Automation in Construction最新文献_第4页

Partial annotations in active learning for semantic segmentation 语义分割主动学习中的部分注释

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-22 DOI: 10.1016/j.autcon.2024.105828

B.G. Pantoja-Rosero , A. Chassignet , A. Rezaie , M. Kozinski , R. Achanta , K. Beyer

{"title":"Partial annotations in active learning for semantic segmentation","authors":"B.G. Pantoja-Rosero , A. Chassignet , A. Rezaie , M. Kozinski , R. Achanta , K. Beyer","doi":"10.1016/j.autcon.2024.105828","DOIUrl":"10.1016/j.autcon.2024.105828","url":null,"abstract":"<div><div>Semantic segmentation with deep learning plays a crucial role in various fields, including civil engineering, particularly in tasks such as damage assessment and urban planning. This paper addresses the challenge of efficiently training deep learning models for semantic segmentation with a limited set of annotated data, thus reducing the burden of ground truth labeling. An active learning strategy is introduced, leveraging partial annotations informed by predictions and uncertainties from previously trained models. Unlike other active learning frameworks, this approach not only facilitates the annotation of highly uncertain image regions but also targets those with low uncertainty, which often lead to false positives and negatives. The results demonstrate that using partial annotations within an active learning framework significantly reduces manual annotation efforts and training time without compromising model performance. These findings have substantial implications for the efficiency and scalability of deep learning in civil engineering, paving the way for future research in active learning and semantic segmentation.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105828"},"PeriodicalIF":9.6,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigating construction workers' perception of risk, likelihood, and severity using electroencephalogram and machine learning 利用脑电图和机器学习调查建筑工人对风险、可能性和严重性的感知

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-22 DOI: 10.1016/j.autcon.2024.105814

Zhengkai Zhao , Shu Zhang , Xinyu Hua , Xiuzhi Shi

{"title":"Investigating construction workers' perception of risk, likelihood, and severity using electroencephalogram and machine learning","authors":"Zhengkai Zhao , Shu Zhang , Xinyu Hua , Xiuzhi Shi","doi":"10.1016/j.autcon.2024.105814","DOIUrl":"10.1016/j.autcon.2024.105814","url":null,"abstract":"<div><div>Understanding how workers perceive risk is essential to construction safety management. Firstly, an event-related potential (ERP) experiment was conducted to investigate the relationship between risk, likelihood, and severity. Then, a linear model was developed to predict workers' risk perception based on ERP components and quantify the relative importance of severity to likelihood. Finally, an additive model was constructed to reflect the risk perception pattern. The results indicate: (1) Workers' emotional responses stem from the process of associating accident consequences in severity assessment, which is represented by the late positive potential (LPP) component. (2) Workers' risk perception relies more on severity compared with likelihood. (3) The additive model (risk = 0.203 * likelihood +0.758 * severity) better matches the risk perception patterns than the multiplicative model. The research results provide a new perspective for understanding workers' risk perception patterns and contributing to proactive safety management in the construction industry.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105814"},"PeriodicalIF":9.6,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534955","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 lightweight YOLO model for grouting defect detection in external post-tensioned ducts via infrared thermography 通过红外热成像检测外部后张法管道灌浆缺陷的实时轻量级 YOLO 模型

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-21 DOI: 10.1016/j.autcon.2024.105830

Shengli Li , Shiji Sun , Yang Liu , Wanshuai Qi , Nan Jiang , Can Cui , Pengfei Zheng

引用次数: 0

Paving block displacement detection and measurement using 3D laser sensors on unmanned ground vehicles 利用无人地面车辆上的 3D 激光传感器检测和测量铺路块的位移

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-17 DOI: 10.1016/j.autcon.2024.105813

Jiwoo Shin , Seoyeon Kim , Young-Hoon Jung , Hong Min , Taesik Kim , Jinman Jung

{"title":"Paving block displacement detection and measurement using 3D laser sensors on unmanned ground vehicles","authors":"Jiwoo Shin , Seoyeon Kim , Young-Hoon Jung , Hong Min , Taesik Kim , Jinman Jung","doi":"10.1016/j.autcon.2024.105813","DOIUrl":"10.1016/j.autcon.2024.105813","url":null,"abstract":"<div><div>Construction sites with deep excavation in urban areas can induce ground deformation, potentially harming nearby infrastructure. Therefore, monitoring construction sites is crucial. Typically, a sidewalk is located adjacent to the construction site, and ground deformation can cause the displacement of paving blocks. Accurate measurement of paving block displacement and cracks is essential. This paper proposes an efficient automated detection and measurement method using a 3D laser line sensor on Unmanned Ground Vehicles (UGVs), emphasizing online measurement capabilities. The method involves two steps: detecting target objects via 2D projection from 3D point cloud data and measuring object features by reducing unnecessary data with the Clustered Piecewise Linear Fitting (CPLF) algorithm. This two-step process enhances parallelism between edge servers and devices, thereby reducing total processing time. Prototype implementation and experiments show that our method achieves low errors of accuracy and is suitable for automated online detection and measurement on UGVs.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105813"},"PeriodicalIF":9.6,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446980","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

