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}
{"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}
{"title":"Indoor scan-to-BIM workflows: Progress, challenges, and future directions (2014–2024)","authors":"Yahan Chen , Xiaowei Luo","doi":"10.1016/j.autcon.2025.106578","DOIUrl":"10.1016/j.autcon.2025.106578","url":null,"abstract":"<div><div>With the increasing adoption of BIM in building operation and maintenance, generating as-built Building Information Models (BIMs) for existing buildings becomes a growing demand. Converting laser-scanned point clouds into as-built BIMs (i.e., “Scan-to-BIM”) in the indoor scenes holds significant potential for the industry demand. This paper reviews the current state of indoor Scan-to-BIM research through a survey of 109 publications from the past decade. In the bibliometric analysis, the research status and relevant hot topics are identified. In the technical analysis, the implementation path, related methods for key steps, and modeling results of indoor Scan-to-BIM are statistically analyzed. Based on these findings, recommendations concerning development foundations, directions, and contents are proposed to foster the advancement of indoor Scan-to-BIM workflows.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"181 ","pages":"Article 106578"},"PeriodicalIF":11.5,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261671","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}
{"title":"Automated multi-label classification of risk clauses in construction contracts using GPT-driven data augmentation","authors":"Lanqian Zhang , Yan Ning , Shenghua Zhou","doi":"10.1016/j.autcon.2025.106599","DOIUrl":"10.1016/j.autcon.2025.106599","url":null,"abstract":"<div><div>Contract review is an important method to prevent risks. Prior research mainly utilizes single-label classification for risk clauses in contracts, with the data imbalance problem neglected. This research proposes an automated model for classifying risk clauses in contracts using Generative Pre-trained Transformer (GPT). To address data imbalance, a dual-stage approach is applied, where GPT-4o and GPT-2 are used for data generation and quality evaluation, respectively. The method is validated through 3411 sentences from international Engineering, Procurement, and Construction (EPC) contracts and 2807 generated sentences. Experimental results show that the GPT-based model outperforms the four baseline models. Data augmentation improves the model performance, as shown by a reduction in Hamming loss across all models, with the GPT model improving by 10.16%. This research contributes to advancing the application of GPT in contract risk analysis and provides an improved approach for addressing data imbalance in multi-label classification tasks.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"181 ","pages":"Article 106599"},"PeriodicalIF":11.5,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261669","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}
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}
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}
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}
Nishanth Purushotham , Chethan Kailashnath , Ivan Mutis
{"title":"Framework for automated building code compliance checking to improve transparency, trust, validation, and design interpretation","authors":"Nishanth Purushotham , Chethan Kailashnath , Ivan Mutis","doi":"10.1016/j.autcon.2025.106598","DOIUrl":"10.1016/j.autcon.2025.106598","url":null,"abstract":"<div><div>Building codes are complex and written in natural language, making interpretation costly and prone to error. This paper addresses the specific question of how Natural Language Processing (NLP) can automate code compliance checking across entire building codes rather than isolated sections. The proposed framework uses a two-stage method that first structures code documents into semantic hierarchies and then applies ontological reasoning to generate and verify rule sets against BIM models. Results show that the approach captures exceptions and cross-references more effectively than current rule-based or commercial systems. This is important for architects and engineers who must ensure accuracy, transparency, and trust in compliance decisions while reducing manual effort. The framework also establishes a foundation for scalable, fully automated compliance systems that can guide future research on integrating artificial intelligence with building regulations.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"181 ","pages":"Article 106598"},"PeriodicalIF":11.5,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261675","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}
{"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}
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}