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Multi-sensor data fusion and deep learning-based prediction of excavator bucket fill rates Autom. Constr. (IF 9.6) Pub Date : 2025-01-24
Shijiang Li, Gongxi Zhou, Shaojie Wang, Xiaodong Jia, Liang HouAccurately predicting the bucket fill rate of excavators is a challenging task due to factors such as material flowability and the complex coupling interactions between the material and the bucket. To address this challenge, this paper proposes a bucket fill rate prediction method based on multi-sensor data fusion and deep learning. The ITCBAM model was developed by integrating a Convolutional Block
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Towards worker-centric construction scene understanding: Status quo and future directions Autom. Constr. (IF 9.6) Pub Date : 2025-01-24
Huimin Li, Hui Deng, Yichuan DengConstruction scene understanding is the process of perceiving, analyzing, and interpreting three-dimensional dynamic scenes observed through sensor networks, which is usually real-time. The purpose is to understand the construction scene by analyzing the geometric and semantic features of the objects and their relationships. Construction scene understanding is a basic technology for construction automation
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Image inpainting using diffusion models to restore eaves tile patterns in Chinese heritage buildings Autom. Constr. (IF 9.6) Pub Date : 2025-01-24
Xiaohan Zhong, Weiya Chen, Zhiyuan Guo, Jiale Zhang, Hanbin LuoWadangs (a type of eaves tile) are integral components of traditional Chinese buildings and often suffer damage over time, resulting in the loss of pattern information. Currently, AI-based image inpainting methods are applied in pattern restoration, but face challenges in capturing fine textures and maintain structural continuity. This paper proposes a coarse-to-fine image inpainting method based on
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Detection of helmet use among construction workers via helmet-head region matching and state tracking Autom. Constr. (IF 9.6) Pub Date : 2025-01-24
Yi Zhang, Shize Huang, Jinzhe Qin, Xingying Li, Zhaoxin Zhang, Qianhui Fan, Qunyao TanAccidents at construction sites are prevalent, posing a significant safety threat to workers. Helmets play a crucial role in protecting workers' heads during accidents, and helmet wearing monitoring is essential for ensuring workers' safety. However, it becomes challenging to detect whether workers are wearing helmets when their heads are obstructed or invisible. To enable continuous and accurate monitoring
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Automated framework for asphalt pavement design and analysis by integrating BIM and FEM Autom. Constr. (IF 9.6) Pub Date : 2025-01-23
Ziming Liu, Hao Huang, Yongdan WangTo address the inefficiencies in asphalt pavement modeling and the challenges of integrating design with structural calculations, an automated framework that connects Building Information Modeling (BIM) to the Finite Element Method (FEM) was proposed for evaluating asphalt pavement design solutions and verifying structural optimization. This framework enables precise interaction between BIM and FEM
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Automated point positioning for robotic spot welding using integrated 2D drawings and structured light cameras Autom. Constr. (IF 9.6) Pub Date : 2025-01-23
Lu Deng, Huiguang Wang, Ran Cao, Jingjing GuoPrecise point positioning is crucial for implementing robotic spot welding. Traditional 2D drawings of structural components lack depth information, making them insufficient for guiding robotic welding. This paper introduces an automated robotic welding framework for spot welding based on 2D drawings and structured light cameras. To enhance the efficiency of point positioning, a new algorithm was also
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What makes a good BIM design? Quantifying the link between design behavior and quality Autom. Constr. (IF 9.6) Pub Date : 2025-01-21
Xiang-Rui Ni, Peng Pan, Jia-Rui LinIn the Architecture Engineering & Construction (AEC) industry, how design behaviors impact design quality remains unclear. This paper proposes an approach that firstly unveils and quantitatively describes the relationship between design behaviors and design quality based on Building Information Modeling (BIM). Real-time collection and log mining are integrated to collect raw data related to design
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Continuous multi-target tracking across disjoint camera views for field transport productivity analysis Autom. Constr. (IF 9.6) Pub Date : 2025-01-21
