Automation in Construction最新文献

Network analysis and graph neural network (GNN)-based link prediction of construction hazards 基于网络分析和图神经网络（GNN）的施工环节危害预测

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-30 DOI: 10.1016/j.autcon.2025.106302

Brian H.W. Guo , Qilan Li , Wen Yi , Bowen Ma , Zhe Zhang , Yonger Zuo

{"title":"Network analysis and graph neural network (GNN)-based link prediction of construction hazards","authors":"Brian H.W. Guo , Qilan Li , Wen Yi , Bowen Ma , Zhe Zhang , Yonger Zuo","doi":"10.1016/j.autcon.2025.106302","DOIUrl":"10.1016/j.autcon.2025.106302","url":null,"abstract":"<div><div>Hazard recognition is critical for construction safety, especially for accident prevention. Traditional methods often fail to capture the dynamic and interdependent nature of construction hazards. To address this issue, this paper proposes a network-based framework that conceptualizes construction hazards as dynamic interactions between objects with hazardous attributes. A link prediction model using Graph Neural Networks (GNNs) is integrated in this framework to automatically explore latent interactions between hazard objects that are ignored by the existing dataset. By analyzing 4470 construction accident reports, this paper constructed a hazard network and revealed key structural properties, including hazard object centrality, cliques, and communities. The experimental results of link prediction showed that the GNN-based model demonstrated superior performance compared to traditional methods, with 81 % of GNN-predicted links validated by actual construction accident cases. This framework provides a practical solution for intelligent hazard recognition and proactive risk management in the construction industry.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106302"},"PeriodicalIF":9.6,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170491","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

Data integration for space-aware Digital Twins of hospital operations 医院操作的空间感知数字孪生数据集成

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-30 DOI: 10.1016/j.autcon.2025.106276

Nicola Moretti , Yin-Chi Chan , Momoko Nakaoka , Anandarup Mukherjee , Jorge Merino , Ajith Kumar Parlikad

{"title":"Data integration for space-aware Digital Twins of hospital operations","authors":"Nicola Moretti , Yin-Chi Chan , Momoko Nakaoka , Anandarup Mukherjee , Jorge Merino , Ajith Kumar Parlikad","doi":"10.1016/j.autcon.2025.106276","DOIUrl":"10.1016/j.autcon.2025.106276","url":null,"abstract":"<div><div>Healthcare facilities are complex systems where operational efficiency depends on space, processes, resources, and logistics. While many studies propose process-simulation-based improvements, few dynamically consider the built space’s effect on process efficiency. The critical challenge here is the effective integration of data from these disparate domains. This article addresses this challenge by proposing an open Building Information Modelling (BIM) to Discrete Event Simulation (DES) data integration framework towards the development of a space-aware process Digital Twin (DT), with the goal of determining and controlling the impact of spatial layout and built asset performance on core-process throughput. A case study of a multi-storey Histopathology laboratory demonstrates how the impact of changes in travel time between process stages, due to a faulty lift and functional re-configurations, on laboratory turnaround time can be managed integrating up-to-date building information in modelling core business processes. This is achieved through the space-aware process DT architecture.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106276"},"PeriodicalIF":9.6,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170490","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

Semi-autonomous aerial robot for ultrasonic assessment of crack depth and surface velocity in concrete structures 混凝土结构裂缝深度和表面速度超声评估的半自主航空机器人

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-29 DOI: 10.1016/j.autcon.2025.106296

Luca Belsito , Diego Marini , Luca Masini , Matteo Ferri , Miguel Ángel Trujillo , Antidio Viguria , Alberto Roncaglia

引用次数: 0

Automated detection and quantification of structural component dimensions using segment anything model (SAM)-based segmentation 基于分段任意模型（SAM）的结构构件尺寸自动检测与量化

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-29 DOI: 10.1016/j.autcon.2025.106304

Gang Xu , Yingshui Zhang , Qingrui Yue , Xiaogang Liu

{"title":"Automated detection and quantification of structural component dimensions using segment anything model (SAM)-based segmentation","authors":"Gang Xu , Yingshui Zhang , Qingrui Yue , Xiaogang Liu","doi":"10.1016/j.autcon.2025.106304","DOIUrl":"10.1016/j.autcon.2025.106304","url":null,"abstract":"<div><div>This paper presents a method for automatic detection and quantification of full cross-sectional dimensions of structural components using oblique photography and the SAM-dimension (Segment Anything Model-dimension) model. Unlike traditional methods that measure only a single cross-section, this approach enables full cross-sectional dimension detection across the entire component, enhancing efficiency and coverage. The method utilizes the camera's crosshair position within the component area to adjust the model's prompt module strategy, allowing operation without human intervention. A binary mask is generated by fusing the model's output with the original image. Additionally, the method incorporates local unit methods, connected domain analysis, and mask feature extraction for dimension quantification. Experimental results show the model's adaptability to varying distances and lighting, accurately segmenting square, circular, and variable cross-section components. The relative errors for column components' cross-sectional width and height are within 1.22 %, and for beam-type components, within 3.02 %, demonstrating robust and generalized performance.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106304"},"PeriodicalIF":9.6,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170486","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

