Automation in Construction最新文献_第2页

Formulating infrastructure restoration as a geospatial multi-project scheduling problem using agent-based simulation 将基础设施修复作为一个地理空间多项目调度问题，采用基于智能体的仿真方法

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-14 DOI: 10.1016/j.autcon.2025.106595

Sihan Cao , Wenying Ji , Dongping Fang , Zaishang Li

{"title":"Formulating infrastructure restoration as a geospatial multi-project scheduling problem using agent-based simulation","authors":"Sihan Cao , Wenying Ji , Dongping Fang , Zaishang Li","doi":"10.1016/j.autcon.2025.106595","DOIUrl":"10.1016/j.autcon.2025.106595","url":null,"abstract":"<div><div>Urban flooding increasingly disrupts transportation networks, requiring efficient coordination of repair operations. This paper addresses how to optimize post-flood road restoration scheduling as a scattered repetitive project in a sophisticated, real-time decision-making environment. The proposed framework integrates agent-based modeling with deep learning, where autonomous repair crews dynamically prioritize tasks based on real-time accessibility predictions from a neural network proxy model. The Beijing case study demonstrated that the accessibility-driven strategy significantly improved recovery of network functionality compared to nearest-first and random approaches, particularly during critical early restoration phases. This improvement matters for emergency managers and infrastructure operators who must rapidly restore community access to vital facilities such as hospitals after disasters. Future research can extend this framework to other hazards and infrastructure systems, incorporating advanced uncertainty quantification, climate-informed risk assessments, and adaptive decision-making mechanisms for enhanced disaster planning.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"181 ","pages":"Article 106595"},"PeriodicalIF":11.5,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314931","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-objective online optimization of building energy systems for improved control smoothness and efficiency 建筑能源系统的多目标在线优化，以提高控制的平滑性和效率

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-14 DOI: 10.1016/j.autcon.2025.106604

Zhe Chen , Fu Xiao , Yongbao Chen

引用次数: 0

Semantic instance segmentation and automated 3D BIM reconstruction for viaduct using LiDAR point clouds and weakly-supervised learning 基于LiDAR点云和弱监督学习的高架桥语义实例分割和自动3D BIM重建

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-14 DOI: 10.1016/j.autcon.2025.106612

Zheng Qiao , Vincent J.L. Gan , Mingkai Li , Kelvin Goh Chun Keong , Lim Pia Lian , Allan Yeo Chen Long

{"title":"Semantic instance segmentation and automated 3D BIM reconstruction for viaduct using LiDAR point clouds and weakly-supervised learning","authors":"Zheng Qiao , Vincent J.L. Gan , Mingkai Li , Kelvin Goh Chun Keong , Lim Pia Lian , Allan Yeo Chen Long","doi":"10.1016/j.autcon.2025.106612","DOIUrl":"10.1016/j.autcon.2025.106612","url":null,"abstract":"<div><div>3D reconstruction of Building Information Models (BIM) for transport infrastructure is challenging due to point cloud incompleteness, uneven density, and variations in structural configurations. This paper presents an AI-based semantic instance segmentation approach that leverages weakly-supervised learning for high-precision segmentation and automated BIM reconstruction of transport infrastructure, focusing on viaducts. The method integrates semantic instance segmentation with voxel-based downsampling and density-based filtering to mitigate data incompleteness and uneven density. Mathematical formulations and algorithms are presented, combining geometric representations and spatial relationships of viaduct components to support BIM modelling. A key contribution consists of integrating weakly-supervised learning to segment uneven, incomplete and structurally diverse point clouds, followed by mathematically grounded formulations for high-precision 3D modelling. Experiments demonstrate that the proposed method achieves 94.72 % overall accuracy and 90.51 % mIoU for segmentation, and BIM accuracy exceeding 85 % within 10 mm tolerance between point clouds and generated models, improving BIM reconstruction of transport infrastructure.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"181 ","pages":"Article 106612"},"PeriodicalIF":11.5,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314984","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