Xiaoling Wang, Dongze Li, Jiajun Wang, Dawei Tong, Ruiqi Zhao, Zhongzhen Ma, Jiandong Li, Benyang SongField transport productivity analysis is crucial for scheduling large-scale earth–rock works. Although camera surveillance facilitates the monitoring of transportation activities, disjoint views from sparse cameras result in discontinuous monitoring. To address this issue, a single-camera tracking with cascade R-CNN is used for target detection, and an improved TransReID for appearance feature extraction
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Objective-directed deep graph generative model for automatic and intelligent highway interchange design Autom. Constr. (IF 9.6) Pub Date : 2025-01-21
Chenxiang Ma, Chengcheng XuHighway interchanges have traditionally been designed through a time-consuming manual process. To enhance efficiency and effectiveness, this paper develops an objective-directed automatic and intelligent interchange design method using graph conditional variational autoencoder. Based on interchange graph representation and augmentation techniques, data are collected from diverse interchanges types
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Guiding GPT models for specific one-for-all tasks in ground penetrating radar Autom. Constr. (IF 9.6) Pub Date : 2025-01-21
Zheng Tong, Yiming Zhang, Tao MaoBuried object detection using ground penetrating radar (GPR) benefits from deep neural networks but still faces the problem of condition- and question-limited outputs. This paper presents an approach to conduct “one-for-all” (OFA) tasks in GPR data processing. In the approach, a generative pre-trained transformer (GPT) generates the prompts based on input GPR data and an open-ended question. The question
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Hybrid powertrain with dual energy regeneration for boom cylinder movement in a hydraulic excavator Autom. Constr. (IF 9.6) Pub Date : 2025-01-21
Van Hien Nguyen, Tri Cuong Do, Kyoung Kwan AhnThis paper presents a powertrain integrated with an energy regeneration system designed to decrease energy consumption and emissions in hybrid hydraulic excavators. The feature of this powertrain is its integration of a hydrostatic transmission (HST), which optimizes torque and speed between the pump and power sources. Additionally, the energy regeneration system includes two distinct methods: employing
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Autonomous cart docking for transportation robots in complex and dynamic construction environments Autom. Constr. (IF 9.6) Pub Date : 2025-01-21
Guang Yang, Shuoyu Wang, Hajime Okamura, Toshiaki Yasui, Shingo Ino, Kazuo Okuhata, Yoshinobu MizobuchiAutonomous material transportation robots offer an efficient solution for moving goods at construction sites, delivering tools, building materials, and supplies to precise locations. The integration of these robots with various types of carts commonly used on construction sites could significantly ease operational requirements, reducing the cost of robotic adoption to a level more feasible for construction
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Physics-informed neural network with fuzzy partial differential equation for pavement performance prediction Autom. Constr. (IF 9.6) Pub Date : 2025-01-18
Jiale Li, Song Zhang, Xuefei WangThe prediction of pavement deterioration is critical for road maintenance and construction. A thorough understanding of road deterioration mechanisms can enhance the effectiveness of maintenance efforts and prevent further degradation. In this paper, a physics-informed neural network (PINN) was developed to incorporate insights from both big data and the macroscopic deterioration behavior of pavements
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Semirigid optimal step iterative algorithm for point cloud registration and segmentation in grid structure deformation detection Autom. Constr. (IF 9.6) Pub Date : 2025-01-18
Bao-Luo Li, Jian-Sheng Fan, Jian-Hua Li, Yu-Fei LiuDeformation detection of grid structures is vital. In complex environments, efficiently identifying locally crooked members among tens of thousands remains a significant challenge. Point cloud-based methods provide dependable solutions for instance segmentation and deformation recognition. However, existing approaches struggle with irrelevant and deficient data, diverse component forms, and low efficiency
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Tunnel crack assessment using simultaneous localization and mapping (SLAM) and deep learning segmentation Autom. Constr. (IF 9.6) Pub Date : 2025-01-18
Huitong Xu, Meng Wang, Cheng Liu, Yongchao Guo, Zihan Gao, Changqing XieArtificial intelligence algorithms and multi-sensor technologies are advancing tunnel crack detection. However, image-based detection methods fail to account for tunnel section curvature, limiting their ability to represent the spatial geometry of cracks. To address these problems, this paper presents a tunnel crack assessment method combining simultaneous localization and mapping (SLAM) with deep
