-
Compaction test of rolled rockfill material using multimodal Rayleigh wave dispersion inversion Autom. Constr. (IF 9.6) Pub Date : 2025-02-07 Yao Wang, Hai Liu, Xu Meng, Guiquan Yuan, Huiguo Wang, Ruige Shi, Mengxiong Tang, Billie F. Spencer
This paper investigated the potential of Rayleigh wave multimodal dispersion inversion to advance automatic construction through real-time, in-situ measurement of rockfill compaction. An acquisition system and inversion method were developed to automate the process of obtaining compaction depth profiles and implemented during a dynamic rolling test. A rockfill layer under 2 m was tested, with Rayleigh
-
Microcrack investigations of 3D printing concrete using multiple transformer networks Autom. Constr. (IF 9.6) Pub Date : 2025-02-07 Hongyu Zhao, Xiangyu Wang, Zhaohui Chen, Xianda Liu, Yufei Wang, Jun Wang, Junbo Sun
Extrusion-filament and no-framework craft significantly influence microcracks in 3D printing concrete (3DPC). A detailed analysis of these microcracks is essential to improve overall performance of material. However, fast and automated methods for capturing and measuring representative microcrack information in 3DPC are currently lacking. This paper presents a transformer based method for automatic
-
Transformer-based deep learning model and video dataset for installation action recognition in offsite projects Autom. Constr. (IF 9.6) Pub Date : 2025-02-06 Junyoung Jang, Eunbeen Jeong, Tae Wan Kim
This paper developed and evaluated the Precast Concrete Installation Dataset (PCI-Dataset), a large-scale video dataset for automatically recognizing precast concrete (PC) installation activities. The dataset comprises 12,791 video clips (5 s each, 1080 × 1080 resolution, 30fps) from actual PC construction sites, including 12 balanced activity classes combining three component types and four work stages
-
Change detection network for construction housekeeping using feature fusion and large vision models Autom. Constr. (IF 9.6) Pub Date : 2025-02-06 Kailai Sun, Zherui Shao, Yang Miang Goh, Jing Tian, Vincent J.L. Gan
Although poor housekeeping leads to construction accidents, there is limited technological research on it. Existing methods for detecting poor housekeeping face many challenges, including limited explanations, lack of locating of poor housekeeping and annotated datasets. To address these challenges, this paper proposes the Housekeeping Change Detection Network (HCDN), integrating a feature fusion module
-
Digital twin-based fatigue life assessment of orthotropic steel bridge decks using inspection robot and deep learning Autom. Constr. (IF 9.6) Pub Date : 2025-02-06 Fei Hu, Hongye Gou, Haozhe Yang, Yi-Qing Ni, You-Wu Wang, Yi Bao
Fatigue cracks are a major issue affecting the lifespan and operation and maintenance (O&M) costs of bridges with orthotropic steel decks (OSDs), while current practices for detecting fatigue cracks often rely on manual inspection with time inefficiency. This paper presents a digital twin framework that employs robots equipped with nondestructive testing devices for data collection and deep learning
-
Inherent risks identification in a contract document through automated rule generation Autom. Constr. (IF 9.6) Pub Date : 2025-02-05 Junho Kim, Baekgyu Kwon, JeeHee Lee, Duhwan Mun
Due to the limited time available during the bidding process, construction companies may fail to identify risky terms in the contract document before submission. This paper proposes a method that uses natural language processing (NLP) models, such as dependency parser and bidirectional encoder representations from transformers (BERT), to disassemble and simplify sentences in a contract document and
-
Releasing the power of graph for building information discovery Autom. Constr. (IF 9.6) Pub Date : 2025-02-05 Junxiang Zhu, Nicholas Nisbet, Mengtian Yin, Ran Wei, Ioannis Brilakis
Graph is considered a promising way to managing building information. A new graph-based representation of Industry Foundation Classes (IFC) data, known as IFC-Graph, has recently been developed. However, investigation into IFC-Graph is insufficient, especially for information query. This paper aims to explore the query of graph-based building information and develop a graph query tool for IFC-Graph
-
3D bridge segmentation using semi-supervised domain adaptation Autom. Constr. (IF 9.6) Pub Date : 2025-02-05 Maximilian Kellner, Timothy König, Jan-Iwo Jäkel, Katharina Klemt-Albert, Alexander Reiterer
Understanding the scene is crucial for automated bridge inspection. Traditionally, bridges are measured using 3D sensors that produce large point clouds. Manually interpreting the captured data is time-consuming and error-prone. This paper proposes an unsupervised and semi-supervised domain adaptation approach for 3D bridge segmentation using labeled synthetically generated data and no or limited real-world
-
Machine learning method for As-Is tunnel information model reconstruction Autom. Constr. (IF 9.6) Pub Date : 2025-02-04 Nicola Rimella, Lorenzo Rimella, Anna Osello
The maintenance of aging infrastructure requires advanced tools for efficient inspection and planning. This paper presents a methodology for segmenting and classifying point clouds of road tunnels to streamline maintenance operations. Processing large datasets, such as those generated by laser surveys, poses significant challenges without appropriate IT solutions. Data from four Italian tunnels were
-
Intelligent co-design of shear wall and beam layouts using a graph neural network Autom. Constr. (IF 9.6) Pub Date : 2025-02-04 Jikang Xia, Wenjie Liao, Bo Han, Shulu Zhang, Xinzheng Lu
Generative artificial intelligence-driven design of shear wall structures is crucial for the intelligent design of buildings, but current methods arrange shear walls and then beams successively, overlooking their interdependence. This paper constructed a graph representation of the coupled potential positions for shear walls and beams and proposed a co-design method driven by a graph neural network
-
Smart virtual sensing for deep excavations using real-time ensemble graph neural networks Autom. Constr. (IF 9.6) Pub Date : 2025-02-03 Chen Yang, Chen Wang, Feng Zhao, Bin Wu, Jian-sheng Fan, Yu Zhang
As urban development in densely populated city centers increasingly expands underground, monitoring surface settlement during deep excavations has become a critical safety measure. Traditional methods, often relying on sparse monitoring points and interpolation techniques, struggle to capture the complex settlement behavior, especially given the high nonlinearity of foundation pits. This paper introduces
