Franco de Carvalho, Fernando Gussão Bellon, Diôgo Silva de Oliveira, José Carlos Lopes Ribeiro
Traditional bridge inspection methods have limitations, driving the need for advanced techniques. The primary objective of this paper is to explore and evaluate the potential of combining Mixed Reality (MR) technologies with Building Information Modeling (BIM) and damage information to overcome these challenges. The paper aims to improve communication, collaboration, and the accuracy of structural
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Motion planning for a quadruped robot in heat transfer tube inspection Autom. Constr. (IF 9.6) Pub Date : 2024-09-14 Jiawei Li, Zhaojin Liu, Sicen Li, Jikai Jiang, Yuxiao Li, Changda Tian, Gang Wang
Steam generators (SGs) are essential in nuclear power facilities and require regular inspection to maintain their safety and operational effectiveness. This paper presents a quadruped robot designed to inspect SG heat-transfer tubes. The point-to-point crawling-motion planning problem of the robot is addressed by integrating an improved A* algorithm with an offline motion-posture library established
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Deep learning applications for point clouds in the construction industry Autom. Constr. (IF 9.6) Pub Date : 2024-09-12 Hongzhe Yue, Qian Wang, Hongxiang Zhao, Ningshuang Zeng, Yi Tan
Deep learning (DL) on point clouds holds significant potential in the construction industry, yet no comprehensive review has thoroughly summarized its applications and shortcomings. This paper presents a detailed review of the current applications of DL on point clouds in the construction industry, highlighting existing challenges, limitations, and future research directions. A two-stage literature
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Recent advances on inspection, monitoring, and assessment of bridge cables Autom. Constr. (IF 9.6) Pub Date : 2024-09-12 Xuan Kong, Zhenwen Liu, Han Liu, Jiexuan Hu, Lu Deng
Cables are critical and vulnerable components of long-span cable-supported bridges, providing essential support and integrity to ensure bridge safety. Bridge cables are susceptible to damage over time due to environmental factors and external loads, and nondestructive evaluation (NDE) and structural health monitoring (SHM) technologies are usually employed for early detection and continuous monitoring
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Robotic tower cranes with hardware-in-the-loop: Enhancing construction safety and efficiency Autom. Constr. (IF 9.6) Pub Date : 2024-09-12 Teerapat Kian Xiong Ku, Bingran Zuo, Wei Tech Ang
This paper presents a full suite of Robotic Tower Crane (RTC) technologies that can be seamlessly implemented on traditional saddle-jib tower cranes to boost the construction safety and productivity. The robotisation of tower cranes enables the RTC capabilities of automatic path planning for point-to-point movement, and dynamic obstacle avoidance with re-planning. While the former fast generates the
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Optimized structural inspection path planning for automated unmanned aerial systems Autom. Constr. (IF 9.6) Pub Date : 2024-09-12 Yuxiang Zhao, Benhao Lu, Mohamad Alipour
Automation in Unmanned Aerial Systems (UAS)-based structural inspections has gained significant traction given the scale and complexity of infrastructure. A core problem in UAS-based inspection is electing an optimal flight path to achieve the mission objectives while minimizing flight time. This paper presents an effective two-stage method that guarantees coverage as a constraint to ensure damage
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Automated measurement of cable shape in super-long span suspension bridges Autom. Constr. (IF 9.6) Pub Date : 2024-09-12 Feiyu Wang, Zhuang Ma, Yuyao Cheng, Wang Chen, Jian Zhang
The current measurement of the main cable shape of large-span suspension bridges relies on the total station, which is time-consuming and labor-intensive. Therefore, this paper proposes an automatic measurement method for the cable shape of suspension bridges: (1) For obtaining target during the construction process, inertial navigation and differential Global Positioning System fusion and route planning
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Physiological impact of powered back-support exoskeletons in construction: Analyzing muscle fatigue, metabolic cost, ergonomic risks, and stability Autom. Constr. (IF 9.6) Pub Date : 2024-09-12 Amit Ojha, Yogesh Gautam, Houtan Jebelli, Abiola Akanmu