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Simulating excavation processes for large-scale underground geological models using dynamic Boolean operations with spatial hash indexing and multiscale point clouds Autom. Constr. (IF 9.6) Pub Date : 2025-01-18
Penglu Chen, Wen Yi, Dong Su, Yi Tan, Jinwei Zhou, Xiangsheng ChenThe emergence of digital twins and construction simulation in underground space engineering has driven the demand for efficient Boolean operations on geological models to quickly simulate real-world excavation processes. Therefore, this paper proposes an efficient dynamic Boolean operation framework for large-scale geological models. Firstly, geological models are divided into finite subspace models
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Integration of thermographic inspection data with BIM for enhanced concrete infrastructure assessment Autom. Constr. (IF 9.6) Pub Date : 2025-01-18
Sandra Pozzer, Gabriel Ramos, Parham Nooralishahi, Ehsan Rezazadeh Azar, Ahmed El Refai, Fernando López, Clemente Ibarra-Castanedo, Xavier MaldagueThis paper presents a framework that integrates passive infrared thermography (IRT) results with building information modeling (BIM) to improve subsurface delamination inspection in concrete infrastructures. The paper combines solar analysis with BIM for better thermography inspection planning and documents thermographic data on delamination within BIM environment using a semi-automatic AI procedure
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Impact of color and mixing proportion of synthetic point clouds on semantic segmentation Autom. Constr. (IF 9.6) Pub Date : 2025-01-18
Shaojie Zhou, Jia-Rui Lin, Peng Pan, Yuandong Pan, Ioannis BrilakisDeep learning (DL)-based point cloud segmentation is essential for understanding built environment. Despite synthetic point clouds (SPC) having the potential to compensate for data shortage, how synthetic color and mixing proportion impact DL-based segmentation remains a long-standing question. Therefore, this paper addresses this question with extensive experiments by introducing: 1) method to generate
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Prediction of shield machine attitude parameters based on decomposition and multi-head attention mechanism Autom. Constr. (IF 9.6) Pub Date : 2025-01-17
Qiushi Wang, Wenqi Ding, Kourosh Khoshelham, Yafei QiaoTo mitigate the impact of shield attitude prediction errors on operational decision-making, a framework centered on decomposition and deep learning is proposed to predict multiple shield attitudes. The shield time series data is decomposed into trends and fluctuations by integrating detrended fluctuation analysis and variational mode decomposition. A deep learning model augmented by the multi-head
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Adaptive domain-aware network for airport runway subsurface defect detection Autom. Constr. (IF 9.6) Pub Date : 2025-01-17
Haifeng Li, Wenqiang Liu, Nansha Li, Zhongcheng GuiGround-penetrating radar (GPR) is widely used in airport runway subsurface defect detection. However, variability in subsurface environments and operational frequencies of GPR systems across different airports can cause significant discrepancies in radar data, which influence defect assessments. To address this problem, this study proposes a deep learning algorithm named AD-DetNet, which is designed
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Efficient matching of Transformer-enhanced features for accurate vision-based displacement measurement Autom. Constr. (IF 9.6) Pub Date : 2025-01-17
Haoyu Zhang, Stephen Wu, Xiangyun Luo, Yong Huang, Hui LiComputer vision technology and monitoring videos have been employed to obtain structural displacement measurements. Noniterative algorithms are mainly designed for rapid tracking of the motions of individual image points, rather than dense motion fields. Iterative algorithms are limited to estimating motion fields with small amplitudes and require high computation cost to achieve high accuracy. This
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Spatiotemporal deep learning for multi-attribute prediction of excavation-induced risk Autom. Constr. (IF 9.6) Pub Date : 2025-01-17
Yue Pan, Wen He, Jin-Jian ChenThis paper presents a hybrid deep learning model named the Online Learning-based Multi-Attribute Spatial-Temporal Transformer Network (OMSTTN) to predict excavation-induced risks during foundation pit excavation. OMSTTN integrates a hybrid Transformer offline model with a parallel embedding layer to process diverse monitoring attributes and employs a Spatial-Temporal Transformer block to capture complex
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Evaluation of shield-tunnel segment assembly quality using a copula model and numerical simulation Autom. Constr. (IF 9.6) Pub Date : 2025-01-16
