Hang Zhao , Vahidreza Gharehbaghi , Caroline Bennett , Rémy D. Lequesne , Jian Li
{"title":"Pixel-level image localization for updating 3D digital twins of dams using frequency convolutional networks","authors":"Hang Zhao , Vahidreza Gharehbaghi , Caroline Bennett , Rémy D. Lequesne , Jian Li","doi":"10.1016/j.autcon.2025.106715","DOIUrl":"10.1016/j.autcon.2025.106715","url":null,"abstract":"<div><div>This paper presents PIL3D, an automated pixel-level image localization framework for maintaining up-to-date 3D digital twins of large-scale civil infrastructure, with a focus on dam structures. Unlike conventional 3D model updating approaches that require extensive manual data acquisition and labor-intensive processing, PIL3D automatically predicts the 3D coordinates of every pixel in an input image relative to an existing model, enabling fully automated dense pixel-to-point correspondences. Experimental validation on a real-world dam case demonstrates centimeter-level localization accuracy, significantly reducing manual intervention, data collection requirements, and computational demand. By integrating PIL3D into digital twin workflows, infrastructure inspection, monitoring, and maintenance can be streamlined into a continuous, automated process, advancing the state of automation in construction and asset management.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106715"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759776","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}
Chenlong Feng , Quan Zhang , Jixin Wang , Xinxing Liu , Yuying Shen , Qingzheng Jia , Jiazhi Zhao
{"title":"Excavator trajectory planning via global probabilistic learning from expert demonstrations","authors":"Chenlong Feng , Quan Zhang , Jixin Wang , Xinxing Liu , Yuying Shen , Qingzheng Jia , Jiazhi Zhao","doi":"10.1016/j.autcon.2025.106736","DOIUrl":"10.1016/j.autcon.2025.106736","url":null,"abstract":"<div><div>Excavator trajectory planning remains challenging due to dependence on expert skill, changing tasks, and complex environments. This paper integrates global probabilistic modeling of expert demonstrations with sampling-based optimization to enable flexible, efficient, and safe autonomous operation. A Global Modulated Movement Primitive (GMMP) model captures global evolution of expert demonstration trajectories in SE(3) space, the 3D rigid-body pose space that combines orientation and translation. A Bayesian update supports efficient task generalization by adjusting new via points. The workspace density of excavator is introduced to enable the transfer of GMMP across different excavator without retraining. A Guided Model Predictive Path Integral (GMPPI) method with SE(3)-consistency cost optimizes GMMP generated trajectories via sampling, handling obstacle avoidance and execution constraints. The method was validated on a full-size excavator and a scaled platform. Results show improved trajectory similarity, execution efficiency, and task adaptability, indicating strong practicality.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106736"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785094","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":"Frequency-aware crack segmentation network (FACS-net) and crack topology loss (CT-loss) for thin cracks","authors":"Siheon Joo, Seokhwan Kim, Hongjo Kim","doi":"10.1016/j.autcon.2025.106719","DOIUrl":"10.1016/j.autcon.2025.106719","url":null,"abstract":"<div><div>Structural crack analysis is vital for infrastructure safety, but existing segmentation models often miss fine cracks due to spectral bias in deep networks. This especially affects thin cracks, which are frequently underrepresented. This paper presents FACS-Net, a Frequency-Aware Crack Segmentation Network with Crack Topology Loss (CT-Loss), to mitigate spectral bias and enhance crack-specific representations. FACS-Net employs frequency-aware attention for decoding, while CT-Loss explicitly incorporates boundary accuracy and structural continuity into the learning objective. Given the high edge-to-area ratio of thin cracks, the proposed approach ensures accurate localization without sacrificing topological coherence. Evaluation on CrackVision12K shows that FACS-Net significantly improves detection of thin cracks (width<span><math><mo>≤</mo></math></span> 2 px), outperforming Hybrid-Segmentor by 0.306 IoU and 0.360 CTS. Overall, FACS-Net achieves state-of-the-art performance with 0.663 IoU and 0.651 CTS, demonstrating precise segmentation and robust structural preservation.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106719"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145787865","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}
Yongqing Jiang , Jianze Wang , Xinyi Shen , Kaoshan Dai , Qingzi Ge