Powered back-support exoskeletons (BSEs) are emerging as ergonomic interventions in construction to reduce musculoskeletal injuries by actively enhancing user strength. However, their adoption remains slow due to limited understanding of potential physiological impacts, including muscle fatigue, metabolic cost, joint hyperextension, and fall risk. This paper empirically investigates the potential physiological
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Question-answering framework for building codes using fine-tuned and distilled pre-trained transformer models Autom. Constr. (IF 9.6) Pub Date : 2024-09-12 Xiaorui Xue, Jiansong Zhang, Yunfeng Chen
Building code compliance checking is considered a bottleneck in construction projects, which calls for a novel approach to building code query and information retrieval. To address this research gap, the paper presents a question and answering framework comprising: (1) a ‘retriever’ for efficient context retrieval from building codes in response to an inquiry, and (2) a ‘reader’ for precise context
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Enhancing pixel-level crack segmentation with visual mamba and convolutional networks Autom. Constr. (IF 9.6) Pub Date : 2024-09-11 Chengjia Han, Handuo Yang, Yaowen Yang
Computer vision-based semantic segmentation methods are currently the most widely used for automated detection of structural cracks in buildings and pavements. However, these methods face persistent challenges in detecting fine cracks with small widths and in distinguishing cracks from background stains. This paper addresses these issues by introducing MambaCrackNet, a new network architecture for
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Self-adaptive 2D[sbnd]3D image fusion for automated pixel-level pavement crack detection Autom. Constr. (IF 9.6) Pub Date : 2024-09-11 Jiayv Jing, Xu Yang, Ling Ding, Hainian Wang, Jinchao Guan, Yue Hou, Sherif M. El-Badawy
Current 2D and 3D image-based crack detection methods in transportation infrastructure often struggle with noise robustness and feature diversity. To overcome these challenges, the paper use CSF-CrackNet, a self-adaptive 2D3D image fusion model utilizes channel and spatial modules for automated pavement crack segmentation. CSF-CrackNet consists of four parts: feature enhanced and field sensing (FEFS)
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Data-driven multi-objective optimization of road maintenance using XGBoost and NSGA-II Autom. Constr. (IF 9.6) Pub Date : 2024-09-11 Jiale Li, Song Zhang, Xuefei Wang
Road maintenance is crucial for road comfort. Inappropriate maintenance construction works may cause waste in budget and extra greenhouse gas emissions. Previous studies designed construction plans based on experience and the current distress stage of the road, without considering the cost and carbon emissions between different construction plans throughout the life cycle. The road deterioration tendency
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Enhanced damage segmentation in RC components using pyramid Haar wavelet downsampling and attention U-net Autom. Constr. (IF 9.6) Pub Date : 2024-09-11 Wentao Wang, Lei Li, Zhe Qu, Xiaoli Yang
Damage identification in post-earthquake reinforced concrete (RC) structures based on semantic segmentation has been recognized as a promising approach for rapid and non-contact damage localization and quantification. In damage segmentation tasks, damage regions are often set against complex backgrounds, featuring irregular geometric boundaries and intricate textures, posing significant challenges
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Integrating deep learning and multi-attention for joint extraction of entities and relationships in engineering consulting texts Autom. Constr. (IF 9.6) Pub Date : 2024-09-11 Binwei Gao, Yuquan Hu, Jianan Gu, Xueqiao Han
While traditional manual knowledge management methods indicate the intelligent approach in the whole-process engineering consulting, related studies like NLP technologies still demonstrated the feasibility and difficulties in processing the complex unstructured long-text consulting knowledge text. To optimize, by firstly incorporating multi attention mechanisms to realize complex long-text knowledge
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Time lag between visual attention and brain activity in construction fall hazard recognition Autom. Constr. (IF 9.6) Pub Date : 2024-09-10 Mei Liu, Mingxuan Liang, Jingyi Yuan, Jiaming Wang, Pin-Chao Liao
Falling hazards pose significant health and safety risks to workers. This paper investigated the correlation between visual attention and brain activity in the recognition of human and object falling hazards. Seventy construction workers were recruited and asked to identify hazards depicted in images while undergoing eye tracking and electroencephalography. Raw electroencephalography and eye movement
