-
Tile detection using mask R-CNN in non-structural environment for robotic tiling Autom. Constr. (IF 9.6) Pub Date : 2025-01-31
Liang Lu, Ning Sun, Zhipeng Wang, Bin HeRobotic tiling is an efficient way to replace manual work, with tile detection and positioning serving as a pivotal technology. However, the tiling environment is characterized by its complexity. This paper introduces the instance segmentation method Mask R-CNN, which can detect tiles in non-structural environments after proper training. To address the difficulty of acquiring datasets and high annotation
-
Application of digitalization and computerization technology in road construction Autom. Constr. (IF 9.6) Pub Date : 2025-01-30
Christopher Pentury, Rudy Hermawan Karsaman, Harmein Rahman, Yusep RosmansyahTechnological advancements have spurred the adoption of digitalization and computerization technologies within the construction industry. This paper employs a systematic review methodology, combining bibliometric analysis, text mining, and visualization techniques, to comprehensively examine the literature on the application of digitalization and computerization technologies in road construction. The
-
Active learning-driven semantic segmentation for railway point clouds with limited labels Autom. Constr. (IF 9.6) Pub Date : 2025-01-29
Zhuanxin Liang, Xudong Lai, Liang ZhangAccurate semantic segmentation of railway point clouds is crucial for railway infrastructure modelling. However, existing fully-supervised methods are heavily dependent on labeled datasets, while label-efficient methods typically struggle to generate representative annotations. To address these challenges, a weakly supervised point cloud semantic segmentation method based on active learning is proposed
-
Occlusion-aware and jitter-rejection 3D video real-time pose estimation for construction workers Autom. Constr. (IF 9.6) Pub Date : 2025-01-29
Benyang Song, Jiajun Wang, Xiaoling Wang, Tuocheng Zeng, Dongze LiVideo pose estimation is widely employed to monitor the activities of workers at construction sites. However, previous studies have often overlooked the challenges posed by complex occlusions and motion jitters, resulting in inaccurate or unrealistic postures that impact subsequent analysis. This paper presents a three-dimensional (3D) worker pose estimation pipeline to mitigate occlusions and jitters
-
Bridge point cloud semantic segmentation based on view consensus and cross-view self-prompt fusion Autom. Constr. (IF 9.6) Pub Date : 2025-01-29
Yan Zeng, Feng Huang, Guikai Xiong, Xiaoxiao Ma, Yingchuan Peng, Wenshu Yang, Jiepeng LiuPoint cloud semantic segmentation has been widely applied for bridge inverse modeling. However, existing methods are either labor-intensive or exhibit poor generality for real-world bridges. To address these limitations, this paper presents a bridge semantic segmentation method based on a pre-trained visual model. A viewpoint selection method based on view consensus is proposed to evaluate and optimize
-
Image-based prediction for enclosure structure deformation in pipe-roof tunnel construction using a physical-guided and generative deep learning method Autom. Constr. (IF 9.6) Pub Date : 2025-01-29
Cunyang Zhang, Yue Pan, Jin-Jian ChenThis paper proposes an image-based enclosure structure deformation prediction model called the physical-guided and generative deep learning (PG-GDL) method for pre-support tunnel construction, filling critical gaps in physical-guided image-based datasets and image-to-image prediction of structure deformations. The PG-GDL method establishes reliable correlations between real-time construction information
-
Computer vision-aided audio dataset generation for recognizing construction equipment actions Autom. Constr. (IF 9.6) Pub Date : 2025-01-28
Gilsu Jeong, Moonseo Park, Changbum R. AhnConstruction sites are dynamic with various activities and equipment sounds, essential for identifying equipment, understanding work processes, and assessing site conditions. However, recognizing equipment actions using audio data faces challenges like manual recording dependency, collecting high-quality datasets, and background noise. This paper introduces an automated framework, aided by computer
-
Real-time rebar spacing measurement system for quality control in construction Autom. Constr. (IF 9.6) Pub Date : 2025-01-28
Ukyong Woo, Myunghun Lee, Taemin Lee, Hajin Choi, Su-Min Kang, Kyoung-Kyu ChoiConstruction supervision is essential for ensuring structural safety but requires significant labor and expertise. This paper developed a 3D point cloud - RGB projection algorithm to integrate RGB and point cloud data from a depth camera, offering a lightweight and efficient solution for real-time measurements in dynamic construction sites. A real-time rebar spacing measurement system (RSMS) was developed
-
Prediction and risk assessment of lateral collapse in deep foundation pits using machine learning Autom. Constr. (IF 9.6) Pub Date : 2025-01-28
Hongyun Fan, Liping Li, Shen Zhou, Ming Zhu, Meixia WangPredicting lateral displacement in deep foundation pits is a critical prerequisite for ensuring effective structural design and the safe construction of foundation pit projects. Traditional prediction methods have limitations in prediction accuracy and efficiency as they primarily rely on experiments and simulations results. To these issues, this paper developed a machine learning (ML)-based method
-
Three-dimensional reconstruction of asphalt pavement macrotexture using event camera and evolved recurrent convolution network Autom. Constr. (IF 9.6) Pub Date : 2025-01-28
Kangnan Wang, Tao Ma, Yuanhang Yang, Zheng TongA three-dimensional (3D) model of asphalt pavement macro-texture is essential for assessing pavement performance. However, the existing methods of 3D macro-texture reconstruction are unstable in various lighting conditions. This paper proposes a method of 3D reconstruction of asphalt pavement macrotexture using an event camera and evolved recurrent convolution network. In this method, an event camera