Xiaohua Bao, Junhong Li, Jun Shen, Xiangsheng Chen, Zefan Huang, Hongzhi CuiThe quality of shield-tunnel segment assembly is uncertain and quantifying the probabilistic coupling effects of these factors is challenging. This paper presents a method for assessing shield-tunnel segment quality using a copula model with numerical simulation. A two-dimensional joint probability-distribution model is developed to model influencing factors, establishing a reliability-based evaluation
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Structural design and optimization of adaptive soft adhesion bionic climbing robot Autom. Constr. (IF 9.6) Pub Date : 2025-01-16
Huaixin Chen, Quansheng Jiang, Zihan Zhang, Shilei Wu, Yehu Shen, Fengyu XuSoft-body climbing robots can automatically adapt to the external shape of the climbing surface, but their load-carrying capacity and output torque are insufficient. To address this problem, a bionic climbing robot that can adapt to different complex climbing surfaces as well as a high load-bearing capacity is designed. The proposed robot consists of three bionic crab-pincer gripping structures and
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Optimizing Railway Track Tamping and Geometry Fine-Tuning Allocation Using a Neural Network-Based Solver Autom. Constr. (IF 9.6) Pub Date : 2025-01-16
Congyang Xu, Huakun Sun, Siyuan Zhou, Zhiting Chang, Yanhua Guo, Ping Wang, Weijun Wu, Qing HeThis paper introduces a Neural Network Solver (NNS) for Railway Geometry Rectification Linear Program Model (RGRLPM), integrating tamping and fine-tuning operations for millimeter-precision adjustments. The NNS, enhanced by a grad norm process for faster convergence, achieves rectification plans three times faster than the simplex method. Dynamic programming is applied to allocate adjustments between
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Efficient low-collision UAV-based automated structural surface inspection using geometric digital twin and voxelized obstacle information Autom. Constr. (IF 9.6) Pub Date : 2025-01-13
Yonghui An, Jianren Ning, Chuanchuan Hou, Jinping OuThe application of Unmanned Aerial Vehicle (UAV) automatic flight is increasingly popular for structural surface inspection. To address the low level of automation and insufficient adaption of the flight path in response to environmental obstacles, a method of automatic planning UAV inspection mission based on the Geometric Digital Twin (GDT) model and Voxelized Obstacle Information (VOI) is proposed
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Automated detection of underwater dam damage using remotely operated vehicles and deep learning technologies Autom. Constr. (IF 9.6) Pub Date : 2025-01-13
Fei Kang, Ben Huang, Gang WanUnderwater damage poses significant risks to the safe operation of dams, making timely detection critical. Traditional manual inspection methods are hazardous, time-consuming, and labor-intensive. This paper introduces an automated detection system integrating remotely operated vehicles (ROVs) and enhanced deep-learning technologies. The proposed YOLOv8n-DCW model incorporates deformable convolution
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Intelligent design for component size generation in reinforced concrete frame structures using heterogeneous graph neural networks Autom. Constr. (IF 9.6) Pub Date : 2025-01-13
Sizhong Qin, Wenjie Liao, Yuli Huang, Shulu Zhang, Yi Gu, Jin Han, Xinzheng LuTraditional reinforced concrete (RC) frame design depends on extensive engineering experience and iterative verification processes, often resulting in significant inefficiencies. The diversity in the topologies and behaviors of structural components further presents considerable obstacles to effective machine learning applications in design. This paper introduces an approach using heterogeneous graph
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Multimodal deep learning-based automatic generation of repair proposals for steel bridge shallow damage Autom. Constr. (IF 9.6) Pub Date : 2025-01-13
Honghong Song, Xiaofeng Zhu, Haijiang Li, Gang YangAs bridges age, manual repair decision-making methods struggle to meet growing maintenance demands. This paper develops AI systems that can imitate experts' decision processes by mining implicit relationships between bridge damage images and corresponding repair proposals. A multimodal deep learning-based end-to-end decision-making method is proposed to extract and map features of bridge damage images
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Multi-objective optimization control for shield cutter wear and cutting performance using LightGBM and enhanced NSGA-II Autom. Constr. (IF 9.6) Pub Date : 2025-01-13
Ziwei Yin, Jianwei Jiao, Ping Xie, Hanbin Luo, Linchun WeiVarying results in cutter wear and cutting performance can be observed based on different selections of shield operational parameters, particularly in hard rock or soil with a high quartz content. Improperly selecting operational parameters may result in excessive wear and reduced cutting performance, leading to longer project duration and increased costs. Furthermore, it is still challenging to balance