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Digital twin with data-mechanism-fused model for smart excavation management Autom. Constr. (IF 9.6) Pub Date : 2024-09-10 Xiong Wang, Yue Pan, Jinjian Chen
The accurate assessment and effective management of deep excavation risk have faced longstanding challenges due to the highly complicated and uncertain construction process. A digital twin, designed with the data-mechanism-fused (DMF) physical and virtual models, is developed to solve problems by integrating Building Information Modeling (BIM), data mining (DM), and physical mechanisms. In the DMF
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3D point-cloud data corrosion model for predictive maintenance of concrete sewers Autom. Constr. (IF 9.6) Pub Date : 2024-09-10 Minghao Li, Xin Feng, Xudu Liu
Predictive maintenance decisions can promote resilient sewers, however, interpretable and accurate corrosion predictions are challenging because of the dynamics of corrosion stages and environmental conditions. In this paper, a 3D point-cloud data-based Bayesian model updating approach is presented to predict the critical parameter evolution of concrete sewer corrosion. The proposed approach adopts
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Blockchain-based security-minded information-sharing in precast construction supply chain management with scalability, efficiency and privacy improvements Autom. Constr. (IF 9.6) Pub Date : 2024-09-10 Shishu Ding, Hao Hu, Feng Xu, Zhenyu Chai, Wen Wang
Blockchain and Interplanetary File System (IPFS) integration holds great promise for enhancing transparency and traceability in precast construction supply chain management (PCSCM). However, such integration faces challenges regarding unauthorized access to confidential data, which can lead to significant consequences, such as financial losses and legal issues. Regarding this gap, this paper proposes
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Trustworthy machine learning-enhanced 3D concrete printing: Predicting bond strength and designing reinforcement embedment length Autom. Constr. (IF 9.6) Pub Date : 2024-09-09 Xin-Rui Ma, Xian-Lin Wang, Shi-Zhi Chen
Three-dimensional concrete printing (3DCP) faces challenges in determining and ensuring adequate bond strength between reinforcement and printed concrete. Traditional methods for predicting bond performance are merely deterministic without considering potential uncertainty, which would lead to risks for structural safety. To address this issue, this paper develops a trustworthy machine learning based
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Intelligent optimization of tamping parameters using discrete element and radial basis function-multi-objective genetic algorithm (RBF-MOGA) Autom. Constr. (IF 9.6) Pub Date : 2024-09-07 Shunwei Shi, Ji Wang, Liang Gao, Yanan Zhang, Yixiong Xiao, Jiaxuan Ding
Tamping can effectively recover the geometry of a ballasted track. Nevertheless, it can decrease the mechanical properties of ballast bed. To mitigate the damage caused by tamping, this paper developed an intelligent optimization method for tamping parameters using the DEM-RBF-MOGA. This intelligent optimization method enables automatic simulation and simultaneous data transmission. Additionally, it
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Forecasting bridge damage within a predictive Structural Reliability-based DSS Autom. Constr. (IF 9.6) Pub Date : 2024-09-07 Francesca Brighenti, Mattia Francesco Bado, Francesco Romeo, Daniele Zonta
Nowadays there is urgent need for expeditious and efficient approaches to assess the structural reliability of widespread aging bridge stocks. As such, a shift towards predictive maintenance measures is required. This paper addresses such pressing necessity for efficient methods to assess the structural state of aging bridges, centring its research question on developing a Decision Support System (DSS)
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Automated safety and practicality enhancement of lift plans in modular construction Autom. Constr. (IF 9.6) Pub Date : 2024-09-06 S. Marzieh Bagheri, Hosein Taghaddos, Ulrich Hermann
Crawler cranes are the most pivotal components on large-scale modular construction sites. Generating a practical and safe lift plan significantly impacts the successful delivery of such projects. This paper introduces an integrated crane planning and lift scheduling framework for managing multiple concurrent mobile cranes' operations. The presented framework is built upon recently developed crane planning
