Automation in Construction最新文献_第3页

Design optimization and assessment of stay-in-place 3D printed concrete formwork for slabs 平板3D打印混凝土模板设计优化与评估

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-11 DOI: 10.1016/j.autcon.2025.106572

Mahsa Sakha , Saim Raza , Xiaomeng Wang , Haifeng Fan , Niels Pichler , Moslem Shahverdi

{"title":"Design optimization and assessment of stay-in-place 3D printed concrete formwork for slabs","authors":"Mahsa Sakha , Saim Raza , Xiaomeng Wang , Haifeng Fan , Niels Pichler , Moslem Shahverdi","doi":"10.1016/j.autcon.2025.106572","DOIUrl":"10.1016/j.autcon.2025.106572","url":null,"abstract":"<div><div>Extrusion-based 3D printing has recently expanded into fabricating concrete structures with complex geometries. Despite its potential to reshape concrete construction, its application in load-bearing elements has been delayed by concerns over weak tensile capacity and challenges in integrating conventional reinforcement. Previous research has shown that post-tensioning provides a reliable method for designing stay-in-place 3D-printed concrete formwork capable of supporting both its self-weight and freshly cast concrete toppings without requiring temporary shoring. However, that work neglected the geometric freedom enabled by 3D printing, as it lacked a comprehensive optimization analysis. This paper introduces a bottom-up optimization workflow consisting of two steps: (I) cross-sectional shape optimization and (II) spanwise optimization. This approach evaluates shapes based on structural performance and demand, then arranges them along the span for higher performance. Given this workflow, two printing strategies, open- and closed-loop, are introduced and compared, and the superior performance of the closed-loop approach is demonstrated.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"181 ","pages":"Article 106572"},"PeriodicalIF":11.5,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261672","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 spalling characterization with a parallel laser line-camera and U-Net++ with Kolmogorov-Arnold networks (UPPKAN) 利用平行激光线摄像机和带有Kolmogorov-Arnold网络（UPPKAN）的U-Net++自动剥落表征

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-11 DOI: 10.1016/j.autcon.2025.106596

Chaobin Li, Haowei Zhang, Ray Kai Leung Su

{"title":"Automated spalling characterization with a parallel laser line-camera and U-Net++ with Kolmogorov-Arnold networks (UPPKAN)","authors":"Chaobin Li, Haowei Zhang, Ray Kai Leung Su","doi":"10.1016/j.autcon.2025.106596","DOIUrl":"10.1016/j.autcon.2025.106596","url":null,"abstract":"<div><div>The spalling of reinforced concrete (RC) structures threatens structural safety, and requires accurate quantification for condition assessment and repair planning. While point cloud-based methods have gained traction, they often lack efficiency and robustness in field applications. This study presents a compact, handheld parallel laser line-camera (PLLC) system capable of rapidly and automatically localizing and quantifying concrete spalling in terms of both surface area and depth using a single image. A segmentation model, known as UPPKAN, integrates U-Net++ with Kolmogorov–Arnold Networks (KANs), to extract fine-grained spalling boundaries. Projected laser lines are further exploited to determine relative spalling location and reconstruct depth profiles via line distortion analysis, ensuring robust measurements even under non-perpendicular photography. Laboratory and field experiments validate the system's accuracy, stability, and adaptability under diverse lighting and surface conditions. The proposed system offers a low-cost, efficient solution for automating routine inspection, especially for difficult-to-access locations such as ceilings.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"181 ","pages":"Article 106596"},"PeriodicalIF":11.5,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261670","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

Indoor scan-to-BIM workflows: Progress, challenges, and future directions (2014–2024) 室内扫描到bim工作流程：进展、挑战和未来方向（2014-2024）

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-11 DOI: 10.1016/j.autcon.2025.106578

Yahan Chen , Xiaowei Luo

引用次数: 0

Automated multi-label classification of risk clauses in construction contracts using GPT-driven data augmentation 使用gpt驱动的数据增强技术实现建筑合同中风险条款的自动多标签分类

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-11 DOI: 10.1016/j.autcon.2025.106599

Lanqian Zhang , Yan Ning , Shenghua Zhou

引用次数: 0

From prediction to prioritization: Automated framework for delay risk management in highway earthwork projects 从预测到优先排序：公路土方工程项目延迟风险管理的自动化框架

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-10 DOI: 10.1016/j.autcon.2025.106588

Mingeon Cho , Minsoo Park , Jongho Park , Sun-Kyu Park , Gichun Cha , Seunghee Park

