Automation in Construction最新文献_第10页

3D reconstruction and dimension recovery algorithm for architectural structures using sequential images and photography trajectories 基于序列图像和摄影轨迹的建筑结构三维重建与尺寸恢复算法

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-11 DOI: 10.1016/j.autcon.2025.106465

Jianxiong Zhang , Hongxing Qiu , Yunlong Lu , Jian Sun , Guanqi Lan

{"title":"3D reconstruction and dimension recovery algorithm for architectural structures using sequential images and photography trajectories","authors":"Jianxiong Zhang , Hongxing Qiu , Yunlong Lu , Jian Sun , Guanqi Lan","doi":"10.1016/j.autcon.2025.106465","DOIUrl":"10.1016/j.autcon.2025.106465","url":null,"abstract":"<div><div>Accurate 3D reconstruction and dimension recovery of engineering structures are crucial for dimensional inspection, whereas classic SfM + MVS reconstruction only generates scaled similar models. To address this issue, a 3D reconstruction and dimension recovery algorithm using sequential images and photography trajectories is proposed. The algorithm improves traditional 3D reconstruction by integrating inertial measurement unit (IMU) data to estimate photography trajectories, down-sampling and time-aligning trajectory points using timestamps of sequential images as reference, and solving the scale factor via data fusion to recover absolute dimensions. Laboratory validation on specimens shows that the proposed algorithm achieves a 0.58 % mean relative error (MRE) between calculated and measured dimensions, confirming high accuracy; further validation on practical structures demonstrates that, paired with the smart terminal-based mobile scheme, the proposed algorithm yields 1.29 % MRE for small-scale components and 2.73 % for large-scale structures, further verifying the engineering practicability.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106465"},"PeriodicalIF":11.5,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809503","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

Automated Robotic Assembly Planning of Space Trusses for Injection 3D Concrete Printing 喷射式3D混凝土打印空间桁架自动化机器人装配规划

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-11 DOI: 10.1016/j.autcon.2025.106440

Yinan Xiao , Aileen Vandenberg , Dirk Lowke , Inka Mai , Pierluigi D’Acunto , Harald Kloft , Norman Hack

引用次数: 0

Generative design for modular construction structures based on expert knowledge-informed graph neural networks and meta-heuristics 基于专家知识的图神经网络和元启发式的模块化建筑结构生成设计

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-11 DOI: 10.1016/j.autcon.2025.106463

Xueqing Li, Weisheng Lu, Ziyu Peng

引用次数: 0

Integrating hierarchical segmentation and vision-language reasoning for spatially complex and occluded MEP point clouds 结合层次分割和视觉语言推理的空间复杂遮挡MEP点云

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-06 DOI: 10.1016/j.autcon.2025.106455

Mingkai Li , Vincent J.L. Gan , Boyu Wang

{"title":"Integrating hierarchical segmentation and vision-language reasoning for spatially complex and occluded MEP point clouds","authors":"Mingkai Li , Vincent J.L. Gan , Boyu Wang","doi":"10.1016/j.autcon.2025.106455","DOIUrl":"10.1016/j.autcon.2025.106455","url":null,"abstract":"<div><div>3D BIM reconstruction for MEP systems reduces manual documentation and enhances asset information management. However, the complexity of real-world MEP scenes, characterized by their non-linear trajectories within dense and cluttered environment, frequent data incompleteness due to occlusions, poses significant challenge for instance segmentation and geometric modeling. This paper proposes a hierarchical and progressive segmentation framework that integrates deep learning-based semantic segmentation, geometry-driven instance segmentation, and vision-language model-assisted (VLM-assisted) segmentation refinement. The semantic segmentation module achieves an overall accuracy of 87.02 % and mIoU of 69.10 %, with true positive rates exceeding 97 % for pipe, duct, and tray systems. A voxel-based DBSCAN algorithm is developed to enhance clustering stability and efficiency, followed by an improved RANSAC to extract directional primitives. In addition, VLM-assisted 2D projection analysis is introduced to refine segmentation boundaries and support downstream geometric modeling. Experimental results across multiple MEP systems demonstrate that the proposed approach achieves high segmentation accuracy and computational efficiency, without relying on large-scale annotated instance training data.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106455"},"PeriodicalIF":11.5,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780360","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

Enhancing robustness of deep learning models against adversarial attacks for construction worker safety 增强深度学习模型对建筑工人安全对抗性攻击的鲁棒性

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-06 DOI: 10.1016/j.autcon.2025.106447

Sharjeel Anjum, Chukwuma Nnaji

{"title":"Enhancing robustness of deep learning models against adversarial attacks for construction worker safety","authors":"Sharjeel Anjum, Chukwuma Nnaji","doi":"10.1016/j.autcon.2025.106447","DOIUrl":"10.1016/j.autcon.2025.106447","url":null,"abstract":"<div><div>The increasing use of deep neural networks (DNNs) in construction safety systems highlights their potential but also reveals vulnerabilities to adversarial perturbations. Such weaknesses can lead to false detections, increasing the risk of accidents on dynamic construction sites. This paper advances construction safety research by developing a framework to enhance AI robustness through adversarial training (AT) using the TRADES method with <span><math><msub><mi>L</mi><mo>∞</mo></msub></math></span> and <span><math><msub><mi>L</mi><mn>2</mn></msub></math></span> norms on a ResNet-18 architecture. The approach was evaluated using a combined dataset of publicly available construction images and custom-collected lab data representing unsafe behaviors. Results show the adversarially trained model achieved 92.50 % benign accuracy and 90.36 % robust accuracy under L₂ attacks. To assess model transparency, LIME (Local Interpretable Model-Agnostic Explanations) was used to visualize regions influencing predictions for both benign and adversarial inputs. These improvements support safer, AI-assisted monitoring in real-world settings by enabling more reliable decision-making and reducing the risk of AI system failures.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106447"},"PeriodicalIF":11.5,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780361","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