Geometrically consistent energy-derivative attention CNN for semantic segmentation of multicategory structural damage 几何一致能量导数关注CNN用于多类别结构损伤的语义分割

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-29 DOI: 10.1016/j.autcon.2025.106300

Xin Jing , Zhanxiong Ma , Tao Zhang , Yu Wang , Ruixian Huang , Yang Xu , Qiangqiang Zhang

{"title":"Geometrically consistent energy-derivative attention CNN for semantic segmentation of multicategory structural damage","authors":"Xin Jing , Zhanxiong Ma , Tao Zhang , Yu Wang , Ruixian Huang , Yang Xu , Qiangqiang Zhang","doi":"10.1016/j.autcon.2025.106300","DOIUrl":"10.1016/j.autcon.2025.106300","url":null,"abstract":"<div><div>Engineering structural damage often exhibits diverse and complex features across multiple scales within small-scale regions of interest (ROI), complicating post-earthquake assessments. This paper proposes an interpretable deep learning (DL) framework for semantic segmentation of multicategory damage. Energy-derivative attention modules are integrated into convolutional neural networks (CNNs) to enhance feature extraction of small-scale ROI. Geometrically consistent and focal-informed (GCF) loss function emphasizes the regions and boundaries of small-scale ROI, incorporating geometrical constraints of split line length, curvature, and area. Mosaic data augmentation method further mitigates feature imbalance. The proposed method outperforms the baseline with an mIoU increase from 80.67 % to 88.88 %. IoU for concrete spalling reaches 89.16 %, and for bar buckling improves to 82.96 %. The synergy of geometrical consistency, energy-derivative attention, and mosaic augmentation method significantly enhances CNN performance for multicategory damage. Finally, the framework is deployed in graphical user interface (GUI) software, enabling structural assessment of post-earthquake buildings.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106300"},"PeriodicalIF":9.6,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170487","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

Subsurface utility detection and augmented reality visualization using GPR and deep learning 利用探地雷达和深度学习进行地下效用探测和增强现实可视化

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-29 DOI: 10.1016/j.autcon.2025.106299

Mahmoud Hamdy Safaan , Mahmoud Metawie , Mohamed Marzouk

{"title":"Subsurface utility detection and augmented reality visualization using GPR and deep learning","authors":"Mahmoud Hamdy Safaan , Mahmoud Metawie , Mohamed Marzouk","doi":"10.1016/j.autcon.2025.106299","DOIUrl":"10.1016/j.autcon.2025.106299","url":null,"abstract":"<div><div>Recent urban revitalisation requires advanced utility management and innovative technology to achieve high-precision utility management. This paper introduces an automated framework that surpasses traditional methods of subsurface utility detection by integrating Ground Penetrating Radar (GPR), deep learning, and Augmented Reality (AR) to provide an advanced solution for subsurface detection and visualization. GPR data is collected using a multisensory GPR device, which employs antennas operating at different frequency ranges to achieve high-resolution imaging and deep penetration. Subsequently, a Mask R-CNN deep learning model is trained using a custom dataset, integrating transfer learning and data augmentation to improve detection reliability. The results are refined through profile alignment and Non-Maximum Suppression to increase accuracy. Finally, the detected utilities are visualized through a developed AR application incorporating spatial mapping and anchoring for precise model alignment and tracking. The developed system demonstrates promising results, providing an efficient utility detection and visualization solution.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106299"},"PeriodicalIF":9.6,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170489","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

Knowledge graph for policy- and practice-aligned life cycle analysis and reporting 与政策和实践相一致的生命周期分析和报告的知识图谱

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-28 DOI: 10.1016/j.autcon.2025.106282

Conor Shaw , Flávia de Andrade Pereira , Martijn de Riet , Cathal Hoare , Karim Farghaly , James O’Donnell

