Automation in Construction最新文献_第9页

Real-time in-tube concrete level tracking during concrete-filled steel tubular arch bridge construction using infrared thermography and computer vision 利用红外热像仪和计算机视觉对钢管混凝土拱桥施工过程中管内混凝土水平进行实时跟踪

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-24 DOI: 10.1016/j.autcon.2025.106227

Chongsheng Cheng , Jie Yu , Zhengsong Xiang , Shaorui Wang , Haonan Cai , Jianting Zhou , Hong Zhang

{"title":"Real-time in-tube concrete level tracking during concrete-filled steel tubular arch bridge construction using infrared thermography and computer vision","authors":"Chongsheng Cheng , Jie Yu , Zhengsong Xiang , Shaorui Wang , Haonan Cai , Jianting Zhou , Hong Zhang","doi":"10.1016/j.autcon.2025.106227","DOIUrl":"10.1016/j.autcon.2025.106227","url":null,"abstract":"<div><div>Automated remote monitoring of the concrete pouring process in concrete-filled steel tubular (CFST) arch bridges is a challenging task due to long distances, oblique camera angles, and occlusion, which hinder the accurate and continuous tracking of the process using existing computer vision (CV)-based methods. This paper proposed an integrated CV system for real-time, automated tracking and localization of the in-tube concrete pumping level with infrared thermography. The main contributions include: (1) Proposing a PNP-based orthographic rectification method to accurately correct the scale distortion of oblique infrared images for arch bridge structures. (2) Developing an improved Kalman filter method for stably tracking the concrete pumping level in infrared images with a low signal-to-noise ratio. The results show that the proposed system can achieve mm-level accuracy for the scaled model in indoor experiments, and its effectiveness is evaluated for an actual construction process at a distance of a hundred meters.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106227"},"PeriodicalIF":9.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863813","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

AI in automated sustainable construction engineering management 人工智能在自动化可持续建筑工程管理中的应用

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-24 DOI: 10.1016/j.autcon.2025.106202

Haytham Elmousalami , Mina Maxy , Felix Kin Peng Hui , Lu Aye

{"title":"AI in automated sustainable construction engineering management","authors":"Haytham Elmousalami , Mina Maxy , Felix Kin Peng Hui , Lu Aye","doi":"10.1016/j.autcon.2025.106202","DOIUrl":"10.1016/j.autcon.2025.106202","url":null,"abstract":"<div><div>The construction industry is responsible for approximately 39 % of global carbon emissions. This paper presents a systematic literature review on the advancements and applications of Artificial Intelligence (AI) in Automated Sustainable Construction Engineering Management. It investigates the transformative role of AI in driving construction automation, sustainability, and improving operational efficiency. The review highlights key IoT, digital twins, and big data analytics developments for real-time construction site monitoring, carbon footprint reduction, and structural health monitoring. In addition, project management automation, including automatic quality control and cost-duration prediction using green AI is reviewed. The article explores the integration of AI in construction robotics, including 4D printing and smart robotics. It highlights the importance of electroencephalogram for human-centric safety, augmented reality for training, and energy-efficient Heating, Ventilation, and Air Conditioning (HVAC) control systems for green buildings. It addresses challenges, limitations, and future research directions for AI in fostering sustainable, efficient, and automated construction.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106202"},"PeriodicalIF":9.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869536","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

Enhanced semi-supervised model for acoustic leak detection in water distribution networks 给水管网声学泄漏检测的改进半监督模型

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-24 DOI: 10.1016/j.autcon.2025.106228

Chang Wang , Wei Qian , Songtu Shen , Yu Shao , Tingchao Yu , Shipeng Chu

{"title":"Enhanced semi-supervised model for acoustic leak detection in water distribution networks","authors":"Chang Wang , Wei Qian , Songtu Shen , Yu Shao , Tingchao Yu , Shipeng Chu","doi":"10.1016/j.autcon.2025.106228","DOIUrl":"10.1016/j.autcon.2025.106228","url":null,"abstract":"<div><div>Automated acoustic leak detection is essential for the optimal operation and reliability management of Water Distribution Networks (WDNs). However, the limited availability of labeled acoustic signals hinders the performance and robustness of existing models, while large volumes of unlabeled acoustic data remain underutilized. To address these challenges, an enhanced semi-supervised model (ESSM) for acoustic leak detection is proposed in this paper, considering unlabeled data utilization, time-frequency data augmentation, and adaptive strategy. The performance of ESSM is evaluated from different perspectives, including model comparison and sensitivity analysis. When only 20 % of labeled data is available, ESSM exhibits competitive performance relative to the full-supervision model. Sensitivity analysis highlights the advantages of SpecAugment in acoustic data augmentation considering time-frequency characteristics, and the effectiveness of unlabeled data in enhancing leak detection performance. ESSM reduces the reliance on manual labeling, thereby improving the effectiveness and robustness of automated leak detection.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106228"},"PeriodicalIF":9.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869537","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