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Fine-Kinney fuzzy-based occupational health risk assessment for Workers in different construction trades Autom. Constr. (IF 9.6) Pub Date : 2024-09-05 Hongyang Li, Yousong Wang, Dan Chong, Darmicka Rajendra, Martin Skitmore
This paper aims to enhance occupational health risk assessment for construction workers by introducing and validating two innovative models: the Occupational Health Risk Assessment Hierarchy Model (OHRAHM) and the Occupational Health Hazard Factor Risk Assessment Model (OHHFRAM). Utilizing the Fine-Kinney method (FKM), fuzzy sets, and the fuzzy inference system (FIS), these models provide a nuanced
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Automatic repetitive action counting for construction worker ergonomic assessment Autom. Constr. (IF 9.6) Pub Date : 2024-09-04 Xinyu Chen, Yantao Yu
Work-related musculoskeletal disorders are the primary cause of nonfatal occupational injuries in the construction industry. Accurate ergonomic assessment is essential to reduce the risk of work-related injuries. Repetitive work significantly contributes to musculoskeletal injuries, and various ergonomic evaluation methods have specific criteria for assessing repetitive actions. However, most existing
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Operator model for wheel loader short-cycle loading handling Autom. Constr. (IF 9.6) Pub Date : 2024-09-02 Manoranjan Kumar, Torbjörn Ekevid, Welf Löwe
The need to virtually analyze the interaction between construction equipment machines and geomaterials is critical. This paper investigates the virtual analysis of force-driven maneuvers of a wheel loader (WL) within a co-simulation framework. This framework has been developed integrating the operators' model of the WL and its interaction with the power source model, i.e., the drive train, the hydraulics
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Self-prompting semantic segmentation of bridge point cloud data using a large computer vision model Autom. Constr. (IF 9.6) Pub Date : 2024-08-31 Na Cui, Hanxin Chen, Xiaocheng Guo, Yan Zeng, Zhengqi Hua, Guikai Xiong, Renbin Yue, Jiepeng Liu
Semantic segmentation of bridge Point Cloud Data (PCD) is an intermediate process required for the tasks such as deformation detection and digital twin. However, existing methods either require a substantial amount of training data or exhibit limited generalization ability. To address these issues, this paper presents an unsupervised framework for semantic segmentation of bridge PCD. A visible point
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Construction digital twin: a taxonomy and analysis of the application-technology-data triad Autom. Constr. (IF 9.6) Pub Date : 2024-08-30 Wahib Saif, SeyedReza RazaviAlavi, Mohamad Kassem
This paper addresses a main gap in the literature: the lack of a comprehensive taxonomy of Digital Twin (DT) applications for the construction phase, and the insufficient conceptualization of the interconnections between DT applications, technologies, and data. Through a systematic review and thematic coding of 112 papers, this paper presents a taxonomy of Digital Twin (DT) applications for construction
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Geometric characterization and segmentation of historic buildings using classification algorithms and convolutional networks in HBIM Autom. Constr. (IF 9.6) Pub Date : 2024-08-28 Juan Moyano, Antonella Musicco, Juan E. Nieto-Julián, Juan Pedro Domínguez-Morales
Building Information Models (BIM) are essential for managing information and creating 3D digital representations, especially in the study of historic buildings. However, generating BIM models from point clouds in these structures is challenging due to complex algorithms and architectural forms. Artificial Intelligence (AI) technologies are beginning to automate point cloud classification and segmentation
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Advanced fire emergency management based on potential fire risk assessment with informative digital twins Autom. Constr. (IF 9.6) Pub Date : 2024-08-28 Young-Jin Kim, Hanjin Kim, Beomsu Ha, Won-Tae Kim
Modern large complex buildings, with their vast expanses and multipurpose usability, present diverse fire-spread scenarios, necessitating precise awareness of fire situations. Despite numerous studies integrating building information models with IoT and AI technologies to provide contextual information, there may be limitations in recognizing potential risks under dynamic fire behaviors during urgent
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Block-based construction worker trajectory prediction method driven by site risk Autom. Constr. (IF 9.6) Pub Date : 2024-08-28 Pinsheng Duan, Jianliang Zhou, Yaning Qiao, Ping Guo
