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Crane safety operations in modular integrated construction Autom. Constr. (IF 10.3) Pub Date : 2024-05-30 Ali Hassan Ali, Tarek Zayed, Mohamed Hussein
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Shotcrete flatness evaluation of initial linings based on vehicular LiDAR scanning Autom. Constr. (IF 10.3) Pub Date : 2024-05-28 Shiyu Fang, Degang Xu, Zhilong Zhao, Qing Song, Weihua Gui
Known for decades, initial linings play a crucial role in supporting tunnel construction as shotcrete-lined structures. However, evaluating shotcrete flatness on these linings is challenging due to their large-span characteristics, rough sprayed surfaces, and complex work environments, which currently relies heavily on manual labor. In response, this paper proposes an innovative algorithm that uses
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CNN-based network with multi-scale context feature and attention mechanism for automatic pavement crack segmentation Autom. Constr. (IF 10.3) Pub Date : 2024-05-24 Jia Liang, Xingyu Gu, Dong Jiang, Qipeng Zhang
The diversity and complexity of cracks pose significant challenges for the rapid and accurate detection of pavement defects. To address these challenges, this paper aims to enhance feature utilization and develop an end-to-end crack segmentation network (CSNet), with the goal of significantly improving detection accuracy. Firstly, the proposed model integrates dense parallel dilated convolutions, enabling
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Applications and challenges of digital twin intelligent sensing technologies for asphalt pavements Autom. Constr. (IF 10.3) Pub Date : 2024-05-23 Xingwang Wang, Yuqing Zhang, Hui Li, Chonghui Wang, Ponan Feng
Enhancing the monitoring of asphalt pavement structural health and implementing lifecycle management practices are essential steps in establishing a secure and efficient usage environment for society. The advent of Digital Twin (DT) technology, has facilitated the realization of this objective. DT technology enables comprehensive lifecycle monitoring and management of asphalt pavements. Successful
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GEOMAPI: Processing close-range sensing data of construction scenes with semantic web technologies Autom. Constr. (IF 10.3) Pub Date : 2024-05-23 Maarten Bassier, Jelle Vermandere, Sam De Geyter, Heinder De Winter
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A computer vision-based approach to automatically extracting the aligning information of precast structural components Autom. Constr. (IF 10.3) Pub Date : 2024-05-22 Xiaotian Ye, Ying Zhou, Hongling Guo, Zhubang Luo
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Classification and automated quality assurance of 3D concrete printed surfaces Autom. Constr. (IF 10.3) Pub Date : 2024-05-22 Jens Otto, Patrick Maiwald
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Shifting research from defect detection to defect modeling in computer vision-based structural health monitoring Autom. Constr. (IF 10.3) Pub Date : 2024-05-21 Junjie Chen, Isabelle Chan, Ioannis Brilakis
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Deep learning-assisted high-resolution sonar detection of local damage in underwater structures Autom. Constr. (IF 10.3) Pub Date : 2024-05-21 Huiming Tan, Leiming Zheng, Chicheng Ma, Yi Xu, Yifei Sun
To detect the underwater structure of hydraulic engineerings, conventional approaches usually require manual diving or underwater optical visualization techniques, which however have some inherent shortcomings, such as low efficiency, poor safety, or susceptibility to operational environment interference. To address these limitations, a deep learning-assisted high resolution sonar detection method
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Stereoscopic monitoring of transportation infrastructure Autom. Constr. (IF 10.3) Pub Date : 2024-05-21 Jianzhu Wang, Shuo Zhang, Hongyu Guo, Yu Tian, Shijie Liu, Cong Du, Jianqing Wu
Automated infrastructure monitoring systems play a vital role in ensuring transportation safety and have been studied extensively in the literature. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, this paper conducts literature review to investigate the stereoscopic monitoring of transportation infrastructure. Based on the specific inclusion criteria
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BIM-based schedule generation and optimization using genetic algorithms Autom. Constr. (IF 10.3) Pub Date : 2024-05-18 Hossam Wefki, Mohamed Elnahla, Emad Elbeltagi
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Automated geometric quality inspection for modular boxes using BIM and LiDAR Autom. Constr. (IF 10.3) Pub Date : 2024-05-18 Yi Tan, Limei Chen, Manfeng Huang, Jia Li, Guorong Zhang
Modular integrated construction (MiC) offers high construction quality and efficiency. Currently, geometric quality inspections for modular boxes are conducted manually. Therefore, based on BIM and LiDAR, this study proposes an automated inspection approach for outer wall surfaces and rebars of modular boxes. To extract the targeted modular boxes from complex construction sites, a novel method using
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Automated BIM-to-scan point cloud semantic segmentation using a domain adaptation network with hybrid attention and whitening (DawNet) Autom. Constr. (IF 10.3) Pub Date : 2024-05-18 Difeng Hu, Vincent J.L. Gan, Ruoming Zhai
Deep learning-based point cloud semantic segmentation facilitates scene understanding and BIM modelling, but its success requires vast amount of labelled point clouds, which is laborious and time-consuming. To reduce point cloud annotation cost, researchers attempt to leverage synthetic point clouds, but the domain gap between synthetic and real point clouds deteriorates the segmentation accuracy.