Integrating wooden altarpieces into H-BIM: Geometric profiling, complex artworks, and digital heritage mapping 将木制祭坛整合到H-BIM中：几何轮廓、复杂艺术品和数字遗产映射

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-23 DOI: 10.1016/j.autcon.2025.106213

Juan Moyano, Eva Martínez, Juan E. Nieto-Julián, María Fernández-Alconchel

{"title":"Integrating wooden altarpieces into H-BIM: Geometric profiling, complex artworks, and digital heritage mapping","authors":"Juan Moyano, Eva Martínez, Juan E. Nieto-Julián, María Fernández-Alconchel","doi":"10.1016/j.autcon.2025.106213","DOIUrl":"10.1016/j.autcon.2025.106213","url":null,"abstract":"<div><div>In the study and conservation of Cultural Heritage, various disciplines contribute to the research aimed at extracting information from both historical objects and heritage buildings. The contribution of this work is part of an interdisciplinary process to model, register, and evaluate complex models of knowledge. Evaluate a complex model, where there are works of art made of wood, painting, and sculpture. The results demonstrate a process of analysis and geometric characterisation of the shapes, in which most of the profiles are worked with a new methodology of the Best Fit Model (BFMP), and in which its analysis represents a deviation between a range of 7 and 9 mm. The development of low-relief models is based on the Poisson reconstruction equation, applied through a variable workflow using multiple software tools. Furthermore, an Entity Information Matrix (EIM) is introduced, enhancing the exchange and classification of architectural data. This study supports the integration of real-world 3D scans into BIM environments, providing a replicable model particularly suited for the digitization of altarpieces and façades.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106213"},"PeriodicalIF":9.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143858991","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

Self-adaptive seam detection framework for unmanned structural steel welding robots in unstructured environments 非结构化环境下无人驾驶结构钢焊接机器人自适应焊缝检测框架

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-23 DOI: 10.1016/j.autcon.2025.106221

Yuzhen He , Zhaoqi Huang , Haotian Liu , Jingang Ye , Yujie Lu , Xianzhong Zhao

{"title":"Self-adaptive seam detection framework for unmanned structural steel welding robots in unstructured environments","authors":"Yuzhen He , Zhaoqi Huang , Haotian Liu , Jingang Ye , Yujie Lu , Xianzhong Zhao","doi":"10.1016/j.autcon.2025.106221","DOIUrl":"10.1016/j.autcon.2025.106221","url":null,"abstract":"<div><div>Existing vision-based seam detection frameworks for structural steel welding robots perform well in static, predefined workspaces but struggle in dynamic, unstructured real production environments. Therefore, a self-adaptive seam detection framework is proposed that enables welding robots to interpret the actual welding environment through an estimation of the target workpiece pose. An enhanced pose estimation algorithm is developed to generate a reliable workpiece pose estimation based on solely one RGB image. Based on this real-time workpiece pose, the system autonomously determined the necessary robot movements toward an optimal position and orientation for subsequent high-precision structured light sensor measurements. The seam extraction and welding trajectory planning were then completed through an automated process. Experimental results demonstrate that the proposed framework not only enables cost-effective, fully automated weld seam detection in dynamic unstructured environments, but also achieves 72 % higher efficiency than conventional methods while eliminating human intervention.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106221"},"PeriodicalIF":9.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859003","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

Efficient instance segmentation framework for UAV-based pavement distress detection 基于无人机的路面破损检测的高效实例分割框架

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-22 DOI: 10.1016/j.autcon.2025.106195

Jiakai Zhou , Yang Wang , Wanlin Zhou

{"title":"Efficient instance segmentation framework for UAV-based pavement distress detection","authors":"Jiakai Zhou , Yang Wang , Wanlin Zhou","doi":"10.1016/j.autcon.2025.106195","DOIUrl":"10.1016/j.autcon.2025.106195","url":null,"abstract":"<div><div>Pavement distress detection is critical for ensuring road safety. Recently, Unmanned Aerial Vehicles (UAVs) become an efficient means of capturing large-scale pavement images. However, traditional pavement distress detection methods face challenges with UAV images: object detection lacks pixel-level information, while semantic segmentation fails to differentiate between individual instances. This paper introduces PDIS-Net, an instance segmentation framework specifically designed for UAV-based pavement distress detection. PDIS-Net first employs a fully dynamic convolution kernel generation strategy, predicting both kernel positions and weights. These kernels are then optimized via metric learning and kernel fusion. Finally, these high-quality kernels are convolved with feature maps to produce accurate instance masks. Experimental results on the UAPD-Instance dataset reveal that PDIS-Net achieves a mean average precision (mAP) of 78.1% at 30.8 FPS, outperforming other methods by 15.4%. Furthermore, real-world tests validate the robustness and effectiveness of PDIS-Net in highway pavement distress detection, highlighting its potential for practical deployment.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106195"},"PeriodicalIF":9.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854968","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