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Deep learning without human labeling for on-site rebar instance segmentation using synthetic BIM data and domain adaptation Autom. Constr. (IF 9.6) Pub Date : 2025-01-13
Tsung-Wei Huang, Yi-Hsiang Chen, Jacob J. Lin, Chuin-Shan ChenOn-site rebar inspection is crucial for structural safety but remains labor-intensive and time-consuming. While deep learning presents a promising solution, existing research often relies on limited real-world labeled data. This paper introduces a framework to train a deep learning model for on-site rebar instance segmentation without human labeling. Synthetic data are generated from BIM models, creating
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Centerline-based registration for shield tunnel 3D reconstruction using spinning mid-range LiDAR point cloud and multi-cameras Autom. Constr. (IF 9.6) Pub Date : 2025-01-10
Liao Jian, Wenge Qiu, Yunjian ChengMobile measurements can rapidly acquire tunnel information. However, cumulative errors in yaw angles occur in the absence or weakness of global positioning system (GPS) signals. This paper presents a method for 3D reconstruction of shield tunnels based on tunnel centerlines using non-repeating spinning mid-range LiDAR (SML) points and photos. First, a low-cost mobile measurement system (MMS) was built
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Multi-objective optimization for flexible design of aerial building machine under various wind conditions Autom. Constr. (IF 9.6) Pub Date : 2025-01-10
Limao Zhang, Junwei Ma, Jiaqi Wang, Qing Sun, Hui YangAerial building machine (ABM) is a climbing formwork-based mechanical equipment, the design of which has been limited by cumbersome processes, insufficient intelligence, and conservative structures. This paper proposes a flexible design framework incorporating multiple reinforcement measures to optimize ABM structures under various wind conditions. Using parametric modeling and multi-objective optimization
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AI-driven computer vision-based automated repair activity identification for seismically damaged RC columns Autom. Constr. (IF 9.6) Pub Date : 2025-01-08
Samira Azhari, Sara Jamshidian, Mohammadjavad HamidiaManual visual inspection is the conventional method for post-earthquake damage assessment, which is unsafe, subjective, and prone to human error. This paper presents an automated rapid and non-contact seismic damage state prediction methodology for reinforced concrete columns using crack image analysis. For surface damage quantification, three features of crack texture complexity including percolation
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Structural design and fabrication of concrete reinforcement with layout optimisation and robotic filament winding Autom. Constr. (IF 9.6) Pub Date : 2025-01-08
Robin Oval, John Orr, Paul ShepherdReinforced concrete is a major contributor to the environmental impact of the construction industry, due not only to its cement content, but also its steel tensile reinforcement, estimated to represent around 40% of the material embodied carbon. Reinforcement has a significant contribution because of construction rationalisation, resulting in regular cages of steel bars, despite the availability of
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Weakly-aligned cross-modal learning framework for subsurface defect segmentation on building façades using UAVs Autom. Constr. (IF 9.6) Pub Date : 2025-01-08
Sudao He, Gang Zhao, Jun Chen, Shenghan Zhang, Dhanada Mishra, Matthew Ming-Fai YuenInfrared (IR) thermography combined with Unmanned Aerial Vehicles (UAVs) offers an innovative approach for automated building façades inspections. However, extracting quantitative defect information from a single image poses a significant challenge. To address this, this paper introduces a Weakly-aligned Cross-modal Learning framework for subsurface defect segmentation using UAVs. This framework consists
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Digital twin construction with a focus on human twin interfaces Autom. Constr. (IF 9.6) Pub Date : 2025-01-08
Ranjith K. Soman, Karim Farghaly, Grant Mills, Jennifer WhyteDespite the growing emphasis on digital twins in construction, there is limited understanding of how to enable effective human interaction with these systems, limiting their potential to augment decision-making. This paper investigates the research question: “How can construction control rooms be utilized as digital twin interfaces to enhance the accuracy and efficiency of decision-making in the digital
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Hybrid-Segmentor: Hybrid approach for automated fine-grained crack segmentation in civil infrastructure Autom. Constr. (IF 9.6) Pub Date : 2025-01-07