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Prospects for purely electric construction machinery: Mechanical components, control strategies and typical machines Autom. Constr. (IF 10.3) Pub Date : 2024-05-17 Xiaohui Huang, Wanbin Yan, Huajun Cao, Sujiao Chen, Guibao Tao, Jin Zhang
The electrification of construction machinery is an inevitable development trend due to strengthened restrictions on greenhouse gas and pollutant emissions from construction machinery. However, the widespread adoption of pure electric construction machinery is still in its infancy and faces numerous challenges. This paper introduces the latest significant contributions in the electrification of construction
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Zero-shot monitoring of construction workers' personal protective equipment based on image captioning Autom. Constr. (IF 10.3) Pub Date : 2024-05-17 Daeyoung Gil, Ghang Lee
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Hierarchical attributed graph-based generative façade parsing for high-rise residential buildings Autom. Constr. (IF 10.3) Pub Date : 2024-05-16 Bolun Wang, Maosu Li, Ziyu Peng, Weisheng Lu
High-rise residential building façades (HRBFs), given their size and abundant façade information, pose a challenge for conventional parsing methods. This paper presents FaçadeGraph, an approach for parsing the information of HRBFs into hierarchical attributed graphs. The method decomposes HRBF information into five hierarchical layers: ternary, floor, unit, space, and component. The façade elements
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Deep learning-integrated electromagnetic imaging for evaluating reinforced concrete structures in water-contact scenarios Autom. Constr. (IF 10.3) Pub Date : 2024-05-16 Alan Putranto, Tzu-Hsuan Lin, Bo-Xun Huang
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Construction risk identification using a multi-sentence context-aware method Autom. Constr. (IF 10.3) Pub Date : 2024-05-15 Nan Gao, Ali Touran, Qi Wang, Nicholas Beauchamp
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Understanding human-robot proxemic norms in construction: How do humans navigate around robots? Autom. Constr. (IF 10.3) Pub Date : 2024-05-15 YeSeul Kim, Seongyong Kim, Yilong Chen, HyunJin Yang, Seungwoo Kim, Sehoon Ha, Matthew Gombolay, Yonghan Ahn, Yong Kwon Cho
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A non-unit repetitive construction project scheduling with uncertainties Autom. Constr. (IF 10.3) Pub Date : 2024-05-14 Luong Duc Long
Repetitive construction project scheduling is a crucial aspect of modern construction project management. This study focuses on the scheduling of non-unit repetitive construction projects with non-serial activity groups, multiple crews, flexible or fixed sequences, and under correlated uncertainties. An integrated model has been developed by combining novel algorithms for non-unit repetitive project
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Identifying at-risk workers using fNIRS-based mental load classification: A mixed reality study Autom. Constr. (IF 10.3) Pub Date : 2024-05-13 Shiva Pooladvand, Woei-Chyi Chang, Sogand Hasanzadeh
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Exploring associations between accident types and activities in construction using natural language processing Autom. Constr. (IF 10.3) Pub Date : 2024-05-11 Numan Khan, Sylvie Nadeau, Xuan-Tan Pham, Conrad Boton
Existing accident analysis methods often fail to integrate the relationship of construction accidents with construction activities, restrict the potential for visualizing the accidents frequency in Building Information Modelling (BIM). This study proposes a decision support system that provides visual insights, and the frequency of the core construction accidents in relation to typical construction
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Blockchain-enabled platform-as-a-service for production management in off-site construction design using openBIM standards Autom. Constr. (IF 10.3) Pub Date : 2024-05-10 Yuhan Liu, Xingyu Tao, Moumita Das, Xingbo Gong, Hao Liu, Yuqing Xu, Anke Xie, Jack C.P. Cheng
Integrating building information modeling (BIM) with emerging blockchain technology has gained considerable attention in addressing design collaboration problems like poor data security, weak traceability, and low transparency in off-site construction. However, blockchain implementations in off-site construction design, especially with openBIM standards, currently lack standardized workflow management
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Robotically winded full scale timber prototypes Autom. Constr. (IF 10.3) Pub Date : 2024-05-10 Andreas Göbert, Georgia Margariti, Julian Ochs, Ole Weyhe, Andrea Rossi, Philipp Eversmann, Julian Lienhard
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Connecting design and fabrication through algorithms: current and future prospects for AEC Autom. Constr. (IF 10.3) Pub Date : 2024-05-09 Inês Caetano, António Leitão