Cost-effective and minimal-intervention BIM information retrieval via condensed multi-LLM agent code generation 成本效益和最小干预BIM信息检索通过浓缩的多llm代理代码生成

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-14 DOI: 10.1016/j.autcon.2025.106585

Pei Troh Koh , Huiyuan Xue , Jun Ma , Jack Chin Pang Cheng

{"title":"Cost-effective and minimal-intervention BIM information retrieval via condensed multi-LLM agent code generation","authors":"Pei Troh Koh , Huiyuan Xue , Jun Ma , Jack Chin Pang Cheng","doi":"10.1016/j.autcon.2025.106585","DOIUrl":"10.1016/j.autcon.2025.106585","url":null,"abstract":"<div><div>The Architecture, Engineering, and Construction (AEC) industry increasingly relies on Building Information Modelling (BIM) to manage complex data. However, manual information retrieval remains inefficient and widespread. Existing automatic approaches focus on Industry Foundation Classes (IFC) format with template-based support, while the potential of large language models (LLMs) to generate retrieval code across diverse domain-specific languages and libraries remains underexplored. This paper introduces Alignment-Refinement Coder for BIM (ARCBIM), an LLM-based system with a ‘prefix’ function alignment module, a ‘suffix’ code refinement module, and an ‘inflexion’ condensed agent-design to improve cost-effectiveness. Evaluation on 80 diverse queries with the Revit C# API showed that ARCBIM reduced the average error count to 1.26 per query, compared with 5.75 for the baseline Standalone Agent. Moreover, 80 % of generated code became usable within three local refinements. This system enhanced flexible BIM data retrieval with limited intervention across multiple complexities, providing a more user-centric automatic retrieval solution.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"181 ","pages":"Article 106585"},"PeriodicalIF":11.5,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314985","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

Self-supervised multi-scale transformer with Attention-Guided Fusion for efficient crack detection 基于注意力引导融合的自监督多尺度变压器裂纹检测方法

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-13 DOI: 10.1016/j.autcon.2025.106591

Blessing Agyei Kyem, Joshua Kofi Asamoah, Eugene Denteh, Andrews Danyo, Armstrong Aboah

{"title":"Self-supervised multi-scale transformer with Attention-Guided Fusion for efficient crack detection","authors":"Blessing Agyei Kyem, Joshua Kofi Asamoah, Eugene Denteh, Andrews Danyo, Armstrong Aboah","doi":"10.1016/j.autcon.2025.106591","DOIUrl":"10.1016/j.autcon.2025.106591","url":null,"abstract":"<div><div>Pavement crack detection has long depended on costly and time-intensive pixel-level annotations, which limit its scalability for large-scale infrastructure monitoring. To overcome this barrier, this paper examines the feasibility of achieving effective pixel-level crack segmentation entirely without manual annotations. Building on this objective, a fully self-supervised framework, Crack-Segmenter, is developed, integrating three complementary modules: the Scale-Adaptive Embedder (SAE) for robust multi-scale feature extraction, the Directional Attention Transformer (DAT) for maintaining linear crack continuity, and the Attention-Guided Fusion (AGF) module for adaptive feature integration. Through evaluations on ten public datasets, Crack-Segmenter consistently outperforms 13 state-of-the-art supervised methods across all major metrics, including mean Intersection over Union (mIoU), Dice score, XOR, and Hausdorff Distance (HD). These findings demonstrate that annotation-free crack detection is not only feasible but also superior, enabling transportation agencies and infrastructure managers to conduct scalable and cost-effective monitoring. This work advances self-supervised learning and motivates pavement cracks detection research.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"181 ","pages":"Article 106591"},"PeriodicalIF":11.5,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314929","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

Transformer-based framework for mapping client requirements to BIM 用于将客户需求映射到BIM的基于变压器的框架

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-13 DOI: 10.1016/j.autcon.2025.106601