{"title":"Knowledge graph for policy- and practice-aligned life cycle analysis and reporting","authors":"Conor Shaw , Flávia de Andrade Pereira , Martijn de Riet , Cathal Hoare , Karim Farghaly , James O’Donnell","doi":"10.1016/j.autcon.2025.106282","DOIUrl":"10.1016/j.autcon.2025.106282","url":null,"abstract":"<div><div>The built environment is a key leverage point for policy intervention to combat climate change and the statutory reporting of financial and non-financial indicators over the asset lifecycle is increasingly required. This poses significant information management challenges in a sector characterised by complexity. Contributions to-date which address Life Cycle Asset Information Management (LCAIM) remain siloed and difficult to generalise, resulting in limited in-practice uptake, but domain literature identifies graph databases and ontologies as suitable strategies for addressing this information-intensive challenge. This paper provides a LCAIM ontology, co-developed with stakeholders, and verified technically through implementation in a case study by responding to end-user-defined storage, retrieval, and enrichment functions using a knowledge graph. The prototype is then validated qualitatively with experts who perceive it as addressing collective governance-practice requirements. Overall, the study suggests that addressing technical LCAIM challenges may be feasible using available technologies and recommends prioritising research towards socio-economic issues.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106282"},"PeriodicalIF":9.6,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154889","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

Semantic digital twin framework for monitoring construction workflows 用于监控建筑工作流程的语义数字孪生框架

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-27 DOI: 10.1016/j.autcon.2025.106301

Yuan Zheng , Alaa Al Barazi , Olli Seppänen , Hisham Abou-Ibrahim , Christopher Görsch

引用次数: 0

From fragmented data to unified construction safety knowledge: A process-based ontology framework for safer work 从碎片化数据到统一的建筑安全知识：基于过程的安全工作本体框架

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-27 DOI: 10.1016/j.autcon.2025.106293

Kilian Speiser , Sebastian Seiß , Frank Boukamp , Jürgen Melzner , Jochen Teizer

{"title":"From fragmented data to unified construction safety knowledge: A process-based ontology framework for safer work","authors":"Kilian Speiser , Sebastian Seiß , Frank Boukamp , Jürgen Melzner , Jochen Teizer","doi":"10.1016/j.autcon.2025.106293","DOIUrl":"10.1016/j.autcon.2025.106293","url":null,"abstract":"<div><div>Effective knowledge management in construction safety is essential yet challenging. Despite emerging technologies to collect valuable data automatically, it continues to rely on manual input. The heterogeneity of data sources in construction makes it additionally difficult, resulting in a high number of incidents due to late changes in the design. Presented is a unified ontology for construction safety named UNOCS that shares safety knowledge between stakeholders during the construction processes. The UNOCS ontology follows the Linked Open Terms methodology and integrates established concepts, ensuring interoperability with other domain-specific knowledge for multiple use cases: (1) hazard and mitigation planning, (2) conformance checking and control, and (3) incident logging. UNOCS was evaluated through automatic consistency checks, criteria-based assessment, and task-based evaluation. The ontology meets the defined requirements and represents safety-related concepts. Implemented in a machine-readable format, it enables reasoning and seamless knowledge transfer between mitigation planning, safety inspections, and incident reporting.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106293"},"PeriodicalIF":9.6,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139679","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

Co-driven physics and machine learning for intelligent control in high-precision 3D concrete printing 协同驱动物理和机器学习在高精度3D混凝土打印中的智能控制

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-26 DOI: 10.1016/j.autcon.2025.106294

Song-Yuan Geng , Bo-Yuan Cheng , Wu-Jian Long , Qi-Ling Luo , Bi-Qin Dong , Feng Xing

{"title":"Co-driven physics and machine learning for intelligent control in high-precision 3D concrete printing","authors":"Song-Yuan Geng , Bo-Yuan Cheng , Wu-Jian Long , Qi-Ling Luo , Bi-Qin Dong , Feng Xing","doi":"10.1016/j.autcon.2025.106294","DOIUrl":"10.1016/j.autcon.2025.106294","url":null,"abstract":"<div><div>With the increasing demand for precise control in 3D concrete printing, coordinating material rheological properties and printing parameters has become a critical challenge. This paper addresses how to intelligently optimize printing parameters to adapt to varying concrete material attributes and improve printing quality. A dual-path framework co-driven by physical information equations (PIE) and machine learning (ML) is proposed. PIE is embedded into convolutional neural networks (CNN) to enhance rheological properties prediction, while also coupled with the random forest (RF) model to predict printing parameters. Results show this approach efficiently matches yield stress (YS), plastic viscosity (PV), extrusion speed (ES), and printing speed (PS), significantly enhancing printing performance. This research provides construction engineers and 3D printing operators with a physics-guided, interpretable intelligent tool that reduces trial-and-error and improves construction reliability. The integration strategy also opens promising directions for future studies on large-scale printing involving multi-scale material-process-structure optimization and time-dependent rheological modeling.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106294"},"PeriodicalIF":9.6,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134786","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