Algorithmic Design (AD) tools enable the creation of geometrically complex architectural shapes that might be challenging to manufacture. This paper presents an overview of recent design-to-fabrication processes based on AD. It reflects on how AD can help overcome fabrication limitations and enhance the connection between architectural geometry, material, and manufacturing, approximating design exploration
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Data-driven automatic classification model for construction accident cases using natural language processing with hyperparameter tuning Autom. Constr. (IF 10.3) Pub Date : 2024-05-09 Louis Kumi, Jaewook Jeong, Jaemin Jeong
The construction industry, while vital to societal progress, is marred by a high incidence of accidents and injuries. Manual classification of accident cases is intensive and susceptible to human bias. This study addresses this challenge by developing an automated accident case classification system for the construction industry using Natural Language Processing and machine learning techniques. This
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From data to knowledge: Construction process analysis through continuous image capturing, object detection, and knowledge graph creation Autom. Constr. (IF 10.3) Pub Date : 2024-05-09 Fabian Pfitzner, Alexander Braun, André Borrmann
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Neural rendering-based semantic point cloud retrieval for indoor construction progress monitoring Autom. Constr. (IF 10.3) Pub Date : 2024-05-09 Zhiming Dong, Weisheng Lu, Junjie Chen
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AI integration in construction safety: Current state, challenges, and future opportunities in text, vision, and audio based applications Autom. Constr. (IF 10.3) Pub Date : 2024-05-09 Ahmed Bin Kabir Rabbi, Idris Jeelani
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Graph-based decomposition for electrical wiring design in interior finishing Autom. Constr. (IF 10.3) Pub Date : 2024-05-08 Andong Qiu, Ganquan Shi, Zhouwang Yang
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Uncertainty-aware point cloud segmentation for infrastructure projects using Bayesian deep learning Autom. Constr. (IF 10.3) Pub Date : 2024-05-08 Hristo Vassilev, Marius Laska, Jörg Blankenbach
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3D pose estimation dataset and deep learning-based ergonomic risk assessment in construction Autom. Constr. (IF 10.3) Pub Date : 2024-05-06 Chao Fan, Qipei Mei, Xinming Li
Pose estimation of construction workers is critical to ensuring safe construction and protecting construction workers from ergonomic risks. Computer vision (CV)-based 3D pose estimation for construction workers is increasingly used in ergonomic risk assessment (ERA) due to its considerable practicability and accuracy. Currently, the deficiencies of (1) dedicated datasets for construction activities
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Multispecies hybrid bioinspired climbing robot for wall tile inspection Autom. Constr. (IF 10.3) Pub Date : 2024-05-05 Tzu-Hsuan Lin, Pin-Chian Chiang, Alan Putranto
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Parameter impact on 3D concrete printing from single to multi-layer stacking Autom. Constr. (IF 10.3) Pub Date : 2024-05-04 Ying Wei, Song Han, Shiwei Yu, Ziwei Chen, Ziang Li, Hailong Wang, Wenbo Cheng, Mingzhe An
Previous studies have revealed that the design of printing parameters influences the filament shape, while the similarity degree between filament shape and modeled slice directly determines the precision of multi-layer stacking structures. This paper experimentally investigates the influence of printing parameters on filament shape and pressure along the printing path. Computational Fluid Dynamic simulation
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Employing simulation to assess value alignment and fulfillment within construction project settings and team dynamics Autom. Constr. (IF 10.3) Pub Date : 2024-05-03 Salam Khalife, Lynn Shehab, Farook Hamzeh
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Tower crane safety technologies: A synthesis of academic research and industry insights Autom. Constr. (IF 10.3) Pub Date : 2024-05-03 Ali Hassan Ali, Tarek Zayed, Roy Dong Wang, Matthew Yau Shun Kit
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Extended IFC-based information exchange for construction management of roller-compacted concrete dam Autom. Constr. (IF 10.3) Pub Date : 2024-05-02 Shihang Zhang, Sherong Zhang, Chao Wang, Guojin Zhu, Han Liu, Xiaohua Wang
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Data linking and interaction between BIM and robotic operating system (ROS) for flexible construction planning Autom. Constr. (IF 10.3) Pub Date : 2024-05-02 Aiyu Zhu, Pieter Pauwels, Elena Torta, Hong Zhang, Bauke De Vries