-
Design Healing framework for automated code compliance Autom. Constr. (IF 9.6) Pub Date : 2025-01-28
Jiabin Wu, Stavros Nousias, André BorrmannAutomated Compliance Checking (ACC) techniques have advanced significantly, enabling designers to evaluate building designs against codes. However, architectural engineers have to improve the design by manually implementing the ACC results, which is laborious, iterative, and requires domain expertise. To address this challenge, this paper introduces a Design Healing framework that adapts the original
-
Blockchain applications in the construction supply chain Autom. Constr. (IF 9.6) Pub Date : 2025-01-28
Mohammadhossein Heydari, Alireza ShojaeiConstruction supply chain issues, such as coordination and collaboration inefficiencies, remain unresolved due to insufficient digitalization progress. Blockchain is investigated as a potential digital solution to overcome these challenges. Despite some reviews on blockchain in construction, specific studies focusing on blockchain in construction supply chain are limited. This paper takes a deeper
-
Artificial intelligence-enhanced non-destructive defect detection for civil infrastructure Autom. Constr. (IF 9.6) Pub Date : 2025-01-28
Yishuang Zhang, Cheuk Lun Chow, Denvid LauAs civil engineering projects become more complex, ensuring the integrity of infrastructure is essential. Traditional inspection methods may damage structures, highlighting the need for non-destructive testing. However, conventional non-destructive methods involve challenges in assessing complex civil infrastructure due to manual operation and subjective interpretation. The integration of artificial
-
Crack image classification and information extraction in steel bridges using multimodal large language models Autom. Constr. (IF 9.6) Pub Date : 2025-01-28
Xiao Wang, Qingrui Yue, Xiaogang LiuExisting deep learning methods fail to meet the requirements of zero-shot learning scenarios for crack detection and have yet to investigate the specific impact of visual prompts on the detection performance of multimodal large language models (MLLMs). This paper proposes a cascaded crack detection strategy based on MLLMs, decomposing the crack detection task into a stepwise classification process
-
Segmentation dataset for reinforced concrete construction Autom. Constr. (IF 9.6) Pub Date : 2025-01-28
Patrick Schmidt, Lazaros NalpantidisThis paper provides a dataset of 14,805 RGB images with segmentation labels for autonomous robotic inspection of reinforced concrete defects. Baselines for the YOLOv8L-seg, DeepLabV3, and U-Net segmentation models are established. Labeling inconsistencies are addressed statistically, and their influence on model performance is analyzed. An error identification tool is employed to examine the error
-
Automatic tile position and orientation detection combining deep-learning and rule-based computer vision algorithms Autom. Constr. (IF 9.6) Pub Date : 2025-01-27
Wenyao Liu, Jinhua Chen, Zemin Lyu, Rui Feng, Tong Hu, Lu DengIncreasing interest in a tile-paving robot calls for a robust tile detection algorithm. This paper proposes the Ultra Clear Tile (UC-Tile) algorithm to detect corners and edges and assist tile paving automation in positioning and installation tasks. UC-Tile is designed to incorporate deep learning for semantic segmentation with rule-based post-processing algorithms. The semantic segmentation algorithm
-
Artificial intelligence in offsite and modular construction research Autom. Constr. (IF 9.6) Pub Date : 2025-01-27
Sitsofe Kwame Yevu, Karen B. Blay, Kudirat Ayinla, Georgios HadjidemetriouThe capabilities of artificial intelligence (AI) in managing complex problems are increasing in construction. Particularly for offsite and modular construction (OMC). However, the knowledge landscape of AI applications in OMC remains fragmented, hindering the understanding of current developments and critical areas for advancing AI-in-OMC. Therefore, this paper presents a comprehensive overview of
-
Curvature-informed paths for shell 3D printing Autom. Constr. (IF 9.6) Pub Date : 2025-01-27
Ioanna Mitropoulou, Mathias Bernhard, Benjamin DillenburgerThe construction of thin, doubly-curved shells poses significant challenges, often necessitating expensive fabrication techniques and extensive formwork. Non-planar 3D printing enables precise fabrication of these geometries with reduced formwork. Curvature plays an important role in the design of non-planar print paths. Nevertheless, designing print paths informed by curvature presents a complex challenge
-
Efficient visual inspection of fire safety equipment in buildings Autom. Constr. (IF 9.6) Pub Date : 2025-01-27
Fangzhou Lin, Boyu Wang, Zhengyi Chen, Xiao Zhang, Changhao Song, Liu Yang, Jack C.P. ChengFire safety equipment (FSE) in buildings is critical in ensuring occupant safety and mitigating losses during emergencies. However, its effectiveness is frequently compromised by inadequate maintenance. As buildings increase size and complexity, traditional manual inspection methods become impractical due to scalability and data management challenges. To address these issues, this paper proposes an
-
Agile digitization for historic architecture using 360° capture, deep learning, and virtual reality Autom. Constr. (IF 9.6) Pub Date : 2025-01-25
Farzan Baradaran Rahimi, Claude M.H. Demers, Mohammad Reza Karimi Dastjerdi, Jean-François LalondeThe agile digitization of historic buildings is becoming increasingly critical for preservation, conservation, and maintenance in response to climate change, geopolitical conflicts, and other threats of destruction. This paper explores whether deep learning-based novel-view synthesis, combined with commercial 360° cameras and standalone virtual reality headsets, can streamline the digitization process
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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