{"title":"Multitask unified large vision-language model for post-earthquake structural damage assessment of buildings","authors":"Yongqing Jiang , Jianze Wang , Xinyi Shen , Kaoshan Dai , Qingzi Ge","doi":"10.1016/j.autcon.2025.106720","DOIUrl":"10.1016/j.autcon.2025.106720","url":null,"abstract":"<div><div>Rapid and accurate damage assessment of structures is critical for post-earthquake recovery and emergency response. Current evaluations are heavily reliant on on-site visual inspections conducted by engineering experts, which are time-consuming and resource-intensive. To this end, the large vision-language model (VLM) for multitask structural damage assessment chatbot (MT-SDAChat) is developed in this paper. It can perform both image-level and regional-level inference analysis, accurately locating and providing specific information about various structural components and damage locations. With the MT-SDAChat, a two-stage automated assessment framework that transitions from a global perspective to a component-specific perspective is proposed. A dataset containing 3348 image-text pairs of seismic structural damage with multiple attributes has been constructed. Experimental results show that MT-SDAChat performs well in multitask evaluation. It achieves a question-and-answer accuracy of 82.92 % and a localization accuracy of 78.6 %. These results highlight its strong zero-shot capability across various damage assessments in building construction.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106720"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813790","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}
Tao Wu , Shitong Hou , Zhishen Wu , Xiaoyuan He , Gang Wu
{"title":"Underwater defect measurement for bridge piers via non-planar refraction correction","authors":"Tao Wu , Shitong Hou , Zhishen Wu , Xiaoyuan He , Gang Wu","doi":"10.1016/j.autcon.2025.106753","DOIUrl":"10.1016/j.autcon.2025.106753","url":null,"abstract":"<div><div>Quantitative measurement of apparent defects using underwater vision-based techniques is essential for structural inspection of submerged bridge components. However, measurement accuracy is greatly limited by nonlinear imaging distortions caused by multi-medium refraction and viewport deformation under hydrostatic pressure. To overcome these challenges, this paper introduces a multi-refraction correction model that accounts for refractive interface deformation. A nonlinear underwater imaging framework is established by integrating a spatial coordinate transformation-based calibration method with deformation analysis of the viewport. The feasibility and accuracy of the proposed approach are validated through underwater checkerboard corner-detection experiments. Compared with traditional multi-plane refraction correction method, the proposed model enhances measurement precision by more than 40 %. Additional experiments on submerged bridge pier components show that the measurement errors for apparent defect dimensions consistently remain below 5 %, highlighting the strong potential of the method for practical implementation in underwater visual inspection of bridge infrastructure.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106753"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903162","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}
Pinsheng Duan , Xuehai Fu , Jinxin Hu , Jianliang Zhou , Ping Guo
{"title":"Path planning for UAV-based construction safety inspection under spatiotemporal interference from tower cranes","authors":"Pinsheng Duan , Xuehai Fu , Jinxin Hu , Jianliang Zhou , Ping Guo","doi":"10.1016/j.autcon.2026.106762","DOIUrl":"10.1016/j.autcon.2026.106762","url":null,"abstract":"<div><div>Construction sites are dynamic, complex, high-risk environments, where Unmanned Aerial Vehicles (UAVs) are vital for enhancing safety inspection efficiency. As large-scale dynamic obstacles, tower cranes can interfere with effective UAV inspection paths. This paper proposes a safety inspection path planning method under the spatiotemporal interference of multiple tower cranes. First, a 3D model of the construction site is reconstructed, and inspection viewpoints for UAV flights are generated by optimizing safety inspection strategies. Then, a hierarchical path planning framework is established: the lower-level planner strictly enforces real-time safety obstacle avoidance strategies, while the higher-level planner focuses on global planning to meet inspection requirements. Finally, both simulation and real project studies are conducted to verify the feasibility of the method. Results from the real project show that the effective coverage area is increased by 39.01 % compared with traditional methods. This paper provides theoretical and practical support for UAV-assisted safety inspections in construction.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106762"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903172","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":"Low-cost pavement roughness evaluation using Inertial Measurement Unit (IMU)-Pulsed Coherent Radar (PCR) sensor fusion","authors":"Yazan Ibrahim Alatoom , Omar Smadi","doi":"10.1016/j.autcon.2025.106713","DOIUrl":"10.1016/j.autcon.2025.106713","url":null,"abstract":"<div><div>Pavement roughness monitoring is critical for infrastructure management, but conventional automated profiling systems require substantial capital investments, making them inaccessible to limited-budget transportation agencies. This paper introduces a low-cost system combining Inertial Measurement Unit (IMU) and Pulsed Coherent Radar (PCR) technologies through sensor fusion. The approach captures vehicle dynamics via the IMU and pavement surface profiles via the PCR, using frequency-domain processing to isolate true pavement roughness from vehicle-induced motion. Comprehensive validation across twelve road segments and 216 test configurations demonstrates strong performance: MAPE below 9 % and R<sup>2</sup> exceeding 0.96 compared to reference measurements. A multi-stage optimization framework integrating Sequential Model-Based Optimization algorithm achieves 80–88 % accuracy improvements through systematic parameter calibration. The complete system costs $214 USD, providing a cost-effective solution for IRI estimation. A user-friendly graphical interface enables practical deployment by non-technical personnel. This approach enables broader adoption of automated pavement monitoring by agencies with limited budgets.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106713"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732318","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}
