Xingjie Xie, Sijia Gu, Xiaofan Gao, Yilei Xu, Philip F. Yuan
{"title":"Vision-guided autonomous drone construction system for standardized bricklaying","authors":"Xingjie Xie, Sijia Gu, Xiaofan Gao, Yilei Xu, Philip F. Yuan","doi":"10.1016/j.autcon.2025.106525","DOIUrl":"10.1016/j.autcon.2025.106525","url":null,"abstract":"<div><div>As the construction industry advances toward intelligent automation, traditional ground-based robots face limitations in flexibility when dealing with tall structures and complex terrains. To address these challenges, this paper introduces BrickPilot, an aerial autonomous bricklaying system that combines the high mobility of drones with the precision of computer vision to automatically identify, grasp, and accurately place standard bricks. The system features custom-developed hardware, including a flight platform, compensation mechanism, and gripper, and uses a depth camera and pre-trained vision model to recognize brick positions and orientations in real time. Demonstrated at the 2024 China Architectural Design Expo, BrickPilot successfully completed the construction of a standard brick wall. Results show that the system achieves high precision, stability, and robustness, with promising potential for large-scale construction projects and extraterrestrial habitat assembly.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"180 ","pages":"Article 106525"},"PeriodicalIF":11.5,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093967","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":"Integrated weatherability optimization design tools for ice-shell architecture based on explainable surrogate models","authors":"Shuoyong Yang , Peng Luo , Xiaoping Liu","doi":"10.1016/j.autcon.2025.106539","DOIUrl":"10.1016/j.autcon.2025.106539","url":null,"abstract":"<div><div>The weatherability of ice-shell architecture directly affects both structural safety and industrial value. However, cost-effective solutions to improve weatherability remain limited. This paper proposes an efficient early-stage design optimization using surrogate models integrated with design tools. The degree of elastic energy degradation is introduced as a quantitative evaluation index. Generalizable spatial and shape features are extracted, and surrogate models are refined through Shapley Additive Explanations (SHAP) interpretation and validation with two engineering cases. The method reduces the data acquisition complexity and reliance on experience in the design process, thereby improving automation in the design workflow. Its universality makes it broadly applicable to airbag mold ice-shell buildings in northeastern China. Applied to a representative combined shuttle-shaped ice-shell architecture, the method reveals climatic coupling relationships and dominant design parameters, including long-axis length, support length, and orientation. Results indicate that the weatherability index can be reduced to 30–40 % through optimization.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"180 ","pages":"Article 106539"},"PeriodicalIF":11.5,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093966","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}
Xianlong Wu , Xiaohua Bao , Jun Shen , Xiangsheng Chen
{"title":"Graph neural network–based framework for predicting seismic damage in shield tunnels with contact loss defects","authors":"Xianlong Wu , Xiaohua Bao , Jun Shen , Xiangsheng Chen","doi":"10.1016/j.autcon.2025.106535","DOIUrl":"10.1016/j.autcon.2025.106535","url":null,"abstract":"<div><div>Contact loss defects (CLDs) at the tunnel–soil interface can significantly affect the seismic response of shield tunnels, while conventional finite element methods (FEM) are too time-consuming for rapid decision-making. This paper proposes a framework for seismic damage prediction of shield tunnels with CLD that directly maps field-detected CLD and stratum parameters to tunnel damage distributions. The framework integrates a multilayer perceptron (MLP) and a graph neural network (GNN) to encode finite element results into graph data and learn spatial damage patterns. It is trained and validated on a large dataset of simulated seismic responses covering diverse CLD scenarios and stratum conditions, and tested on real detection cases. The model achieves an R<sup>2</sup> of 0.98, RMSE of 4.2, and MAPE of 0.08, while reducing computation time by 90-fold compared with FEM. These results demonstrate the framework's effectiveness, efficiency, and scalability for rapid post-earthquake assessment of shield tunnels.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"180 ","pages":"Article 106535"},"PeriodicalIF":11.5,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094008","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":"LiDAR-based temporal surface damage assessment of bridge infrastructure using efficient scan area planning","authors":"Vignesh Vijayalakshmi Palanisamy, Senthilkumar Venkatachalam","doi":"10.1016/j.autcon.2025.106526","DOIUrl":"10.1016/j.autcon.2025.106526","url":null,"abstract":"<div><div>Aging bridges pose significant risks to public safety and economic stability, and traditional inspection methods lack accuracy and historical data. LiDAR-based damage assessment has gained prominence in recent years because of its precision and automation capabilities. However, optimal scanner placement for efficient and comprehensive, accurate data collection remains a challenge. This paper demonstrates and validates a scan area planning method that minimizes differences between planned and measured interpoint spacings, achieving sub-millimeter accuracy. Using this approach, point clouds of bridge pier infrastructure were collected over two years to track cracks, spalling, and reinforcement corrosion. The results revealed a 47.2% reduction in the diameter of the exposed reinforcement and a 37.1% increase in the vertical crack width, indicating significant structural deterioration. The proposed method provides valuable insights into the condition of the bridge components and their surrounding environment, supporting proactive maintenance planning and more efficient resource allocation.