Syed Haseeb Shah, Saddiq Ur Rehman, Inhan Kim, Kyung-Eun Hwang

{"title":"Transformer-based framework for mapping client requirements to BIM","authors":"Syed Haseeb Shah, Saddiq Ur Rehman, Inhan Kim, Kyung-Eun Hwang","doi":"10.1016/j.autcon.2025.106601","DOIUrl":"10.1016/j.autcon.2025.106601","url":null,"abstract":"<div><div>Translating heterogeneous, client-authored textual requirements into constructible, information-rich models constitutes a primary impediment to digital transformation in early design phases. Legacy workflows demand high frequency client architect iteration, manual decoding of narrative requirements, and bespoke parametric modeling, introducing latency and inconsistency. This paper introduces an end-to-end automation pipeline that couples advanced Natural Language Processing (NLP) with Building Information Modeling (BIM) to dynamically interpret design intent from user inputs and instantiate corresponding BIM assemblies. A semantic translation layer maps parsed entities to a curated BIM model repository and propagates constraints into the authoring environment. On a multi project evaluation set the framework achieved 92 % mapping accuracy between client inputs and instantiated BIM elements. Embedding this capability enhances requirement traceability, clarifies intent for stakeholders, and enables scalable data driven design analytics. This contribution operationalizes AI assisted construction automation by unifying NLP and BIM within a single extensible workflow.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"181 ","pages":"Article 106601"},"PeriodicalIF":11.5,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314928","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-objective optimization model for automated risk-based inspection planning for concrete bridges 基于风险的混凝土桥梁自动检测规划多目标优化模型

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-13 DOI: 10.1016/j.autcon.2025.106608

Abdelhady Omar , Osama Moselhi

{"title":"Multi-objective optimization model for automated risk-based inspection planning for concrete bridges","authors":"Abdelhady Omar , Osama Moselhi","doi":"10.1016/j.autcon.2025.106608","DOIUrl":"10.1016/j.autcon.2025.106608","url":null,"abstract":"<div><div>This paper addresses the critical challenge of optimizing inspection planning for reinforced concrete bridges, considering budgetary and operational constraints. The developments presented here focus on bridge decks. This entails identifying which bridges require inspections, the optimal timing for these inspections, and the most effective non-destructive evaluation inspection methods to employ. A multi-objective optimization model is developed, leveraging the non-dominated sorting genetic algorithm-II and probabilistic modeling. The developed model strikes a balance between minimizing the structure risk of failure, maximizing inspection effectiveness, and optimizing direct costs and impact costs of inspections. The developed model is expected to provide transportation agencies and infrastructure managers with a robust decision-support tool for automated, efficient inspection planning for this class of bridges, that increases inspection effectiveness and enables condition- and risk-driven utilization of advanced non-destructive evaluation methods. The developments here lay the groundwork for integrating inspection outcomes from these methods in selecting subsequent intervention strategies.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"181 ","pages":"Article 106608"},"PeriodicalIF":11.5,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314986","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 Scan-to-BIM for reinforced concrete bridges using point cloud completion techniques 增强扫描到bim的钢筋混凝土桥梁使用点云完成技术

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-13 DOI: 10.1016/j.autcon.2025.106606

Tao Yang , Yang Zou , Enrique del Rey Castillo , Lei Hou , Jian Zhong

{"title":"Enhancing Scan-to-BIM for reinforced concrete bridges using point cloud completion techniques","authors":"Tao Yang , Yang Zou , Enrique del Rey Castillo , Lei Hou , Jian Zhong","doi":"10.1016/j.autcon.2025.106606","DOIUrl":"10.1016/j.autcon.2025.106606","url":null,"abstract":"<div><div>Scan-to-BIM, the process of capturing the 3D point clouds and converting them into Building Information Models (BIM), is essential for modern bridge management systems. However, occlusion in point clouds poses significant challenges in designing reconstruction approaches and generating high-quality geometric BIM. This paper addresses this challenge by integrating a new point cloud completion module into the existing bridge Scan-to-BIM framework. The proposed module employs an improved point completion network (PCN) model to predict complete geometry from incomplete input, followed by using it to repair occlusions in incomplete point clouds. Its effectiveness was evaluated using both synthetic and real-world point cloud datasets. Experimental results demonstrated that (1) the proposed approach effectively resolves most occlusions in real-world datasets and (2) restores synthetic incomplete point clouds and enhances their geometric similarity to the ground-truth shapes, reducing Chamfer Distance (CD) by an average of 12.68 and increasing the F-score by 7.26 %.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"181 ","pages":"Article 106606"},"PeriodicalIF":11.5,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314934","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-modal vision-driven point cloud registration for efficient fusion of multi-source models in regional building clusters 基于多模态视觉驱动点云配准的区域建筑群多源模型高效融合