Meng Wang, Georgios N. Lilis, Dimitris Mavrokapnidis, Kyriakos Katsigarakis, Ivan Korolija, Dimitrios Rovas
{"title":"Automated and scalable BIM2BEM framework with zoning-based model simplification leveraging knowledge graph integration","authors":"Meng Wang, Georgios N. Lilis, Dimitris Mavrokapnidis, Kyriakos Katsigarakis, Ivan Korolija, Dimitrios Rovas","doi":"10.1016/j.autcon.2025.106712","DOIUrl":"10.1016/j.autcon.2025.106712","url":null,"abstract":"<div><div>Accurate and scalable generation of Building Energy Models (BEM) from Building Information Modelling (BIM) data is critical for performance-driven building design. However, existing methods are often constrained by data quality issues and rigid workflows, limiting automation. This paper proposes an automated and scalable BIM-to-BEM (BIM2BEM) framework enabled by knowledge graph integration, designed to support automation and scalability in model generation from imperfect BIM data. To manage model complexity, zoning-based mappings from BIM spaces to thermal zones are derived through multi-factor analysis of spatial relationships, functional usage, thermal load similarity, and HVAC configuration. Applied to a real-world complex building, the framework reduces simulation time by up to 70%, while maintaining energy use deviations within 3% and HVAC sizing variations up to 10%, compared with the full-model baseline. These findings indicate that the proposed framework can enhance BIM2BEM automation, supporting the scalable and flexible generation of simulation-ready models under practical data limitations.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106712"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732333","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}
Neng Wang , Bowen Jiang , Camillo J. Taylor , Zili Li
{"title":"Diffusion-enhanced semantic segmentation for underground crack detection","authors":"Neng Wang , Bowen Jiang , Camillo J. Taylor , Zili Li","doi":"10.1016/j.autcon.2025.106721","DOIUrl":"10.1016/j.autcon.2025.106721","url":null,"abstract":"<div><div>Unlike surface structures, crack detection in underground environments faces greater challenges due to low illumination and complex structural context. This paper develops a diffusion-based model for crack segmentation in underground tunnel structures. Building on a framework that reformulates semantic segmentation as conditional image generation, an enhanced diffusion architecture guided by prior masks is proposed. Trained on a diverse dataset comprising crack images from the Dublin Port Tunnel, the European Organization for Nuclear Research, and the Bochum Crack Dataset, the proposed model outperforms benchmark models (e.g., Mask2Former), achieving a 9.1% increase in mIoU and 16.1% in F1-score in multi-scenario validation. In field testing, the proposed two-stage pipeline optimizes inference strategy by excluding crack-free patches (87.5% of the total), and tests real-world applicability, with an absolute mIoU gain of 0.063. Additionally, a post-processing strategy leveraging uncertainty maps further refines the segmentation results.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106721"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732334","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":"Digital twin synchronization in closed-loop HVAC control for building operations","authors":"Jabeom Koo , Sungmin Yoon","doi":"10.1016/j.autcon.2025.106723","DOIUrl":"10.1016/j.autcon.2025.106723","url":null,"abstract":"<div><div>Synchronization is essential for maintaining reliable and consistent digital twin environments in building operations. However, achieving accurate synchronization remains challenging due to limitations in virtual model representation and operational uncertainties. This paper proposes a digital twin synchronization (DTS) method based on a bidirectional synchronization framework that enhances digital twins' ability to simulate physical behaviors and mitigate uncertainties during operation. A field implementation was conducted in actual building operations, applying the DTS method to a chilled water pump differential pressure control loop. The results show that the bidirectional approach outperforms conventional unidirectional synchronization by reducing sensor errors and model uncertainties and improving overall simulator reliability. The proposed DTS method enables more consistent and reliable digital twin operation by improving synchronization accuracy and reducing inconsistencies. The DTS approach reduced the digital twin simulator's overall MAPE from 5.32 % to 0.88 %, demonstrating its effectiveness in accurately replicating physical and control behaviors.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106723"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823244","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}