{"title":"From prediction to prioritization: Automated framework for delay risk management in highway earthwork projects","authors":"Mingeon Cho , Minsoo Park , Jongho Park , Sun-Kyu Park , Gichun Cha , Seunghee Park","doi":"10.1016/j.autcon.2025.106588","DOIUrl":"10.1016/j.autcon.2025.106588","url":null,"abstract":"<div><div>Construction delays remain a critical challenge, often leading to cost overruns, contractual disputes, and project inefficiencies. This paper presents a proactive, data-driven framework designed to predict and manage delays specifically in highway earthwork projects, one of the most delay-sensitive and uncertainty-prone phases in infrastructure construction. Leveraging both structured and unstructured data, the proposed method combines pretrained language models (PLMs) and machine learning (ML) algorithms to accurately forecast delay types and durations. An automated probability-impact matrix (PIM) is incorporated to visualize and prioritize risks, enabling more strategic resource allocation. To ensure interpretability, the framework integrates SHapley Additive exPlanations (SHAP), offering stakeholders transparent insights into model predictions. Case studies demonstrate the framework’s effectiveness in reducing manual workload and enhancing early decision-making in real-world construction settings. By embedding predictive intelligence into the delay management process, this approach aligns with the goals of Construction 4.0 and offers a scalable solution adaptable to broader industries. The proposed framework improves predictive accuracy and provides actionable insights that support more efficient and informed risk management.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"181 ","pages":"Article 106588"},"PeriodicalIF":11.5,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261833","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

Regional graph-based pavement condition data quality assessment in support of trustworthy highway infrastructure digital twins 基于区域图的道路状况数据质量评估，支持可信赖的公路基础设施数字孪生

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-10 DOI: 10.1016/j.autcon.2025.106603

Linjun Lu, Yuandong Pan, Brian Sheil, Peihang Luo, Ioannis Brilakis

{"title":"Regional graph-based pavement condition data quality assessment in support of trustworthy highway infrastructure digital twins","authors":"Linjun Lu, Yuandong Pan, Brian Sheil, Peihang Luo, Ioannis Brilakis","doi":"10.1016/j.autcon.2025.106603","DOIUrl":"10.1016/j.autcon.2025.106603","url":null,"abstract":"<div><div>Pavement condition data plays a critical role in highway digital twins (DTs) for intelligent infrastructure management. However, automated condition surveys often contain abnormal measurements that, if left undetected, can distort deterioration modeling and compromise maintenance decision-making. Existing section-level abnormal data identification methods remain limited in accuracy, as they typically rely on statistical thresholds or temporal heuristics and overlook the spatial-temporal dependencies inherent in pavement data. To address this hurdle, this paper introduces a regional graph-based method for section-level data quality assessment. Specifically, it first groups neighboring road sections into homogeneous clusters based on their historical condition patterns. Subsequently, a Graph-Mamba Attention Network (GMAN) is utilized to capture spatial-temporal dependencies and identify abnormal data points within each cluster. A case study on the UK highway network showed that the proposed method significantly outperforms the existing methods, thus signifying its potential to enhance the trustworthiness of pavement data in highway DTs.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"181 ","pages":"Article 106603"},"PeriodicalIF":11.5,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261673","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 rebar arrangement in prefabricated concrete components using multi-agent transfer reinforcement learning 基于多智能体转移钢筋学习的预制混凝土构件钢筋自动布置

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-10 DOI: 10.1016/j.autcon.2025.106590

Zijing Wan , Chengran Xu , Xuefeng Chen , Hongtuo Qi , Jiepeng Liu , Liang Feng

{"title":"Automated rebar arrangement in prefabricated concrete components using multi-agent transfer reinforcement learning","authors":"Zijing Wan , Chengran Xu , Xuefeng Chen , Hongtuo Qi , Jiepeng Liu , Liang Feng","doi":"10.1016/j.autcon.2025.106590","DOIUrl":"10.1016/j.autcon.2025.106590","url":null,"abstract":"<div><div>Prefabricated concrete components (PCCs) require accurate and efficient rebar arrangements to avoid clashes and ensure structural reliability. Existing methods model each rebar sequentially as an agent, using obstacle-avoidance and constraint-based actions to automate layout in PCCs with complex geometries and multiple embedded parts. However, the repeat planning of similar tasks is inefficient, and sequence-dependent interactions among rebars are frequently overlooked. Therefore, an automated rebar arrangement framework for PCCs is developed, and a multi-agent transfer reinforcement learning (MATRL) method with case-based reasoning is proposed to promote knowledge sharing among agents and accelerate convergence. The feasibility and effectiveness of the framework are demonstrated through three real-world cases: PC beam–column joints, PC stairs, and PC frames. The results show that the proposed MATRL method improves the training efficiency, convergence, and solution quality compared with baseline approaches. These findings provide a foundation for extending reinforcement learning strategies to more complex structural optimization tasks.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"181 ","pages":"Article 106590"},"PeriodicalIF":11.5,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261713","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

