Automation in Construction最新文献_第6页

Real-time detection of water-bearing structures in tunneling using the long-electrode while-drilling resistivity method 随钻长电极电阻率法实时探测隧道含水构造

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-09-09 DOI: 10.1016/j.autcon.2025.106528

Lichao Nie, Zhenhua Pan, Shixun Jia, Zhi-Qiang Li, Yue Xiao

{"title":"Real-time detection of water-bearing structures in tunneling using the long-electrode while-drilling resistivity method","authors":"Lichao Nie, Zhenhua Pan, Shixun Jia, Zhi-Qiang Li, Yue Xiao","doi":"10.1016/j.autcon.2025.106528","DOIUrl":"10.1016/j.autcon.2025.106528","url":null,"abstract":"<div><div>In tunnel engineering, water-related hazards such as water inrush and mud pose severe safety risks, especially in water-rich or karst environments. Traditional resistivity methods have limitations in real-time data acquisition and construction coordination. This paper proposes a long electrode source while-drilling (LEWD) resistivity method. Utilizing the drill rod as a current electrode and synchronously acquiring observation data from the tunnel face electrode array, LEWD enables continuous, automated monitoring without interrupting excavation. A 3D finite element forward model is optimized using Cholesky decomposition to improve computational efficiency, while a time-windowed iterative inversion strategy enhances the stability and accuracy of resistivity imaging. Based on this framework, numerical simulations verify the method's ability to accurately identify low-resistivity anomalies under various conditions. Furthermore, field tests in the Xianglu Mountain tunnel in China demonstrate that the LEWD method can effectively detect water-bearing structures ahead of the tunnel face. This method provides effective technical support for water hazard prevention in tunnel construction.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"180 ","pages":"Article 106528"},"PeriodicalIF":11.5,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Virtual worlds in AECO operations: Towards a human-centric framework AECO操作中的虚拟世界：走向以人为中心的框架

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-09-09 DOI: 10.1016/j.autcon.2025.106529

Proboste Martinez Mathias , Mora Serrano Javier , Muñoz La Rivera Felipe

{"title":"Virtual worlds in AECO operations: Towards a human-centric framework","authors":"Proboste Martinez Mathias , Mora Serrano Javier , Muñoz La Rivera Felipe","doi":"10.1016/j.autcon.2025.106529","DOIUrl":"10.1016/j.autcon.2025.106529","url":null,"abstract":"<div><div>The AECO industry faces persistent challenges, including fragmentation, inefficiencies, and limited human-centric integration in digital workflows. While BIM and Digital Twins (DTs) have advanced digitalization, they often lack the immersive, multi-user, and interactive environments crucial for Industry 5.0 and Construction 5.0. This paper presents a comprehensive review of Virtual Worlds (VWs) and their potential for the AECO sector. Based on this review, a human-centric methodological and technological framework is proposed, structured across four synergistic layers: Data & Context; Processing & Simulation; Interaction & Visualization; and Integration & Connectivity. This framework integrates Extended Reality (XR), Artificial Intelligence (AI), and Gaming Technology with existing BIM and DTs data. Its applicability is conceptually illustrated through five research prototypes, demonstrating its potential to guide advancements in collaboration, simulation, visualization, and human-machine interaction in AECO. This work contributes a structured vision and roadmap for fostering more efficient, collaborative, and human-augmented digital practices.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"180 ","pages":"Article 106529"},"PeriodicalIF":11.5,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Electric vehicle charging infrastructure design: Expertise, methods, and challenges 电动汽车充电基础设施设计：专业知识、方法和挑战

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-09-07 DOI: 10.1016/j.autcon.2025.106521

Dong Zhao , Hongyu Hu , Cornelia Asiedu-Kwakyewaa , Noah Durst , Janice Beecher , Linlang He , Lei Shu

{"title":"Electric vehicle charging infrastructure design: Expertise, methods, and challenges","authors":"Dong Zhao , Hongyu Hu , Cornelia Asiedu-Kwakyewaa , Noah Durst , Janice Beecher , Linlang He , Lei Shu","doi":"10.1016/j.autcon.2025.106521","DOIUrl":"10.1016/j.autcon.2025.106521","url":null,"abstract":"<div><div>This review synthesizes the research landscape on electric vehicle charging infrastructure design to identify key methods, challenges, and future directions relevant to the architecture, engineering, and construction (AEC) industry. The analysis reveals that research is dominated by two problems: station location planning and charging technology innovation, which are primarily framed using Optimization Modeling and driven by traffic and geospatial data. A key contribution of this review is the clarification of the methodological hierarchy, from these high-level formulation approaches down to the specific computational techniques (e.g., Mixed-Integer Programming) and executable algorithms (e.g., Genetic Algorithm) used to find solutions. Critically, the review identifies the need for a strategic shift from reactively accommodating demand to proactive planning that uses infrastructure to guide market growth. Future work must resolve the core engineering trade-off between network capacity expansion and technological efficiency and integrate social and regional equity as a formal constraint in system design.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"180 ","pages":"Article 106521"},"PeriodicalIF":11.5,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced MEP construction progress tracking using panoramic mobile positioning and optimized pipeline segmentation 利用全景移动定位和优化的管道分割增强环保部建设进度跟踪