Parallel simulation and prediction techniques for digital twins in urban underground spaces 城市地下空间数字孪生体并行模拟与预测技术

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-21 DOI: 10.1016/j.autcon.2025.106212

Haofeng Gong , Dong Su , Shiqi Zeng , Xiangsheng Chen

{"title":"Parallel simulation and prediction techniques for digital twins in urban underground spaces","authors":"Haofeng Gong , Dong Su , Shiqi Zeng , Xiangsheng Chen","doi":"10.1016/j.autcon.2025.106212","DOIUrl":"10.1016/j.autcon.2025.106212","url":null,"abstract":"<div><div>With the advancement of intelligent systems and the increasing utilization of underground spaces, digital twin technology has become pivotal in enhancing the efficiency and safety of subterranean operations and maintenance. The concept of parallel simulation and prediction (PSP) technology, a critical component in the realization of digital twins, along with its associated research challenges, requires further elucidation. This paper offers a comprehensive overview of current research on PSP within the context of digital twins for urban underground spaces. The 62 papers meeting the inclusion and exclusion criteria are categorized into two key areas: model updating and future evolution prediction. Key challenges identified include the need for regular updates in geometric models, the demand for real-time predictive analytics, and data-related issues in information modeling. Finally, future research directions are outlined, focusing on the automatic interpretation of detection data, self-updating digital twin models, and multi-source heterogeneous data integration technologies.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106212"},"PeriodicalIF":9.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851690","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

Integration of BIM and ontologies for pumped storage hydropower design change management in EPC projects BIM与本体集成在抽水蓄能水电EPC项目设计变更管理中的应用

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-21 DOI: 10.1016/j.autcon.2025.106189

Shihang Zhang , Sherong Zhang , Han Liu , Xiaohua Wang , Zhiyong Zhao , Chao Wang , Lei Yan

引用次数: 0

Integrating domain-specific knowledge and fine-tuned general-purpose large language models for question-answering in construction engineering management 集成领域特定知识和精细调整的通用大型语言模型，用于建筑工程管理中的问答

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-21 DOI: 10.1016/j.autcon.2025.106206

Shenghua Zhou , Xuefan Liu , Dezhi Li , Tiantian Gu , Keyan Liu , Yifan Yang , Mun On Wong

{"title":"Integrating domain-specific knowledge and fine-tuned general-purpose large language models for question-answering in construction engineering management","authors":"Shenghua Zhou , Xuefan Liu , Dezhi Li , Tiantian Gu , Keyan Liu , Yifan Yang , Mun On Wong","doi":"10.1016/j.autcon.2025.106206","DOIUrl":"10.1016/j.autcon.2025.106206","url":null,"abstract":"<div><div>General-purpose Large Language Models (GLLMs) for Question-Answering (QA) of Construction Engineering Management (CEM) usually lack CEM knowledge and fine-tuning datasets, leading to unsatisfactory performance. Hence, this paper integrates the CEM External Knowledge Base (CEM-EKB) with out-of-domain fine-tuned GLLMs for CEM-QA. It encompasses (i) devising a process to develop the CEM-EKB with 235 documents, (ii) conducting out-of-domain fine-tuning to enhance GLLMs' abilities, (iii) integrating CEM-EKB with fine-tuned GLLMs, (iv) building CEM-QA test datasets with 5050 Multiple-Choice Questions (MCQs) and 100 Case-Based Questions (CBQs), and (v) comparing GLLMs' performance. The results indicate that CEM knowledge-incorporated fine-tuned GLLMs surpass original GLLMs by an average of 27.1 % in professional examinations, with an average improvement of 27.5 % across 7 CEM subdomains and 22.05 % for CBQs. This paper contributes to devising an effective, reusable, and updatable CEM-EKB; revealing the feasibility of out-of-domain datasets for fine-tuning; and sharing a large-scale CEM-QA test dataset.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106206"},"PeriodicalIF":9.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851689","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

Robust crack detection in complex slab track scenarios using STC-YOLO and synthetic data with highly simulated modeling 基于STC-YOLO和高度模拟建模的合成数据的复杂板坯轨道场景鲁棒裂纹检测

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-21 DOI: 10.1016/j.autcon.2025.106219

Wenbo Hu , Xianhua Liu , Zhizhang Zhou , Weidong Wang , Zheng Wu , Zhengwei Chen

引用次数: 0