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Safety risk perception and control of water inrush during tunnel excavation in karst areas: An improved uncertain information fusion method Autom. Constr. (IF 10.3) Pub Date : 2024-04-30 Xianguo Wu, Zongbao Feng, Sai Yang, Yawei Qin, Hongyu Chen, Yang Liu
Water inrush disasters seriously endanger the construction safety of karst tunnels. To accurately assess the water inrush risk level of karst tunnels, actively prevent and control water inrush disasters. A risk evaluation method based on an improved cloud model (CM) and evidence reasoning (ER) combined with Monte Carlo simulation is proposed, which is applied in a water inrush risk evaluation of the
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CNN-Transformer hybrid network for concrete dam crack patrol inspection Autom. Constr. (IF 10.3) Pub Date : 2024-04-29 Mingchao Li, Jingyue Yuan, Qiubing Ren, Qiling Luo, Junen Fu, Zhitang Li
Regular patrol inspection of concrete dams can detect cracks at an early stage. However, conventional crack segmentation models based on deep learning (DL) are difficult to be deployed in resource-constrained mobile devices due to the large number of parameters. This paper describes a lightweight semantic segmentation model, termed as CrackTrNet, for images of concrete dam cracks. CrackTrNet is a hybrid
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Underwater dam crack image generation based on unsupervised image-to-image translation Autom. Constr. (IF 10.3) Pub Date : 2024-04-27 Ben Huang, Fei Kang, Xinyu Li, Sisi Zhu
Underwater crack detection is necessary for the safe operation of concrete dams. Current deep-learning-based underwater crack detection methods rely heavily on a large number of crack images; these images are difficult to collect because of complex and dangerous underwater environments. This study proposes a method that can generate underwater dam crack images using above-water dam crack images through
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Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-Model-Based Conceptual Design of Pedestrian Bridges Autom. Constr. (IF 10.3) Pub Date : 2024-04-27 Vera Balmer, Sophia V. Kuhn, Rafael Bischof, Luis Salamanca, Walter Kaufmann, Fernando Perez-Cruz, Michael A. Kraus
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Enhancing images for vision measurement in hazy tunnel construction Autom. Constr. (IF 10.3) Pub Date : 2024-04-26 Zhichao Meng, Zilu Shi, Junzhou Huo, Zhen Wu, Fan Yang
To address the negative impacts of image degradation on vision measurement in hazy tunnel construction, this paper presents a unified enhancement method of color and grayscale images in tunnel haze environments. Firstly, a tunnel haze environment light scattering model is introduced utilizing geometric optics principles and the conventional atmospheric scattering model. Secondly, a model for perceiving
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Reactive UAV-based automatic tunnel surface defect inspection with a field test Autom. Constr. (IF 10.3) Pub Date : 2024-04-24 Ran Zhang, Guangbo Hao, Kong Zhang, Zili Li
This work addresses the problem of automatic tunnel surface defect inspection using unmanned aerial vehicles (UAVs). The research aims at proposing a robust and efficient monitoring method for image data acquisition and processing in complex and dark tunnel environments. A method, called Proximity Move-Pause-Photo for Surface Defect Inspection (PMPP-SDI), is proposed by combining reactive flying control
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TBM tunneling strata automatic identification and working conditions decision support Autom. Constr. (IF 10.3) Pub Date : 2024-04-23 Kang Fu, Daohong Qiu, Yiguo Xue, Tao Shao, Gonghao Lan
It has become an important research topic to ensure the safe and efficient tunnel boring machine (TBM) tunneling in the construction of long distance and deep buried tunnels. This study aims to construct an assistant decision support system that integrates surrounding rock classification identification and tunneling parameters prediction optimization, providing quantitative decision guidance for driving
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Design of curvilinear sections in vertical alignment of roads and railways using general transition curves Autom. Constr. (IF 10.3) Pub Date : 2024-04-23 Andrzej Kobryń
Creation of road and railway routes in general and also design of vertical alignment in particular constitute an significant engineering problem. The vertical alignment of roads, and especially of railways, is of great importance for passenger comfort under high-speed driving conditions. This requires alignment smoothness to a greater extent. In practice, the dominant tendency is to consider vertical
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Construction metaverse: Application framework and adoption barriers Autom. Constr. (IF 10.3) Pub Date : 2024-04-18 Zhen-Song Chen, Jun-Yang Chen, Yue-Hua Chen, Witold Pedrycz