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-09-06 DOI: 10.1016/j.autcon.2025.106487

Wei Wei , Yujie Lu , Ruihan Bai , Lijian Zhong , Yufan Chen , Yijun Lin , Jack C.P. Cheng

{"title":"Enhanced MEP construction progress tracking using panoramic mobile positioning and optimized pipeline segmentation","authors":"Wei Wei , Yujie Lu , Ruihan Bai , Lijian Zhong , Yufan Chen , Yijun Lin , Jack C.P. Cheng","doi":"10.1016/j.autcon.2025.106487","DOIUrl":"10.1016/j.autcon.2025.106487","url":null,"abstract":"<div><div>Efficient progress management is pivotal for the successful delivery of MEP construction projects. While non-intrusive methods, such as image recognition, hold promise in enhancing progress management efficiency, several challenges in MEP scenarios, particularly irregular and sparse features, may constrain the progress recognition accuracy. This paper proposes a framework for automated MEP construction progress tracking, which integrates panoramic mobile positioning, instance segmentation, and Unreal Engine to compare virtual (as-planned) and real (as-built) MEP construction scenes. Moreover, quantitative progress can be estimated by identifying and mapping unfinished construction components. Remarkably, dynamic snake convolution is introduced to enhance the component segmentation accuracy, specifically for irregular and sparse features. The framework is successfully applied to an MEP construction project in China, achieving commendable component segmentation (mAP<sub>50</sub> = 85 %) and progress recognition accuracy. This paper provides theoretical references for vision-based MEP construction progress tracking, offering practical insights for intelligent construction inspection in continuous spaces.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"180 ","pages":"Article 106487"},"PeriodicalIF":11.5,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent quality assessment of concrete vibration using computer vision and large language models 基于计算机视觉和大语言模型的混凝土振动质量智能评估

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-09-06 DOI: 10.1016/j.autcon.2025.106507

Tan Li , Hong Wang , Jiasheng Tan , Lingjie Kong , Haoran Zhang , Dongxu Pan , Zhihao Zhao

{"title":"Intelligent quality assessment of concrete vibration using computer vision and large language models","authors":"Tan Li , Hong Wang , Jiasheng Tan , Lingjie Kong , Haoran Zhang , Dongxu Pan , Zhihao Zhao","doi":"10.1016/j.autcon.2025.106507","DOIUrl":"10.1016/j.autcon.2025.106507","url":null,"abstract":"<div><div>The monitoring of concrete vibration quality is crucial for ensuring construction quality. This paper proposes a monitoring method that combines computer vision and Large Language Model (LLM). First, an unsupervised shadow removal method is used to optimize image quality. Next, a multi-head classification model is applied to conduct a multi-dimensional comprehensive assessment of vibration quality. After that, the classification results are mapped to natural language information through a key-value image-to-text mapping method. Finally, the natural language is used for inference in the LLM to generate real-time feedback. Experimental results show that the proposed method achieves an accuracy of 94.45 % in classifying the vibration quality. Additionally, by combining image classification results with LLM for logical reasoning and feedback generation, the system can provide detailed descriptions of compaction quality and corresponding solutions. This research has been successfully applied in real-world projects and is expected to promote the intelligent development of construction operations.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"180 ","pages":"Article 106507"},"PeriodicalIF":11.5,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-time detection of highway tunnel lining cracks using YOLOv8-DTD with an android application 基于YOLOv8-DTD的公路隧道衬砌裂缝实时检测

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-09-06 DOI: 10.1016/j.autcon.2025.106524

Yong Liu , Zhi-Feng Wang , Ya-Qiong Wang , Li-Wei Zhou , Xing-Kai Li , Xiang-Hui Ding

{"title":"Real-time detection of highway tunnel lining cracks using YOLOv8-DTD with an android application","authors":"Yong Liu , Zhi-Feng Wang , Ya-Qiong Wang , Li-Wei Zhou , Xing-Kai Li , Xiang-Hui Ding","doi":"10.1016/j.autcon.2025.106524","DOIUrl":"10.1016/j.autcon.2025.106524","url":null,"abstract":"<div><div>The heavy, memory-intensive nature of existing detection models limits their applicability for efficient crack recognition on mobile and embedded devices with constrained resources. To address this issue, this paper proposes YOLOv8-DTD, a real-time detection model for identifying tunnel lining cracks that integrates Deformable Convolutional Network v2 (DCNv2) and a Transformer Decoder. DCNv2 enhances precise and swift detection of crack deformation features, while the Transformer Decoder optimises the end-to-end process and eliminates computational costs associated with anchor-free methods. The model subsequently was deployed in an Android application for automatic real-time crack detection on smartphones. Results show that YOLOv8-DTD achieves 10.84 % and 9.31 % improvements in <em>mAP</em> and <em>F</em>1 score, respectively, while reducing parameters by 43.21 % and reaching 65.46 frames per second, evaluated on a dataset comprising lining cracks from 141 highway tunnels in Shaanxi Province, China. Moreover, detection efficiency is further validated via Jetson Nano acceleration and field feasibility testing.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"180 ","pages":"Article 106524"},"PeriodicalIF":11.5,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Masked gaussian fields for automated building surface meshing from multi-view images 遮罩高斯场用于多视图图像的自动建筑表面网格划分