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-13 DOI: 10.1016/j.autcon.2025.106580

Penglu Chen , Wen Yi , Bing Li , Zhengrong Gui , Yi Tan

{"title":"Multi-modal vision-driven point cloud registration for efficient fusion of multi-source models in regional building clusters","authors":"Penglu Chen , Wen Yi , Bing Li , Zhengrong Gui , Yi Tan","doi":"10.1016/j.autcon.2025.106580","DOIUrl":"10.1016/j.autcon.2025.106580","url":null,"abstract":"<div><div>Integrating the OPM (Oblique Photogrammetry Model) and BIM (Building Information Model) is a critical challenge in advancing smart city due to difficulties in multi-scale heterogeneous data fusion. This paper presents a method to improve the efficiency and accuracy of automatic multi-source model fusion in regional building clusters. The proposed framework integrates YOLOv10 and SAM to detect and segment building contours from multi-modal images. A ray-tracing method is then applied to unitize buildings within the OPM, enabling accurate localization. To ensure scale consistency, a ring-scanning strategy performs resolution-based sampling of exterior surfaces from both unitized OPM and BIM. For fusion, computer vision algorithms conduct point cloud denoising and coarse registration, which is further refined using the Iterative Closest Point (ICP) algorithm. This method enables seamless fusion of multi-source models into a unified, closed-loop digital twin base, establishing a robust foundation for high-precision data integration and visualization in smart city applications.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"181 ","pages":"Article 106580"},"PeriodicalIF":11.5,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314930","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

Automatic evaluation and analysis of indoor visual comfort for sustainable building design using interpretable ensemble learning 基于可解释集合学习的可持续建筑室内视觉舒适性自动评价与分析

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-12 DOI: 10.1016/j.autcon.2025.106582

Yuxin Zhou , Tomohiro Fukuda , Nobuyoshi Yabuki

{"title":"Automatic evaluation and analysis of indoor visual comfort for sustainable building design using interpretable ensemble learning","authors":"Yuxin Zhou , Tomohiro Fukuda , Nobuyoshi Yabuki","doi":"10.1016/j.autcon.2025.106582","DOIUrl":"10.1016/j.autcon.2025.106582","url":null,"abstract":"<div><div>Sustainable building design increasingly emphasizes daylight access and glare reduction due to their impact on energy efficiency and occupant comfort. However, integrating daylight distribution with dynamic glare risk from an occupant-centered perspective remains a significant challenge. To address this, this paper develops an interpretable Stacking ensemble framework enhanced with SHapley Additive exPlanations (SHAP) method for automated evaluation of indoor visual comfort (IVC). Six ensemble models are optimized through Bayesian optimization and 5-Fold cross-validation. The final Stacking model, which includes ensemble XGBoost, LightGBM, and CatBoost, achieves high predictive accuracy (R<sup>2</sup> = 0.911) and efficient prediction capability. SHAP analysis identifies six key design variables accounting for 80.6 % of the model's contribution, with building forms (46.6–52.7 %) and fenestration features (22.6–24.9 %) as primary factors. The framework provides rapid feedback in early-stage design, supporting data-driven decisions to optimize IVC and integrate performance analysis into occupant-centered design processes.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"181 ","pages":"Article 106582"},"PeriodicalIF":11.5,"publicationDate":"2025-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261710","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