Different from pedestrian trajectory prediction, construction worker trajectories are usually affected by risks and have complex movement patterns. Track point-based prediction methods require high prediction accuracy for safety management. This paper presents a block-based construction worker trajectory prediction method driven by site risk. First, the construction site is divided into multiple blocks
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Sustainable and cost-effective optimal design of steel structures by minimizing cutting trim losses Autom. Constr. (IF 9.6) Pub Date : 2024-08-27 Raffaele Cucuzza, Majid Movahedi Rad, Marco Domaneschi, Giuseppe C. Marano
Since the beginning of the structural optimization field, the optimal design was characterized by the least-weight configuration. In this sense, all the researchers agreed on adopting the minimum-weight optimization statement as the most promising approach to achieve an optimized employment of material. However, especially for steel structures, this approach completely fails the primary goal of encouraging
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Human-robot collaboration for building deconstruction in the context of construction 5.0 Autom. Constr. (IF 9.6) Pub Date : 2024-08-27 Chukwuka Christian Ohueri, Md. Asrul Nasid Masrom, Masa Noguchi
Deconstruction, a hazardous stage in a building's lifecycle, requires safe and efficient human-robot collaboration (HRC). However, existing studies lack a comprehensive understanding of the advancements and future potential of HRC in building deconstruction within Construction 5.0. This paper reviewed 251 articles related to HRC in deconstruction from 2014 to 2024, using a mixed quantitative-qualitative
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Linear Scheduling Method-Based Multi-Objective Optimization for Off-Site Construction Manufacturing Autom. Constr. (IF 9.6) Pub Date : 2024-08-27 Mizanoor Rahman, Sang Hyeok Han
Optimal production scheduling in off-site construction is hindered by the lack of comprehensive methods that simultaneously addresses workforce allocation, realistic labor productivity, workstation idle time (WIT), work-in-progress (WIP), and project completion time (PCT). To overcome these limitations, this paper introduces a hybrid planning and scheduling method that integrates the linear scheduling
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Enhancement of underwater dam crack images using multi-feature fusion Autom. Constr. (IF 9.6) Pub Date : 2024-08-26 Dong Chen, Fei Kang, Junjie Li, Sisi Zhu, Xuewen Liang
Underwater dam-crack images captured by remotely operated vehicles (ROVs) often exhibit blurred and unclear features because of the absorption and scattering of water and device shaking. To address these challenges, we propose an underwater crack image-enhancement network (UCE-CycleGAN) that outperforms existing methods on unpaired crack datasets. This network employs multi-feature fusion, incorporating
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Digital twin for smart metro service platform: Evaluating long-term tunnel structural performance Autom. Constr. (IF 9.6) Pub Date : 2024-08-26 Cheng Zhou, Wenbo Qin, Hanbin Luo, Qunzhou Yu, Bin Fan, Qi Zheng
The metro system is crucial for urban transportation networks because it significantly improves traffic flow, with tunnels playing a key role in ensuring the safety and quality of metro services. Over time, the structural performance of metro tunnels deteriorates, posing risks to the system's operational dependability. This paper highlights the importance of accurately assessing the long-term structural
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Constructability-driven design of frame structures with state-space search methods Autom. Constr. (IF 9.6) Pub Date : 2024-08-26 Yijiang Huang, Caelan Garrett, Caitlin Mueller
In the design of frames and trusses, the relationship between the structural form, the construction sequence, and the structural behavior during construction is rarely systematically considered. This paper proposes a method to systematically consider constructability in design, specifically focusing on minimizing the maximum displacement or stress during construction. The paper presents the formulation
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Automated generation and semantic segmentation of roof orthophoto for digital twin -based monitoring of slated roofs Autom. Constr. (IF 9.6) Pub Date : 2024-08-25 Jiajun Li, Boan Tao, Frédéric Bosché, Chris Xiaoxuan Lu, Lyn Wilson
Roofs are one of the building elements most exposed to environment-induced deterioration, and their deterioration can rapidly result in potential safety hazards to occupants or even buildings’ failure. In pursuit of cost-effective, efficient and pro-active building roof monitoring, this paper presents a computing pipeline for: 1. Automated roof orthophoto generation that uses as input the 3D reconstruction