-
Semantics-based connectivity graph for indoor pathfinding powered by IFC-Graph Autom. Constr. (IF 9.6) Pub Date : 2025-02-03 Junxiang Zhu, Mun On Wong, Nicholas Nisbet, Jinying Xu, Tom Kelly, Sisi Zlatanova, Ioannis Brilakis
Pathfinding is important for many indoor location-based applications such as facility management and indoor navigation. Conventional approaches for constructing indoor networks for pathfinding are mostly based on geometry processing, which can be computing-intensive and time-consuming, especially for buildings with complex irregular shapes. In addition, the generated geometric networks often lack semantics
-
Enhanced vision-based 6-DoF pose estimation for robotic rebar tying Autom. Constr. (IF 9.6) Pub Date : 2025-02-01 Mi Liu, Jingjing Guo, Lu Deng, Songyue Wang, Huiguang Wang
Rebar tying is a labor-intensive and time-consuming task that involves repeatedly securing rebar intersections. While rebar tying robots have been developed to automate this process, most research focuses on tying point localization for horizontal ties, neglecting the 6 degrees of freedom (DoF) tying pose estimation required for reinforcement skeletons with rebar planes in various directions. This
-
Robotic motion planning for autonomous in-situ construction of building structures Autom. Constr. (IF 9.6) Pub Date : 2025-02-01 Cong Zhao, Jian-Ye Chen, Tao Sun, Wei Fan, Xiao-Yan Sun, Yi Shao, Guan-Qin Guo, Hai-Long Wang
The in-situ construction of building structures with autonomous robotic systems holds great promise for addressing labor shortages and increasing people’s quality of living. Motion planning is a fundamental component in developing such systems, as it ensures that the actions of construction robots adhere to constraints and adapt to uncertainties. This review critically surveyed related studies from
-
Cogeneration method for crack images and masks Autom. Constr. (IF 9.6) Pub Date : 2025-02-01 Xun Zhang, Jianming Ding, Yutao Wang, Kaiyun Wang
The scarcity of sleeper crack samples limits the effectiveness of deep learning detection methods. Traditional data augmentation produces similar images, whereas classic GANs require manual mask annotation, increasing complexity. This paper proposes MaskGAN, a model that generates crack images and corresponding masks simultaneously in a single-step generation. MaskGAN maintains background consistency
-
Automated large-scale additive manufacturing of structural formwork with rapid fibre-reinforced polymer tape lamination Autom. Constr. (IF 9.6) Pub Date : 2025-02-01 Zhuo-Yang Xin, Guan-Qi Zhu, Joseph M. Gattas, Dan Luo
A fully automated additive lamination manufacturing (ALM) system was developed for producing large-scale fibre-reinforced polymer (FRP) structural formworks. Automation is achieved through the integration of a UV-curable resin matrix with glass fibre tape and a robotic arm end-effector. This paper presents details of the hardware and material development for the UV-based ALM system, as well as performance
-
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 He
Robotic 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 Rosmansyah
Technological 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 Zhang
Accurate 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 Li
Video 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 Liu
Point 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 Chen
This 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. Ahn
Construction 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 Choi
Construction 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 Wang
Predicting 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 Tong
A 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 Borrmann
Automated 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 Shojaei
Construction 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 Lau
As 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 Liu
Existing 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 Nalpantidis
This 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 Deng
Increasing 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 Hadjidemetriou
The 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 Dillenburger
The 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. Cheng
Fire 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 Lalonde
The 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 Hou
Accurately 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 Deng
Construction 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 Luo
Wadangs (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 Tan
Accidents 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 Wang
To 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 Guo
Precise 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 Lin
In 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 Song
Field 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 Xu
Highway 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 Mao
Buried 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 Ahn
This 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 Mizobuchi
Autonomous 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
-
Corrigendum to: “Improved multi-body rope approach for free-form gridshell structures using equal-length element strategy.” [Automation in Construction 161 (2024): 105340]. Autom. Constr. (IF 9.6) Pub Date : 2025-01-18 Amedeo Manuello Bertetto, Jonathan Melchiorre, Giuseppe Carlo Marano
-
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 Wang
The 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 Liu
Deformation 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 Xie
Artificial 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 Chen
The 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 Maldague
This 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 Brilakis
Deep 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 Qiao
To 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 Gui
Ground-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 Li
Computer 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 Chen
This 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 Cui
The 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