Automated concrete damage detection using GPR: A universal solver based on AI-assisted relative permittivity estimation 基于探地雷达的混凝土损伤自动检测：基于人工智能辅助相对介电常数估计的通用求解器

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-06 DOI: 10.1016/j.autcon.2025.106453

Jinyoung Hong , Minju Kang , Hajin Choi , Shibin Lin , Heng Liu , Hoda Azari

{"title":"Automated concrete damage detection using GPR: A universal solver based on AI-assisted relative permittivity estimation","authors":"Jinyoung Hong , Minju Kang , Hajin Choi , Shibin Lin , Heng Liu , Hoda Azari","doi":"10.1016/j.autcon.2025.106453","DOIUrl":"10.1016/j.autcon.2025.106453","url":null,"abstract":"<div><div>Ground-penetrating radar (GPR) has recently been adopted for detecting damage in concrete based on relative permittivity variations. However, its practical applicability remains limited due to the need for pre-known parameters such as wave velocity or rebar depth. This paper proposes an automated algorithm that back-calculates relative permittivity from electromagnetic responses without requiring any prior structural information. Leveraging AI-assisted analysis, a YOLO-based model detects rebar-induced reflections and estimates permittivity. The algorithm was validated in three phases: (1) testing on a mock-up slab with artificial voids; (2) application to a deteriorated bridge deck, benchmarked against impact-echo results; and (3) deployment on an in-service reinforced concrete bridge. Results demonstrate high detection accuracy and significantly enhanced efficiency, enabling robust performance across varying GPR datasets. The proposed algorithm also functions as a universal solver compatible with diverse structures and equipment, advancing the automation of GPR interpretation and its broader application in civil infrastructure assessment.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106453"},"PeriodicalIF":11.5,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144779599","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

Comparative analysis of fire evacuation decision-making in immersive vs. non-immersive virtual reality environments 沉浸式与非沉浸式虚拟现实环境下火灾疏散决策的比较分析

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-06 DOI: 10.1016/j.autcon.2025.106441

Yuxuan Zhang , Daniel Paes , Zhenan Feng , Dorothy Scorgie , Peijin He , Ruggiero Lovreglio

{"title":"Comparative analysis of fire evacuation decision-making in immersive vs. non-immersive virtual reality environments","authors":"Yuxuan Zhang , Daniel Paes , Zhenan Feng , Dorothy Scorgie , Peijin He , Ruggiero Lovreglio","doi":"10.1016/j.autcon.2025.106441","DOIUrl":"10.1016/j.autcon.2025.106441","url":null,"abstract":"<div><div>Understanding emergency behavior is crucial for designing safer, resilient infrastructure. Immersive Virtual Reality (VR) realistically simulates emergencies but is resource-intensive, so systematic comparisons with non-immersive VR remain scarce. To address this gap, a multifactorial VR fire-evacuation experiment was conducted in which participants navigated a room with three exits under varied conditions (e.g., social influence, smoke presence, exit distance, exit familiarity). Results indicated no significant difference in overall decision-making between immersive and non-immersive VR. Nevertheless, immersion modulated key factors: in immersive VR, participants preferred nearer exits, were more susceptible to social influence, and experienced stronger effects of smoke and exit familiarity. Smoke also reduced the influence of exit distance. Personal factors (e.g., prior VR experience, age, gender) shaped perceptions and emotions; heightened negative emotions and perceived risk were associated with less rational (i.e., suboptimal) choices, particularly in immersive VR. These insights inform VR safety training, guiding simulations that more faithfully replicate real emergencies.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106441"},"PeriodicalIF":11.5,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144779597","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

Multi-granular crew activity recognition for construction monitoring 用于施工监控的多粒度班组活动识别

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-06 DOI: 10.1016/j.autcon.2025.106428

Cheng Yun Tsai, Mik Wanul Khosiin, Jacob J. Lin, Chuin-Shan Chen

引用次数: 0

High-dimensional railway vertical alignment optimization using hybrid differential evolution and gradient descent 基于混合差分演化和梯度下降的高维铁路垂直线形优化

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-05 DOI: 10.1016/j.autcon.2025.106413

Dongying Yang , Qing He , Honghui Wang , Yan Gao , Senhao Zhang , Guangle Yao , Meng Zhou

引用次数: 0

Digital twin modelling approaches and applications in urban infrastructure operations and maintenance 数字孪生模型方法及其在城市基础设施运营和维护中的应用

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-05 DOI: 10.1016/j.autcon.2025.106445

Zhansheng Liu , Yanchi Mo , Benwei Hou , Mingming Li , Weiyi Li , Chengshun Xu

引用次数: 0