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Consortium blockchain-based tunnel data bank for traceable sharing and treatment of structural health monitoring data Autom. Constr. (IF 9.6) Pub Date : 2024-08-25 Dong-Ming Zhang, Cong Nie, Jin-Zhang Zhang, Hong-Wei Huang, Xu Huang
The volume of structural health monitoring (SHM) data is rapidly increasing due to the explosive growth of information from constructed infrastructures. However, data storage by different monitoring organizations is often isolated, even when they serve the same construction project, resulting in “data silos”. Improving the efficiency of sharing massive monitoring data and developing a reliable, distributed
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AI-assisted ultrasonic wave analysis for automated classification of steel corrosion-induced concrete damage Autom. Constr. (IF 9.6) Pub Date : 2024-08-25 Julfikhsan Ahmad Mukhti, Nenad Gucunski, Seong-Hoon Kee
Early detection of cracks in reinforced concrete caused by chloride intrusion is crucial for effective maintenance. This paper develops and compares AI-assisted models that use ultrasonic pulse waves to automatically assess early-stage concrete damage, particularly cracks caused by steel corrosion. Data were collected from 108 concrete cubes with various mixture designs, cover depths, and steel corrosion
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Additive manufacturing of natural materials Autom. Constr. (IF 9.6) Pub Date : 2024-08-25 Olga Beatrice Carcassi, Lola Ben-Alon
As additive manufacturing (AM) technology continues to advance for computer-aided design and engineering applications, a parallel imperative emerges — a conscientious shift towards more responsible material practices, aligning with ethical, environmental, and social sustainability considerations. The present systematic review analyzes the state-of-the-art developments in relation to AM using natural
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Enhancing robotic steel prefabrication with semantic digital twins driven by established industry standards Autom. Constr. (IF 9.6) Pub Date : 2024-08-25 Lukas Kirner, Victoria Jung, Jyrki Oraskari, Sigrid Brell-Cokcan
To increase automation in steel construction, new approaches are needed to strengthen the robustness of robotic steel prefabrication processes against manufacturing tolerances. While Digital Twins (DTs) can enable the detection of deviations and the adaptation of machine control accordingly, an adaptive information model interface that can integrate cross-process and cross-machine considerations is
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Make it till you fake it: Construction-centric computational framework for simultaneous image synthetization and multimodal labeling Autom. Constr. (IF 9.6) Pub Date : 2024-08-25 Ali Tohidifar, Daeho Kim, SangHyun Lee
This paper introduces BlendCon, a fully automated framework capable of simultaneously synthesizing and labeling construction imagery data. This framework simulates a construction site by orchestrating 3D mobile objects against a 3D background and produces multimodal labels for target entities. The effectiveness of the synthetic data in training object detection models was thoroughly validated. For
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AI-based 3D pipe automation layout with enhanced ant colony optimization algorithm Autom. Constr. (IF 9.6) Pub Date : 2024-08-25 Chao Liu, Lei Wu, Guangxin Li, Wensheng Xiao, Liping Tan, Dengpan Xu, Jingjing Guo
Pipe automation layout (PAL) is an important part of the system and has been widely used in many fields. To address the shortcomings of traditional ant colony optimization (ACO) algorithm that tend to fall into local optimum, slow convergence and initial stagnation in three-dimensional (3D) PAL, a variant of ACO called improved multiple strategy ACO (IMSACO) is proposed in this paper. The IMSACO mainly
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Attention-based image captioning for structural health assessment of apartment buildings Autom. Constr. (IF 9.6) Pub Date : 2024-08-25 Nguyen Ngoc Han Dinh, Hyunkyu Shin, Yonghan Ahn, Bee Lan Oo, Benson Teck Heng Lim
Automated visual assessment report generation in structural health monitoring (SHM) offers advantages for building inspections. However, current vision-based approaches that focus primarily on local surface detection cannot be directly used for inspection reports without further interpretation of the detected labels and coordinator metrics for an appropriate serviceability assessment. To address this