Automation in Construction最新文献_第2页

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

Standardisation framework for metal additive manufacturing in construction 建筑业金属增材制造标准化框架

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-26 DOI: 10.1016/j.autcon.2025.106267

Xin Meng, Leroy Gardner

{"title":"Standardisation framework for metal additive manufacturing in construction","authors":"Xin Meng, Leroy Gardner","doi":"10.1016/j.autcon.2025.106267","DOIUrl":"10.1016/j.autcon.2025.106267","url":null,"abstract":"<div><div>Wire-arc directed energy deposition (DED-Arc), also known as wire arc additive manufacturing (WAAM), brings about unprecedented opportunities in the construction sector to improve material efficiency, enhance automation and reduce embodied carbon. To address the current standardisation gap, a normative framework for the use of DED-Arc in construction is proposed in this paper. The current standardisation landscape for additive manufacturing and steel construction is firstly introduced. The standardisation requirements for DED-Arc in construction, encompassing qualification, structural design and execution, are outlined. A quality classification system for DED-Arc construction products and qualification requirements for the manufacturing process and finished parts are required. Regarding the structural design, modifications and additional rules to the Eurocode 3 are needed for DED-Arc structural components, in terms of design material and geometric properties, design methods, reliability and design for long-term and extreme actions. Finally, additional requirements for the execution of steel structures featuring DED-Arc are discussed.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106267"},"PeriodicalIF":9.6,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134787","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

Two-stage optimization of infinite rotation-freedom façade systems using machine learning surrogate models 基于机器学习代理模型的无限旋转自由度faradade系统两阶段优化

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-26 DOI: 10.1016/j.autcon.2025.106295

Yisu Wang , Shuo Ji , Gang Feng , Chenyu Huang

{"title":"Two-stage optimization of infinite rotation-freedom façade systems using machine learning surrogate models","authors":"Yisu Wang , Shuo Ji , Gang Feng , Chenyu Huang","doi":"10.1016/j.autcon.2025.106295","DOIUrl":"10.1016/j.autcon.2025.106295","url":null,"abstract":"<div><div>Increasing the Degrees Of Freedom (DOFs) of Kinetic Façade Systems (KFS) potentially enhances environmental adaptability but presents challenges in mechanical feasibility and optimization complexity due to high-dimensional design spaces. This paper investigates the mechanism design and optimization strategies for multi-DOF KFS, and assesses the performance trade-offs associated with increased motion and control freedom. An Infinite Rotation Freedom (IRF) prototype is proposed and experimentally validated, and a two-stage surrogate-based optimization framework is developed for multi-DOF façade systems by integrating machine learning-based surrogate models with optimization algorithms for both static feature selection and kinetic motion control. Comparative performance analyses demonstrated that the IRF system significantly improves daylight distribution and thermal regulation compared to conventional louvers, with multi-DOF motion enhancing daylight distribution and increased control freedom enabling more precise glare mitigation. These findings highlight the feasibility and environmental advantages of multi-DOF KFS. Future research should address movement continuity issues to improve operational efficiency.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106295"},"PeriodicalIF":9.6,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134192","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

Weighted adaptive active transfer learning for imbalanced multi-object classification in construction site imagery 基于加权自适应主动迁移学习的建筑工地图像不平衡多目标分类

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-26 DOI: 10.1016/j.autcon.2025.106297

Karunakar Reddy Mannem , Samuel A. Prieto , Borja García de Soto , Fernando Bacao

{"title":"Weighted adaptive active transfer learning for imbalanced multi-object classification in construction site imagery","authors":"Karunakar Reddy Mannem , Samuel A. Prieto , Borja García de Soto , Fernando Bacao","doi":"10.1016/j.autcon.2025.106297","DOIUrl":"10.1016/j.autcon.2025.106297","url":null,"abstract":"<div><div>Construction site monitoring relies on robust image classification to enhance safety, track progress, and optimize resource management. However, the amount of clutter and the high cost of manual labeling pose significant challenges. This paper presents an approach to multi-object classification in construction sites using Adaptive Active Transfer Learning. The Weighted Active Transfer Learning with Adaptive Sampling (WATLAS) framework is introduced, where Transfer Learning is combined with weighted Active Learning to efficiently classify diverse objects. A pre-trained InceptionV3 architecture integrated with bidirectional long short-term memory (BiLSTM) layers is utilized, and superior performance is achieved through adaptive sampling techniques compared to traditional methods. WATLAS achieves 97 % accuracy on a comprehensive dataset of 9344 construction site images spanning 15 object categories and maintaining 90 % accuracy with only 5 % labeled data. By optimizing performance metrics, the framework demonstrates significant improvements over traditional methods, making it a scalable solution for construction site monitoring.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106297"},"PeriodicalIF":9.6,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134788","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

Integrative AI and UAV-based visual recognition with metaheuristics for automated repair cost analysis of bridge structural deterioration 基于人工智能和无人机视觉识别的桥梁结构退化维修成本分析

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-24 DOI: 10.1016/j.autcon.2025.106273

Jui-Sheng Chou , Jhe-Shian Lien , Chi-Yun Liu

{"title":"Integrative AI and UAV-based visual recognition with metaheuristics for automated repair cost analysis of bridge structural deterioration","authors":"Jui-Sheng Chou , Jhe-Shian Lien , Chi-Yun Liu","doi":"10.1016/j.autcon.2025.106273","DOIUrl":"10.1016/j.autcon.2025.106273","url":null,"abstract":"<div><div>Aging bridges urgently need maintenance, as many exceed their lifespans. Traditional inspections are manual, time-consuming, costly, and error-prone. This has prompted a shift toward integrating advanced technologies to automate inspection processes and provide more efficient and accurate maintenance solutions. This paper introduces a multi-stage automated inspection system for bridge maintenance designed to classify bridge components and accurately assess the type and extent of deterioration. Unmanned aerial vehicles (UAVs) capture high-resolution images of bridge components, enabling comprehensive visual data collection without requiring manual access to challenging or hazardous areas. The inspection process employs the Vision Transformer (ViT) model for precise image classification, while You Only Look Once (YOLO) is used for instance segmentation. To further enhance the system's effectiveness, the Pilgrimage Walk Optimization (PWO)-Lite algorithm is applied to optimize the detection of deteriorated areas and estimate repair costs. This integration improves structural assessments, extends bridge longevity, and benefits bridge management agencies.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106273"},"PeriodicalIF":9.6,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125010","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