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"180 ","pages":"Article 106526"},"PeriodicalIF":11.5,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094178","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}
Sizeng Zhao , Fei Kang , Junjie Li , Jin Gong , Maosong Yang , Liangchong Xie
{"title":"Precision UAV image-to-BIM registration through contour-based matching for concrete dam structural health monitoring","authors":"Sizeng Zhao , Fei Kang , Junjie Li , Jin Gong , Maosong Yang , Liangchong Xie","doi":"10.1016/j.autcon.2025.106536","DOIUrl":"10.1016/j.autcon.2025.106536","url":null,"abstract":"<div><div>The accurate mapping of UAV images to BIM is critical for long-term structural health monitoring. However, the complexity of concrete dams introduce positioning deviations, making it challenging to precisely localize defects. This paper proposes a precise image-to-BIM method based on contour matching. After point cloud registration establishes global coordinate transformation, the structural contour templates are extracted for UAV viewpoints. The concrete dam contours are classified as outer or inner, and the UAV images are matched with templates using different algorithms. 2D pixel variations are then converted into 3D spatial displacements, and the UAV coordinates are iteratively refined for accurate contour alignment. The corrected coordinates map the detected defects onto the BIM surface to ensure precise image-to-BIM registration. The proposed method is validated on a real concrete dam. After coordinate correction, cracks detected in the UAV images are accurately mapped to the BIM surface, with consistent localization across different viewing angles.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"180 ","pages":"Article 106536"},"PeriodicalIF":11.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060860","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":"Multistage cognitive dynamics for team-based teleoperation in challenging environments","authors":"Di Liu , Youngjib Ham","doi":"10.1016/j.autcon.2025.106537","DOIUrl":"10.1016/j.autcon.2025.106537","url":null,"abstract":"<div><div>Excavation in urban areas presents high risks, particularly underground utility strikes, which can lead to significant disruptions and safety hazards. This paper investigates the cognitive dynamics of teleoperated excavation, focusing on how operator-spotter communication and environmental complexity impact cognitive load, attention allocation, and task performance in high-stress settings. Excavation scenarios are simulated in baseline and challenging urban environments, involving 56 subjects examined key metrics, including attention resources, cognitive workload, situational awareness, and unsafe behaviors. Findings show high perceptual and cognitive-control loads in a complex environment impair situational awareness, extend task completion times, and increase communication error rates. Results also highlight hand signals reduce cognitive load, enhancing focus on peripheral cues, whereas verbal signals help operators concentrate on task-critical elements, lowering collision risks. This paper advances the understanding of human-machine collaboration by building on a multistage cognitive framework that provides insights into team communication and safety for teleoperation in challenging environments.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"180 ","pages":"Article 106537"},"PeriodicalIF":11.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060859","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}
Zongshuai Wan , Haotian Man , Kristof Crolla , Eike Schling
{"title":"Design and construction of an asymptotic kinetic transformable canopy","authors":"Zongshuai Wan , Haotian Man , Kristof Crolla , Eike Schling","doi":"10.1016/j.autcon.2025.106508","DOIUrl":"10.1016/j.autcon.2025.106508","url":null,"abstract":"<div><div>Transformable structures offer significant potential for adaptive and deployable architecture but face challenges of complex fabrication and actuation. This paper addresses the design and construction of kinetic grid structures using asymptotic curve networks, enabling the creation of doubly curved gridshells from straight, elastic Glass Fiber Reinforced Polymer (GFRP) planks. Connected by standardised scissor joints, the planks undergo controlled transformation through elastic deformation. The design process integrates differential geometry and discrete mesh optimization to define and simulate the structure's behaviour. A full-scale prototype—the <em>Kinetic Canopy</em>—was fabricated and transformed from a flat configuration into an arched gridshell using an integrated cable actuation system, without support movements. The transformation was analysed using a non-linear finite element model and validated against physical experiments. The paper provides a detailed evaluation of initial stresses, actuation forces, and energy shifts, establishing a comprehensive framework for the design and analysis of asymptotic kinetic structures.