Automation in Construction最新文献_第6页

Self-hosted multimodal large language models for speech-driven perception and navigation in construction robotics 建筑机器人中语音驱动感知和导航的自托管多模态大语言模型

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-03-01 Epub Date: 2026-01-31 DOI: 10.1016/j.autcon.2026.106805

Muhammad Anas Gopee , Samuel A. Prieto , Borja García de Soto

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Semantic naming convention-based automated BIM generation of precast concrete components from 2D CAD drawings 基于语义命名约定的2D CAD图纸自动生成预制混凝土构件的BIM

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-03-01 Epub Date: 2026-02-03 DOI: 10.1016/j.autcon.2026.106804

Eunbeen Jeong , Seoyoung Jung , Insu Jung , Kyujin Ko , Junyoung Jang

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Construction site object detection with active transfer learning and weighted adaptive uncertainty-diversity sampling using a small imbalanced dataset 基于主动迁移学习和加权自适应不确定性多样性采样的小型不平衡数据集建筑工地目标检测

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-03-01 Epub Date: 2026-02-03 DOI: 10.1016/j.autcon.2026.106819

Karunakar Reddy Mannem , Eyob Mengiste , Dhanu Vardhan , Borja García de Soto , Fernando Bacao

{"title":"Construction site object detection with active transfer learning and weighted adaptive uncertainty-diversity sampling using a small imbalanced dataset","authors":"Karunakar Reddy Mannem , Eyob Mengiste , Dhanu Vardhan , Borja García de Soto , Fernando Bacao","doi":"10.1016/j.autcon.2026.106819","DOIUrl":"10.1016/j.autcon.2026.106819","url":null,"abstract":"<div><div>This paper presents an active learning framework for robust object detection in dynamic construction environments, addressing the challenges of limited labeled data and high annotation costs. The framework integrates YOLOv10 with a weighted, adaptive uncertainty–diversity sampling strategy and employs transfer learning to mitigate cold-start issues and accelerate model convergence. The adaptive fusion mechanism dynamically weights multiple uncertainty measures (classification confidence, class entropy, and bounding box variance) while incorporating sample diversity to prioritize the most informative data. Experiments achieved mAP50 of 0.885 and mAP50–95 of 0.730 by Cycle 8. Rare classes showed notable gains: Circular ducts improved from 0.25 to 0.60 mAP50, and Drywall panels from 0.35 to 0.65. The approach reduced labeling effort by 30–40% compared to random sampling, showing its potential to improve the development of construction element recognition algorithms, including small items that are often not visible, such as safety equipment, building parts, and construction tools.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106819"},"PeriodicalIF":11.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110226","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

Artifact-driven LLM integration for mouseless design workflows 无鼠标设计工作流的工件驱动LLM集成

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-03-01 Epub Date: 2026-01-16 DOI: 10.1016/j.autcon.2026.106766

Ghang Lee , Sejin Park , Soo-in Yang

{"title":"Artifact-driven LLM integration for mouseless design workflows","authors":"Ghang Lee , Sejin Park , Soo-in Yang","doi":"10.1016/j.autcon.2026.106766","DOIUrl":"10.1016/j.autcon.2026.106766","url":null,"abstract":"<div><div>This paper investigates “mouseless design” feasibility, replacing traditional mouse-based interfaces with natural language interaction, in professional design practice. A three-month experiment tested LLMs for developing a sports complex project for competition. Through triangulation analysis of 2162 conversation turns, 1281 messages, and an 84-page design journal, this study established a quantitative baseline for LLM performance across professional design workflows. It revealed 86.9% unsuccessful individual interactions despite successful project completion and identified inconsistent spatial reasoning and geometry handling as the main weaknesses. Two methodological breakthroughs using conversational programming overcame these limitations: the “artifact-driven” approach repositioning LLMs as custom digital tool creators rather than direct design generators, and self-learning approaches extending complex BIM functionality. A statistical analysis (χ<sup>2</sup>(90) = 156, Cramer's V = 0.120) shows that terminology alignment serves as a success multiplier when combined with other strategies. These contributions provide empirical evidence for natural language-driven design while identifying critical requirements for successful AI integration.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106766"},"PeriodicalIF":11.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976063","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

Control strategies for Cellular Automata-based generative design in architecture and urbanism 基于元胞自动机的生成式建筑与城市设计控制策略