Framework for automated building code compliance checking to improve transparency, trust, validation, and design interpretation 用于自动建筑规范遵从性检查的框架，以提高透明度、信任、验证和设计解释

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-10 DOI: 10.1016/j.autcon.2025.106598

Nishanth Purushotham , Chethan Kailashnath , Ivan Mutis

引用次数: 0

Bidirectional spatio-temporal networks for predicting cost performance in construction using deep learning extension of earned value management 利用挣值管理的深度学习扩展预测建筑成本绩效的双向时空网络

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-10 DOI: 10.1016/j.autcon.2025.106597

Fatemeh Mostofi , Onur Behzat Tokdemir , Vedat Toğan

{"title":"Bidirectional spatio-temporal networks for predicting cost performance in construction using deep learning extension of earned value management","authors":"Fatemeh Mostofi , Onur Behzat Tokdemir , Vedat Toğan","doi":"10.1016/j.autcon.2025.106597","DOIUrl":"10.1016/j.autcon.2025.106597","url":null,"abstract":"<div><div>Network-based construction planning methods are often not incorporated into machine learning (ML) models, whereas existing temporal models struggle to effectively capture the short- and high-frequency nature of construction activities, as well as the spatial dependencies inherent in construction tasks. This paper presents a dynamic spatio-temporal ML architecture —bidirectional recurrent neural networks with graph convolutional networks (Bi-RNN-GCN)—that forecasts the cost performance index (CPI) within earned value management (EVM) control cycles. The system encodes spatial connectivity via work breakdown structure (WBS) logic and temporal sequencing through delivery scheduling. By structuring the construction progress data into a network format, where nodes represent individual activities and edges reflect both spatial and temporal dependencies, thereby better representing the functional realities of construction dynamics. In this way, both the temporal evolution and the spatial relationships among tasks are learned. The model was trained on a large dataset of over 200,000 progress records collected over 53 weeks, and different configurations were evaluated, including Bi-RNN-GCN, RNN-GCN, and GCN-RNN, against static benchmark models. The findings clearly indicated that the Bi-RNN-GCN model, which prioritizes bidirectional temporal learning followed by spatial learning, achieved 15 % higher accuracy than static models such as GCN.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"181 ","pages":"Article 106597"},"PeriodicalIF":11.5,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261674","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 reliability-based multi-camera strategy for excavator tracking under dynamic occlusion using deep learning with instance segmentation 基于实例分割的深度学习多摄像机动态闭塞跟踪策略

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-10-09 DOI: 10.1016/j.autcon.2025.106589

Seongkyun Ahn, Seungwon Seo, Yejin Shin, Choongwan Koo

{"title":"Automated reliability-based multi-camera strategy for excavator tracking under dynamic occlusion using deep learning with instance segmentation","authors":"Seongkyun Ahn, Seungwon Seo, Yejin Shin, Choongwan Koo","doi":"10.1016/j.autcon.2025.106589","DOIUrl":"10.1016/j.autcon.2025.106589","url":null,"abstract":"<div><div>Occlusion in vision-based excavator tracking is a major issue in dynamic construction environments where frequent obstructions significantly degrade object tracking performance. To overcome this, this paper proposes an automated reliability-based multi-camera strategy for robust excavator tracking under simultaneous occlusion in dynamic construction environments by integrating deep learning-based segmentation. The paper was conducted in three phases: (i) performance validation using authentic and synthetic videos; (ii) reliability modeling with occlusion ratio and viewpoint analysis; and (iii) strategy validation in real-world scenarios. The key findings are as follows. The reliability of support vector classifier reached a weighted F1-score of 0.904 in classifying reliable tracking zones. The mask-based method achieved a multi-object tracking accuracy of 84.41 % under real-world scenarios for empirical validation. These results demonstrate the effectiveness of the proposed approach in mitigating occlusion-induced degradation, laying the foundation for automation in carbon emission and productivity analysis on construction sites.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"181 ","pages":"Article 106589"},"PeriodicalIF":11.5,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261677","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