June Moh Goo, Xenios Milidonis, Alessandro Artusi, Jan Boehm, Carlo CilibertoIt is essential to detect and segment cracks in various infrastructures, such as roads and buildings, to ensure safety, longevity, and cost-effective maintenance. Despite deep learning advancements, precise crack detection across diverse conditions remains challenging. This paper introduces Hybrid-Segmentor, a deep learning model combining Convolutional Neural Networks-based and Transformer-based architectures
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Rapid post-disaster infrastructure damage characterisation using remote sensing and deep learning technologies: A tiered approach Autom. Constr. (IF 9.6) Pub Date : 2025-01-03
Nadiia Kopiika, Andreas Karavias, Pavlos Krassakis, Zehao Ye, Jelena Ninic, Nataliya Shakhovska, Sotirios Argyroudis, Stergios-Aristoteles MitoulisCritical infrastructure is vital for connectivity and economic growth but faces systemic threats from human-induced damage, climate change and natural disasters. Rapid, multi-scale damage assessments are essential, yet integrated, automated methodologies remain underdeveloped. This paper presents a multi-scale tiered approach, which addresses this gap, by demonstrating how automated damage characterisation
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Enhancing building fire safety inspections with cognitive ergonomics-driven augmented reality: Impact of interaction modes Autom. Constr. (IF 9.6) Pub Date : 2025-01-03
Xiang Wang, Ming Zhang, Yiyang Yang, Fu Xiao, Xiaowei LuoBuilding fire safety equipment (BFSE) management is increasingly complex and time-consuming. The objective of this paper is to develop augmented reality (AR)-enabled systems for BFSE based on cognitive ergonomics theory and explore the impacts of AR interaction modes on enhancing inspection performance. An experiment was conducted with 48 participants divided into three groups: control group with no
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Incremental digital twin framework: A design science research approach for practical deployment Autom. Constr. (IF 9.6) Pub Date : 2025-01-02
Diego Calvetti, Pedro Mêda, Eilif Hjelseth, Hipólito de SousaDigital Twins (DTw) in the construction industry combine multiple digital concepts aimed at achieving high levels of automation. While the industry pursues digital transition, professionals struggle to implement DTw due to their complexity and lack of standards. An incremental approach to deploying DTw can enable phased implementations, reducing costs and delivering faster outcomes. This paper applies
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Associative reasoning for engineering drawings using an interactive attention mechanism Autom. Constr. (IF 9.6) Pub Date : 2025-01-02
Xu Xuesong, Xiao Gang, Sun Li, Zhang Xia, Wu Peixi, Zhang Yuanming, Cheng ZhenboIn infrastructure construction, engineering drawings combine graphic and textual information, with text playing a critical role in retrieving and measuring the similarity of these drawings in practical applications. However, existing research primarily focuses on graphics, neglecting the extraction and semantic representation of text. Existing Optical Character Recognition (OCR)-based methods face
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Enabling scalable Model Predictive Control design for building HVAC systems using semantic data modelling Autom. Constr. (IF 9.6) Pub Date : 2025-01-02
Lu Wan, Ferdinand Rossa, Torsten Welfonder, Ekaterina Petrova, Pieter PauwelsModel Predictive Control (MPC) is a promising optimal control technique to reduce the energy consumption of Heating, Ventilation, and Air Conditioning systems in buildings. However, MPC currently involves significant manual efforts in data preparation, control model design, and software interface design. Better semantic representations of buildings, their systems, and telemetry data could help address
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Unsupervised anomaly detection for tile spalling segmentation using synthetic outlier exposure and contrastive learning Autom. Constr. (IF 9.6) Pub Date : 2024-12-30
Hai-Wei Wang, Rih-Teng WuTile spalling poses significant threats to pedestrians on sidewalks. Recently, deep learning-based approaches have been developed for autonomous building assessments. However, training a supervised model typically requires a large labeled dataset, which is often unavailable in new domain tasks. Moreover, data acquisition and ground-truth labeling are costly. This paper presents an unsupervised framework
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Automation in manufacturing and assembly of industrialised construction Autom. Constr. (IF 9.6) Pub Date : 2024-12-28
Li Xu, Yang Zou, Yuqian Lu, Alice Chang-RichardsThe integration of automation technologies has improved the efficiency of industrialised construction (IC), yet a deeper understanding of their effects on the manufacturing and assembly stages remains necessary. This paper provides a systematic review of how various automation technologies influence these key stages in IC, analysing 53 articles. It explores the deployment of 22 technologies, including