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-09-04 DOI: 10.1016/j.autcon.2025.106502

Tengfei Wang, Zongqian Zhan, Rui Xia, Linxia Ji, Xin Wang

{"title":"Masked gaussian fields for automated building surface meshing from multi-view images","authors":"Tengfei Wang, Zongqian Zhan, Rui Xia, Linxia Ji, Xin Wang","doi":"10.1016/j.autcon.2025.106502","DOIUrl":"10.1016/j.autcon.2025.106502","url":null,"abstract":"<div><div>Over the last few decades, automated image-based building surface reconstruction has garnered substantial research interest and has been applied across various fields, such as heritage preservation, architectural planning, etc. Compared to the traditional photogrammetric and NeRF-based solutions, recently, Gaussian fields-based methods have exhibited significant potential in generating surface meshes due to their time-efficient training and detailed 3D information preservation. However, most gaussian fields-based methods are trained with all image pixels, encompassing building and nonbuilding areas, which results in significant noise for building meshes and a degeneration in time efficiency. This paper proposes a framework, Masked Gaussian Fields (MGFs), designed to generate accurate surface reconstruction for building in a time-efficient way. The framework first applies EfficientSAM and COLMAP to generate multi-level masks of the building and the corresponding masked point clouds. Subsequently, the masked gaussian fields are trained by integrating two innovative losses: a multi-level perceptual masked loss focused on constructing building regions and a boundary loss aimed at enhancing the details of the boundaries between different masks. Finally, this paper improves the tetrahedral surface mesh extraction method based on the masked gaussian spheres. Comprehensive experiments on UAV images demonstrate that, compared to the traditional method and several NeRF-based and Gaussian-based SOTA solutions, this approach significantly improves both the accuracy and efficiency of building surface reconstruction. Notably, as a byproduct, there is an additional gain in the novel view synthesis of the building.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"180 ","pages":"Article 106502"},"PeriodicalIF":11.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent shield machine selection for subway tunnel using machine learning 基于机器学习的地铁隧道智能盾构机选型

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-09-04 DOI: 10.1016/j.autcon.2025.106492

Jichen Xie , Jinyang Fu , Haoyu Wang , Junsheng Yang

引用次数: 0

Graph-convolutional neural networks for predicting tunnel boring machine performance 用于隧道掘进机性能预测的图卷积神经网络

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-09-03 DOI: 10.1016/j.autcon.2025.106436

Haibo Li , Zhiguo Zeng , Xu Li , Min Yao

引用次数: 0

Digital-physical integration for MEP-structural clash coordination using path planning and AR navigation 基于路径规划和AR导航的mep -结构冲突协调的数字物理集成

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-09-02 DOI: 10.1016/j.autcon.2025.106501

Wenyu Xu , Penglu Chen , Hung-Lin Chi , Shenghua Wang , Songbai Zhou

{"title":"Digital-physical integration for MEP-structural clash coordination using path planning and AR navigation","authors":"Wenyu Xu , Penglu Chen , Hung-Lin Chi , Shenghua Wang , Songbai Zhou","doi":"10.1016/j.autcon.2025.106501","DOIUrl":"10.1016/j.autcon.2025.106501","url":null,"abstract":"<div><div>In current construction practice, MEP-structural clash coordination remains a critical efficiency problem, where BIM-based detection methods suffer from excessive irrelevant clashes and inadequate management, particularly in complex intersection areas. This paper proposes a clash coordination system that integrates path planning with augmented reality (AR) navigation to optimize MEP-structural clash coordination in construction. The proposed system includes three parts: Lee and Kim (2014) (1) A* and genetic algorithm (GA) path optimization with multi-criteria constraints (i.e., path length, clash priority, and turning costs), Pärn et al. (2018) (2) AR-guided worker navigation using ORB-SLAM3 tracking, and (Akhmetzhanova et al., 2022 (3)) a synchronized digital-physical system connecting mobile application with AR HMD application. Experimental validation at a 1930.54 m<sup>2</sup> construction site demonstrated a 51.0 % shorter coordination distance and 63.89 % faster coordination time compared to the conventional MEP visualization-only approach. Overall, these results demonstrate that the proposed system can enhance MEP-structural clash coordination efficiency through digital-physical task linkage, priority-based sequencing, and integrated collaboration between on-site and off-site teams.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106501"},"PeriodicalIF":11.5,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0