Automation in Construction最新文献_第9页

Early-stage architecture design assistance by LLMs and knowledge graphs 法学硕士和知识图谱的早期架构设计协助

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2026-01-10 DOI: 10.1016/j.autcon.2025.106756

Danrui Li , Yichao Shi , Mathew Schwartz , Mubbasir Kapadia

引用次数: 0

AI-driven extraction of electrical circuits from floorplans for BIM ai驱动的从平面图中提取电路用于BIM

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2026-01-07 DOI: 10.1016/j.autcon.2025.106746

Martin Urbieta , Matias Urbieta , Guillermo Burriel

引用次数: 0

Collaborative learning architecture for autonomous excavator planning and execution 自主挖掘机规划与执行的协同学习体系结构

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2025-12-30 DOI: 10.1016/j.autcon.2025.106742

Junhyung Cho , Mingyu Shin , Joongheon Kim , Soyi Jung

{"title":"Collaborative learning architecture for autonomous excavator planning and execution","authors":"Junhyung Cho , Mingyu Shin , Joongheon Kim , Soyi Jung","doi":"10.1016/j.autcon.2025.106742","DOIUrl":"10.1016/j.autcon.2025.106742","url":null,"abstract":"<div><div>Autonomous excavation systems face fundamental challenges balancing computational tractability with operational sophistication. This paper presents the collaborative learning for excavation framework (CLEF), resolving this trade-off through strategic decomposition: separating high-level planning from low-level execution while maintaining collaborative optimization. The framework’s key contributions include a bidirectional information flow between specialized modules consisting of reinforcement learning for strategic planning using polar coordinates, and attention-enhanced generative adversarial imitation learning (A-GAIL) with multi-head attention capturing phase-specific temporal dependencies. Unlike monolithic approaches suffering computational intractability, CLEF enables module specialization while coordinating through shared representations. Planning decisions condition trajectory generation while execution outcomes update environmental models, creating adaptive behavior without manual tuning. Validation demonstrates 90.8% success rate compared to 71.1% for monolithic approaches, with trajectory generation achieving 91.3% completion confirming superior performance essential for construction automation.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106742"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880900","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

Collaborative inspection for large-scale urban sewer pipe networks by coupling multiple robotic pipe capsules and spatial optimization 基于多机器人管道胶囊耦合和空间优化的大型城市污水管网协同检测

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2026-01-12 DOI: 10.1016/j.autcon.2026.106763

Wei Tu , Yu Gu , Ruizhe Chen , Xing Zhang , Jiasong Zhu , Chisheng Wang , Qingquan Li

引用次数: 0

Mapping digital twin applications in infrastructure and the built environment across research types, methods, sectors, phases, and scales 绘制跨研究类型、方法、部门、阶段和规模的基础设施和建筑环境中的数字孪生应用

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2026-01-13 DOI: 10.1016/j.autcon.2026.106778

Soheila Kookalani , Stephen Green , Peihang Luo , Hamidreza Alavi , Erika Parn , Zhaojie Sun , Ioannis Brilakis

{"title":"Mapping digital twin applications in infrastructure and the built environment across research types, methods, sectors, phases, and scales","authors":"Soheila Kookalani , Stephen Green , Peihang Luo , Hamidreza Alavi , Erika Parn , Zhaojie Sun , Ioannis Brilakis","doi":"10.1016/j.autcon.2026.106778","DOIUrl":"10.1016/j.autcon.2026.106778","url":null,"abstract":"<div><div>Digital Twin technologies are increasingly used in infrastructure and the built environment to create dynamic, data-driven models of physical assets and processes. This review analyses recent advancements across sectors such as tunnels, bridges, roads, buildings, construction management, and urban planning, covering all life-cycle phases from design to operation. Integrating Digital Twins with Building Information Modelling, Internet of Things sensors, and Artificial Intelligence enhances real-time monitoring, decision-making, and asset performance. Key methods include monitoring, modelling, and simulation, which improve resource use and proactive maintenance. However, adoption faces challenges such as poor data interoperability, high costs, and technical complexity in merging multiple technologies. Ethical and governance issues around data privacy and security also persist. The review identifies future research needs in improving interoperability, expanding predictive analytics, and assessing large-scale impacts. It highlights Digital Twins' potential to improve resilience, efficiency, and sustainability, stressing the need for policy support and stakeholder collaboration.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106778"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962000","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

Pixel-level image localization for updating 3D digital twins of dams using frequency convolutional networks 基于频率卷积网络的大坝三维数字孪生体的像素级图像定位

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2025-12-14 DOI: 10.1016/j.autcon.2025.106715

Hang Zhao , Vahidreza Gharehbaghi , Caroline Bennett , Rémy D. Lequesne , Jian Li

引用次数: 0

Excavator trajectory planning via global probabilistic learning from expert demonstrations 基于专家演示的全局概率学习的挖掘机轨迹规划

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2025-12-20 DOI: 10.1016/j.autcon.2025.106736

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}

引用次数: 0

Frequency-aware crack segmentation network (FACS-net) and crack topology loss (CT-loss) for thin cracks 薄裂纹的频率感知裂纹分割网络（FACS-net）和裂纹拓扑损失（CT-loss）

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2025-12-15 DOI: 10.1016/j.autcon.2025.106719

Siheon Joo, Seokhwan Kim, Hongjo Kim

引用次数: 0

Multitask unified large vision-language model for post-earthquake structural damage assessment of buildings 地震后建筑物结构损伤评估的多任务统一大视觉语言模型

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2025-12-22 DOI: 10.1016/j.autcon.2025.106720

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}

引用次数: 0

Underwater defect measurement for bridge piers via non-planar refraction correction 桥墩水下缺陷的非平面折射校正测量

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.autcon.2025.106753

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}

引用次数: 0