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Deep learning-enhanced smart ground robotic system for automated structural damage inspection and mapping Autom. Constr. (IF 9.6) Pub Date : 2024-12-27
Liangfu Ge, Ayan SadhuGround robotic systems are essential for structural inspection, enhancing mobility, efficiency, and safety while minimizing risks in manual inspections. These systems automate 3D mapping and defect assessment in aging. However, current robotic platforms often require the integration of various sensors and complex parameter tuning, raising costs and limiting efficiency. This paper proposes a streamlined
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FEM-based real-time task planning for robotic construction simulation Autom. Constr. (IF 9.6) Pub Date : 2024-12-25
Qingfeng Xu, Aiyu Zhu, Gangyan Xu, Zimu Shao, Junjun Zhang, Hong ZhangReal-time prefabricated construction faces challenges in robot planning using Building Information Modeling (BIM) due to the need for temporary structure stability. Existing static analysis methods fail to account for dynamic changes and uncertainties. This paper introduces a framework to streamline construction planning, focusing on real-time stability checks for prefabricated structures. Construction
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Semantic navigation for automated robotic inspection and indoor environment quality monitoring Autom. Constr. (IF 9.6) Pub Date : 2024-12-24
Difeng Hu, Vincent J.L. GanMaintaining a comfortable indoor environment is essential for enhancing occupant well-being. However, traditional inspection methods rely on manual input of precise coordinates for target objects, limiting efficiency. This paper proposes a semantic navigation approach to improve robotic inspection intelligence and efficiency. A revised RandLA-Net and KNN algorithm construct a semantic map rich in detailed
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Implementation of hardware technologies in offsite construction (2014–2023) Autom. Constr. (IF 9.6) Pub Date : 2024-12-24
Erfan Hedayati, Ali Zabihi Kolaei, Mostafa Khanzadi, Gholamreza Ghodrati AmiriAttention to offsite construction (OSC) is increasing as it can reduce construction problems. At the same time, researchers are exploring various technologies to maximize the benefits of OSC and minimize its challenges. In contrast to other review papers that have studied the implementation of technologies in OSC with a particular focus on a specific application, a wide range of or a group of technologies
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Digital tool integrations for architectural reuse of salvaged building materials Autom. Constr. (IF 9.6) Pub Date : 2024-12-24
Malgorzata A. Zboinska, Frederik GöbelBuilding material reuse can reduce the environmental impact of construction yet its advanced digital support is still limited. Which digital tools could effectively support repair of highly irregular, salvaged materials? To probe this question, a framework featuring six advanced digital tools is proposed and verified through six design and prototyping experiments. The experiments demonstrate that a
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Automated system of scaffold point cloud data acquisition using a robot dog Autom. Constr. (IF 9.6) Pub Date : 2024-12-24
Duho Chung, Juhyeon Kim, Sunwoong Paik, Seunghun Im, Hyoungkwan KimThis paper introduces Automated system of Scaffold Point cloud data Acquisition using a Robot dog (ASPAR), a method for automating scaffold point cloud data acquisition using a quadruped robot. The method consists of three stages: (1) Initial Exploration, where the robot autonomously explores the site and detects scaffolds in real-time; (2) Scan Plan Generation, which uses 3D SLAM data and scaffold
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Control of existing tunnel deformation caused by shield adjacent undercrossing construction using interpretable machine learning and multiobjective optimization Autom. Constr. (IF 9.6) Pub Date : 2024-12-24
Hongyu Chen, Jun Liu, Geoffrey Qiping Shen, Zongbao FengA hybrid intelligent framework is proposed in this paper to reduce the existing tunnel deformation caused by shield adjacent undercrossing construction (SAUC). A Bayesian optimization natural gradient boosting (BO-NGBoost) model for existing tunnel deformation prediction is developed, and the Shapley additive explanations (SHAP) approach is used to analyze the interpretability of the prediction model
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Dynamic hazard analysis on construction sites using knowledge graphs integrated with real-time information Autom. Constr. (IF 9.6) Pub Date : 2024-12-24