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1016/j.autcon.2025.106754

Yiming Liu, Christiane M. Herr

{"title":"Control strategies for Cellular Automata-based generative design in architecture and urbanism","authors":"Yiming Liu, Christiane M. Herr","doi":"10.1016/j.autcon.2025.106754","DOIUrl":"10.1016/j.autcon.2025.106754","url":null,"abstract":"<div><div>As Artificial Intelligence transforms design through decentralised and self-organising generative systems, Cellular Automata (CA) exemplify a foundational yet underexplored paradigm capable of bridging rule-based emergence and computational creativity in architecture and urbanism. Driven by simple local rules, CA produce spatially responsive and systemic patterns well-suited to capturing the dynamics of complex interrelated systems, making them valuable for generative design exploration. This review systematically investigates control strategies for guiding CA-based generative processes. It identifies temporal logic methods for adjusting CA behaviour through bibliometric analysis. The review further demonstrates control factors, computational control, and human-mediated control, analysing their impact on the adaptability of CA design processes at each stage through the content-based synthesis. The results reveal the advantages of different control strategies in guiding goal-directed CA generation. This study advances the understanding of CA-based design mechanisms and highlights opportunities to develop intelligent control, process-oriented design tools integrating data-driven and AI technologies.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106754"},"PeriodicalIF":11.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071952","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

Knowledge-augmented multi-modal data fusion and reasoning for automated crane lift monitoring 基于知识增强的多模态数据融合与推理的自动起重机升力监测

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-03-01 Epub Date: 2026-02-07 DOI: 10.1016/j.autcon.2026.106822

Junlin Wang , Songbo Hu , Yihai Fang , Hongling Guo

{"title":"Knowledge-augmented multi-modal data fusion and reasoning for automated crane lift monitoring","authors":"Junlin Wang , Songbo Hu , Yihai Fang , Hongling Guo","doi":"10.1016/j.autcon.2026.106822","DOIUrl":"10.1016/j.autcon.2026.106822","url":null,"abstract":"<div><div>Crane lift operations are critical to construction productivity, yet their monitoring remains largely manual, fragmented, and inefficient. This paper introduces a knowledge-augmented, multi-modal data fusion and reasoning framework for the automated tracking and analysis of crane lifts. The proposed approach integrates a domain ontology to fuse computer vision, sensor signals, and schedule data, enabling a hierarchical hybrid reasoning pipeline that infers transient behaviours, segments complete operations, and maps them to scheduled tasks via similarity metrics. In a two-day field experiment, the system accurately recognised eight distinct behaviours with an overall accuracy of 0.911 and an average F1 score of 0.907, segmented lift cycles with a median duration error under 10 s, and mapped operations to scheduled orders with an F1 score of 0.944 and accuracy of 0.905. These results demonstrate the technical feasibility and robustness of the framework, which transforms low-level data into high-level, context-rich knowledge to support productivity assessment and workflow optimisation in construction environments.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106822"},"PeriodicalIF":11.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138879","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

From geometry to graph: Automation of building performance modeling via convex graph encoding 从几何到图形：通过凸图编码实现建筑性能建模的自动化

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-03-01 Epub Date: 2026-02-04 DOI: 10.1016/j.autcon.2026.106815

Yihui Li , Jun Xiao , Hao Zhou , Borong Lin

{"title":"From geometry to graph: Automation of building performance modeling via convex graph encoding","authors":"Yihui Li , Jun Xiao , Hao Zhou , Borong Lin","doi":"10.1016/j.autcon.2026.106815","DOIUrl":"10.1016/j.autcon.2026.106815","url":null,"abstract":"<div><div>Early-stage design decisions strongly influence a building’s lifetime energy performance, yet the transformation from architectural geometry to analysis-ready models remains fragmented. This paper presents CUGER, an automated framework that converts complex 3D building geometries into physically consistent heterogeneous graphs for building energy analysis. CUGER integrates a convex optimization-based segmentation algorithm with structured graph encoding to directly transform CAD geometries into simulation- and learning-ready representations. Evaluated on 266 architecturally diverse building models, the framework achieved an average geometric-to-graph conversion success rate above 80% with strong topological consistency (<span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>82</mn></mrow></math></span>). Building on the generated graphs, a heterogeneous ZoneGNN model enables zone-level energy prediction of single building, achieving accurate and stable performance (<span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>88</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>01</mn></mrow></math></span>) and outperforming MLP baselines, particularly under limited training data. Overall, CUGER establishes a topology-preserving and fully automatable bridge between architectural modeling, performance simulation, and data-driven prediction for early-stage building design.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106815"},"PeriodicalIF":11.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146185054","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

Distributed acoustic sensing for monitoring engineering infrastructure: Mechanisms, signal analytics, and applications 分布式声学传感监测工程基础设施：机制，信号分析和应用

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-03-01 Epub Date: 2026-01-17 DOI: 10.1016/j.autcon.2026.106784