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"180 ","pages":"Article 106508"},"PeriodicalIF":11.5,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046637","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 design of self-centering shear walls using machine learning and genetic algorithms","authors":"Qimian Dong , Longhe Xu , Xingsi Xie , Yan Zhang","doi":"10.1016/j.autcon.2025.106532","DOIUrl":"10.1016/j.autcon.2025.106532","url":null,"abstract":"<div><div>Self-centering shear walls (SCSWs) have demonstrated superior resilience compared to conventional shear walls in both numerical simulations and physical experiments. However, analytical design methods for SCSWs and other self-centering structural systems remain underdeveloped. This paper develops and validates a finite element model of SCSWs. Machine learning techniques are employed to evaluate seismic performance. The resulting models achieve high accuracy in predicting stiffness, peak shear capacity, and residual drift of SCSWs. Building on these predictors, an automated design tool is introduced to generate SCSW designs that satisfy resilience requirements while minimizing construction costs.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"180 ","pages":"Article 106532"},"PeriodicalIF":11.5,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046561","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":"Multivariate fusion-based surrogate modeling for predicting excavation-induced full-field vertical soil displacement","authors":"Jian Wei, Yue Pan, Jin-Jian Chen","doi":"10.1016/j.autcon.2025.106511","DOIUrl":"10.1016/j.autcon.2025.106511","url":null,"abstract":"<div><div>In geotechnical engineering, foundation pit projects face growing challenges in predicting excavation-induced vertical soil displacement under complex and variable conditions. This paper presents PitGAN, a generalizable surrogate modeling approach designed to deliver accurate, full-field predictions of vertical soil displacement for field-scale risk assessment. PitGAN integrates (1) region-adaptive data generation using hydro-mechanical coupled simulations, (2) multivariate fusion preprocessing guided by 2D spatial knowledge, and (3) generative adversarial network-based learning with attention mechanisms. A region-specific PitGAN model developed for Shanghai, China achieved accuracy comparable to numerical simulations while identifying interpretable geotechnical patterns and providing precise displacement predictions in a real-world case with complex excavation conditions. Compared with existing approaches, PitGAN delivers full-field predictions with higher accuracy and improved spatial fidelity in capturing localized peak deformations, while achieving inference speeds over three orders of magnitude faster than numerical methods, thereby making it well suited for real-time, field-scale risk assessment and safety management.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"180 ","pages":"Article 106511"},"PeriodicalIF":11.5,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046636","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}
Naaga Vedula, Masih Beheshti, Shivkesh Madasu, Hasan Ozer
{"title":"Automated framework for evaluating asphalt pavement construction using UAV imagery","authors":"Naaga Vedula, Masih Beheshti, Shivkesh Madasu, Hasan Ozer","doi":"10.1016/j.autcon.2025.106498","DOIUrl":"10.1016/j.autcon.2025.106498","url":null,"abstract":"<div><div>Thermal uniformity and in-place density are key quality assurance factors affecting freshly placed asphalt pavement (referred to as mat) performance. Existing techniques like Paver Mounted Thermal Profilers and Intelligent Compaction Technologies quantify thermal non-uniformities and density differentials. In this paper, an alternative protocol was developed using unmanned aerial vehicle (UAV) assisted aerial infrared (IR) image data. A deep-learning object detection model was developed to identify the location of the paved mat and rollers in each thermal image using the YOLOv8 model. The developed framework was used for various sites visited in 2023 and 2024. Three quantification metrics for thermal segregation—Differential Range Statistic (DRS), Thermal Segregation Index (TSI), and Mat Temperature Differential Matrix (MDM)—are compared across all sites’ thermal images. A case study based on data from an experimental site is presented, and two lanes were monitored for roller movements. Compaction metrics such as the overall roller pass counts, roller speed, and insufficient roller passes based on the bottom quantile data were obtained from the developed protocol. The presented framework successfully identifies and maps the roller movements while calculating the thermal segregation and compaction metrics. When processed in the field during construction, the metrics can give near-real-time insights into the mat’s non-uniformities during paving. This developed protocol can provide actionable feedback to the contractors/agencies on the job site and help improve overall consistency in the quality of construction.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"180 ","pages":"Article 106498"},"PeriodicalIF":11.5,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046634","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}