Juntong Zhang, Xin Ruan, Han Si, Xiangyu WangConstruction, as a significant production activity, is inherently prone to accidents. These accidents often result from a chain of multiple hazards. However, existing methods of hazard analysis are limited to single-dimensional network modeling and static analysis, which makes them inadequate for addressing the complexity and variability of construction sites. This paper presents a dynamic construction
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Automatic crack defect detection via multiscale feature aggregation and adaptive fusion Autom. Constr. (IF 9.6) Pub Date : 2024-12-21
Hanyun Huang, Mingyang Ma, Suli Bai, Lei Yang, Yanhong LiuIn this paper, a multi-scale feature aggregation and adaptive fusion network, is proposed for automatic and accurate pavement crack defect segmentation. Specifically, faced with the linear characteristic of pavement crack defects, a multiple-dimension attention (MDA) module is proposed to effectively capture long-range correlation from three directions, including space, width and height, and help identify
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Graph neural networks for classification and error detection in 2D architectural detail drawings Autom. Constr. (IF 9.6) Pub Date : 2024-12-20
Jaechang Ko, Donghyuk LeeThe assessment and classification of architectural sectional drawings is critical in the architecture, engineering, and construction (AEC) field, where the accurate representation of complex structures and the extraction of meaningful patterns are key challenges. This paper established a framework for standardizing different forms of architectural drawings into a consistent graph format, and evaluated
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Delamination detection in concrete decks using numerical simulation and UAV-based infrared thermography with deep learning Autom. Constr. (IF 9.6) Pub Date : 2024-12-19
Dyala Aljagoub, Ri Na, Chongsheng ChengThe potential of concrete bridge delamination detection using infrared thermography (IRT) has grown with technological advancements. However, most current studies require an external input (subjective threshold), reducing the detection's objectivity and accuracy. Deep learning enables automation and streamlines data processing, potentially enhancing accuracy. Yet, data scarcity poses a challenge to
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Automated reality capture for indoor inspection using BIM and a multi-sensor quadruped robot Autom. Constr. (IF 9.6) Pub Date : 2024-12-19
Zhengyi Chen, Changhao Song, Boyu Wang, Xingyu Tao, Xiao Zhang, Fangzhou Lin, Jack C.P. ChengThis paper presents a real-time, cost-effective navigation and localization framework tailored for quadruped robot-based indoor inspections. A 4D Building Information Model is utilized to generate a navigation map, supporting robotic pose initialization and path planning. The framework integrates a cost-effective, multi-sensor SLAM system that combines inertial-corrected 2D laser scans with fused laser
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Egocentric-video-based construction quality supervision (EgoConQS): Application of automatic key activity queries Autom. Constr. (IF 9.6) Pub Date : 2024-12-18
Jingjing Guo, Lu Deng, Pengkun Liu, Tao SunConstruction quality supervision is essential for project success and safety. Traditional methods relying on manual inspections and paper records are time-consuming, error-prone, and difficult to verify. In-process construction quality supervision offers a more direct and effective approach. Recent advancements in computer vision and egocentric video analysis present opportunities to enhance these
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Experimental study on in-situ mesh fabrication for reinforcing 3D-printed concrete Autom. Constr. (IF 9.6) Pub Date : 2024-12-18
Xiangpeng Cao, Shuoli Wu, Hongzhi CuiThe lack of reinforcements persisted as a significant issue in 3D-printed concrete, particularly concerning the continuous vertical reinforcement along the direction of mortar stacking. This paper introduced an in-situ mesh fabrication technique that involved injecting high-flowability material to connect reinforcement segments, resulting in a reinforcing mesh within the stacked mortar. Parallel and
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Physics-guided deep learning for generative design of large-diameter tunnels under existing metro lines Autom. Constr. (IF 9.6) Pub Date : 2024-12-17
Limao Zhang, Jiaqi Wang, Zhuang Xia, Xieqing SongThe overlapping construction of large-diameter tunnels is inevitable, but the construction control faces great challenges due to the complexity of underground environments. A generative design method for large-diameter tunnels under existing metro lines based on physic-guided deep learning is proposed, aiming at optimizing tunnel layouts from a physical perspective to ensure effective construction