Yuanyuan Li , Runze Zhao , Yin Liu , Hongnan Li , Qingrui Yue , Hongbing Chen

{"title":"Distributed acoustic sensing for monitoring engineering infrastructure: Mechanisms, signal analytics, and applications","authors":"Yuanyuan Li , Runze Zhao , Yin Liu , Hongnan Li , Qingrui Yue , Hongbing Chen","doi":"10.1016/j.autcon.2026.106784","DOIUrl":"10.1016/j.autcon.2026.106784","url":null,"abstract":"<div><div>Vibration monitoring of engineering infrastructures is indispensable for structural safety and scientific maintenance. Distributed acoustic sensing (DAS) has been increasingly adopted in engineering field, owing to its attractive characteristics over conventional point-based transducers, including high spatial resolution, spatial continuity, non-invasiveness and superior stability. These advantages align well with instrumentation requirements for long-term and widely-distributed vibration monitoring in large-scale infrastructures. Accordingly, this paper provides a systematic review of DAS technique with respect to sensing mechanisms, deployment strategies, signal analysis, and typical applications. This review is structured around a complete operational workflow that explicates what the technology is, how it works, and what it enables in practice. Furthermore, current challenges and promising directions are discussed to envisage the widespread implementation of DAS systems, with the ultimate goal of automated monitoring for infrastructures. This review also aims to provide an exhaustive reference for researchers, professionals or engineering inspectors seeking state-of-the-art in DAS research.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106784"},"PeriodicalIF":11.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976064","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

Real-time robotic teleoperation for pavement pothole segmentation, quantification, and localization using multimodal sensing and efficient multi-scale attention-enhanced edge deep learning 利用多模态传感和高效的多尺度注意力增强边缘深度学习进行路面坑洼分割、量化和定位的实时机器人远程操作

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-03-01 Epub Date: 2026-02-04 DOI: 10.1016/j.autcon.2026.106806

Xi Hu , Rayan H. Assaad

{"title":"Real-time robotic teleoperation for pavement pothole segmentation, quantification, and localization using multimodal sensing and efficient multi-scale attention-enhanced edge deep learning","authors":"Xi Hu , Rayan H. Assaad","doi":"10.1016/j.autcon.2026.106806","DOIUrl":"10.1016/j.autcon.2026.106806","url":null,"abstract":"<div><div>This paper proposes a robotic teleoperation pipeline to automate the segmentation, quantification, localization, and visualization of pavement potholes in real-time. The pipeline includes a new attention-based deep learning (DL) model and integrates a 4WD robot, teleoperation workstation, multimodal RGBD sensing fusion and point cloud processing on the edge, and interactive web application through cloud services. The DL model was developed by incorporating an efficient multi-scale attention (EMA) mechanism and transfer learning, which was trained and tested on a pavement dataset with 9472 images. The pipeline was validated through real-world field tests. The new EMA-based DL model yielded a 0.611 mAP50–95(B) and a 0.613 mAP50–95(M), outperforming the YOLOv9 baseline by 8.33% and 6.98%, respectively. The findings also showed that the proposed pipeline successfully automates pothole inspection and generates an interactive map, enabling remote access to the robot's trajectory and detailed pothole information, including pothole area, volume, average and maximum depth.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106806"},"PeriodicalIF":11.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146185057","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 lightweight networks for multi-material bridge crack segmentation 多材料桥梁裂缝分割的自动化轻量化网络

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-03-01 Epub Date: 2026-01-30 DOI: 10.1016/j.autcon.2026.106808

Mohammed Ameen Mohammed , Haijun Zhou , Jiaolei Zhang , Shikun Xu

{"title":"Automated lightweight networks for multi-material bridge crack segmentation","authors":"Mohammed Ameen Mohammed , Haijun Zhou , Jiaolei Zhang , Shikun Xu","doi":"10.1016/j.autcon.2026.106808","DOIUrl":"10.1016/j.autcon.2026.106808","url":null,"abstract":"<div><div>Crack segmentation on concrete, steel, and asphalt surfaces remains challenging due to irregular crack patterns, low contrast, and noise interference, particularly in complex environments. Although deep neural network–based methods show promise, they often struggle to balance fine-grained feature extraction with contextual understanding. Moreover, no unified model effectively detects cracks across concrete, steel bridges, and asphalt pavements on bridge decks, while most existing models are too large for edge deployment. This paper introduces CrackSeg-GWD, a lightweight encoder–decoder model integrating Group Normalization, Weight-Standardized Convolutions, DropBlock regularization, and a Symmetric Unified Focal Loss to enhance stability, reduce overfitting, and handle class imbalance. With only 0.414 M parameters and 0.849 GFLOPs, it achieves high accuracy with low computational cost. Evaluated on five public datasets, SteelCrack, YCD, Crack500, DeepCrack, and Ozgenel, CrackSeg-GWD outperforms ten state-of-the-art models, achieving consistent gains across five metrics and confirming its suitability for real-time structural monitoring and construction automation.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106808"},"PeriodicalIF":11.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074978","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