This paper addresses the limited research on the metaverse's application in the construction industry. It aims to investigate how the metaverse can empower construction, identify adoption barriers, and determine the most significant barriers. We propose a novel application framework of construction metaverse based on cyber-physical-social systems, identify 17 barriers using the political-economic-
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High-resolution infrastructure defect detection dataset sourced by unmanned systems and validated with deep learning Autom. Constr. (IF 10.3) Pub Date : 2024-04-18 Benyun Zhao, Xunkuai Zhou, Guidong Yang, Junjie Wen, Jihan Zhang, Jia Dou, Guang Li, Xi Chen, Ben M. Chen
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Sustainability and building information modelling: Integration, research gaps, and future directions Autom. Constr. (IF 10.3) Pub Date : 2024-04-17 Saeed Akbari, Moslem Sheikhkhoshkar, Farzad Pour Rahimian, Hind Bril El Haouzi, Mina Najafi, Saeed Talebi
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Computer vision in drone imagery for infrastructure management Autom. Constr. (IF 10.3) Pub Date : 2024-04-15 Naveed Ejaz, Salimur Choudhury
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Towards innovative and sustainable buildings: A comprehensive review of 3D printing in construction Autom. Constr. (IF 10.3) Pub Date : 2024-04-15 Habibelrahman Hassan, Edwin Rodriguez-Ubinas, Adil Al Tamimi, Esra Trepci, Abraham Mansouri, Khalfan Almehairbi
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Convolutional neural network-based model for recognizing TBM rock chip gradation Autom. Constr. (IF 10.3) Pub Date : 2024-04-13 Yuan-en Pang, Xu Li, Zi-kai Dong, Qiu-ming Gong
A tunnel boring machine (TBM) generates rock chips during excavation, which are crucial for assessing surrounding rock integrity, enhancing excavation efficiency, and evaluating cutter wear. However, traditional methods struggle to identify small rock chips, chips submerged in soil or water, and chips in stacked states. This paper proposes a convolutional neural network (CNN)-based method for directly
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Cascade refinement extraction network with active boundary loss for segmentation of concrete cracks from high-resolution images Autom. Constr. (IF 10.3) Pub Date : 2024-04-13 Lu Deng, Huaqing Yuan, Lizhi Long, Pang-jo Chun, Weiwei Chen, Honghu Chu
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Utilizing synthetic images to enhance the automated recognition of small-sized construction tools Autom. Constr. (IF 10.3) Pub Date : 2024-04-12 Soeun Han, Wonjun Park, Kyumin Jeong, Taehoon Hong, Choongwan Koo
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Multi-domain adaptive analysis of intelligent compaction measurement value for subgrade construction Autom. Constr. (IF 10.3) Pub Date : 2024-04-11 Xuefei Wang, Wei Lu, Jiale Li, Jianmin Zhang, Guowei Ma
Intelligent Compaction (IC) utilizes the Intelligent Compaction Measurement Value (ICMV) to assess compaction quality in real-time. However, current engineering practices rely on a fixed ICMV for evaluation, overlooking material variations and different compaction stages. The choice of ICMV is often dictated by the manufacturer, lacking rationality. This paper presents a multi-domain ICMV system to
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Automatic deflection measurement for outdoor steel structure based on digital image correlation and three-stage multi-scale clustering algorithm Autom. Constr. (IF 10.3) Pub Date : 2024-04-10 Haobo Sun, Yongqi Huang
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BIM-based automated rule-checking in the AECO industry: Learning from semiconductor manufacturing Autom. Constr. (IF 10.3) Pub Date : 2024-04-06 Wawan Solihin, Ziwen Liu, Yujie Lu, Lai Wei
The Architecture, Engineering, Construction and Owner-operated (AECO) industry faces an array of productivity challenges, in part stemming from limited interoperability and suboptimal management of building information across project lifecycles. This paper investigates the application of rule-based systems from the manufacturing industry, especially as used in the semiconductor manufacturing sector
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Predictive models in machine learning for strength and life cycle assessment of concrete structures Autom. Constr. (IF 10.3) Pub Date : 2024-04-03 A. Dinesh, B. Rahul Prasad
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An unsupervised low-light image enhancement method for improving V-SLAM localization in uneven low-light construction sites Autom. Constr. (IF 10.3) Pub Date : 2024-04-03 Xinyu Chen, Yantao Yu
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Real-time risk assessment of multi-parameter induced fall accidents at construction sites Autom. Constr. (IF 10.3) Pub Date : 2024-04-01 Min-Yuan Cheng, Quoc-Tuan Vu, Ren-Kwei Teng