Impact of environmental pollutants on work performance using virtual reality 利用虚拟现实技术研究环境污染物对工作表现的影响

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-17 DOI: 10.1016/j.autcon.2024.105833

Juwon Hong , Sangkil Song , Chiwan Ahn , Choongwan Koo , Dong-Eun Lee , Hyo Seon Park , Taehoon Hong

{"title":"Impact of environmental pollutants on work performance using virtual reality","authors":"Juwon Hong , Sangkil Song , Chiwan Ahn , Choongwan Koo , Dong-Eun Lee , Hyo Seon Park , Taehoon Hong","doi":"10.1016/j.autcon.2024.105833","DOIUrl":"10.1016/j.autcon.2024.105833","url":null,"abstract":"<div><div>Virtual reality-based experiments were conducted to assess the impacts of environmental pollutants (i.e., noise, vibration, and dust) on work performance. In these experiments, concrete chipping work was performed in eight different exposure environments based on exposure to three environmental pollutants to measure data related to work performance: (i) work performance metrics, including work duration and accuracy; and (ii) mental workload. The relationships between data related to work performance and environmental pollutants were then analyzed using statistical techniques as follows: First, work duration was statistically significantly affected by dust, while work accuracy was significantly affected by vibration. Second, mental workload was statistically significantly affected by all three environmental pollutants, increasing with the number of environmental pollutants the workers exposed to. Third, all data related to work performance were found to be correlated with each other. These findings provide insights into improving work performance by managing environmental pollutants in the construction industry.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105833"},"PeriodicalIF":9.6,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446979","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

Artificial intelligence driven tunneling-induced surface settlement prediction 人工智能驱动的隧道诱导地表沉降预测

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-17 DOI: 10.1016/j.autcon.2024.105819

Muyuan Song , Minghui Yang , Gaozhan Yao , Wei Chen , Zhuoyang Lyu

{"title":"Artificial intelligence driven tunneling-induced surface settlement prediction","authors":"Muyuan Song , Minghui Yang , Gaozhan Yao , Wei Chen , Zhuoyang Lyu","doi":"10.1016/j.autcon.2024.105819","DOIUrl":"10.1016/j.autcon.2024.105819","url":null,"abstract":"<div><div>There has been an increasing demand for shield tunnel construction due to the extensive utilization and limited land in metropolitan cities. However, the behaviors of soils and rocks exhibit a high level of uncertainty in material modeling. Artificial Intelligence (AI) techniques exhibit huge potential in addressing geotechnical issues that involve non-linear soil-structure interaction. This paper aims to review AI-driven prediction of tunneling-induced surface settlement, focusing on aspects of dataset establishment, input feature selection, and hyperparameter optimization. An overview of AI key applications in surface settlement prediction over the past decades is compiled. Furthermore, the capabilities and limitations of diverse AI techniques are discussed, guiding the selection of methodologies for different scenarios. Subsequently, recent developments such as AI variants, the latest optimization algorithms, and cutting-edge methods are illustrated. Lastly, possible countermeasures of AI for challenges in pragmatic applications are proposed, offering orientations for further research in AI-driven tunneling-induced surface settlement prediction.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105819"},"PeriodicalIF":9.6,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446981","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 progress monitoring of land development projects using unmanned aerial vehicles and machine learning 利用无人飞行器和机器学习自动监测土地开发项目的进度

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-16 DOI: 10.1016/j.autcon.2024.105827

Jen-Yu Han , Chin-Rou Hsu , Chun-Jia Huang

引用次数: 0

Fully automated extraction of railtop centerline from mobile laser scanning data 从移动激光扫描数据中全自动提取轨顶中心线

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-16 DOI: 10.1016/j.autcon.2024.105812

Aleksi Kononen , Harri Kaartinen , Antero Kukko , Matti Lehtomäki , Josef Taher , Juha Hyyppä

引用次数: 0

Weakly supervised 3D point cloud semantic segmentation for architectural heritage using teacher-guided consistency and contrast learning 利用教师指导的一致性和对比度学习对建筑遗产进行弱监督三维点云语义分割

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-16 DOI: 10.1016/j.autcon.2024.105831

Shuowen Huang , Qingwu Hu , Mingyao Ai , Pengcheng Zhao , Jian Li , Hao Cui , Shaohua Wang

{"title":"Weakly supervised 3D point cloud semantic segmentation for architectural heritage using teacher-guided consistency and contrast learning","authors":"Shuowen Huang , Qingwu Hu , Mingyao Ai , Pengcheng Zhao , Jian Li , Hao Cui , Shaohua Wang","doi":"10.1016/j.autcon.2024.105831","DOIUrl":"10.1016/j.autcon.2024.105831","url":null,"abstract":"<div><div>Point cloud semantic segmentation is significant for managing and protecting architectural heritage. Currently, fully supervised methods require a large amount of annotated data, while weakly supervised methods are difficult to transfer directly to architectural heritage. This paper proposes an end-to-end teacher-guided consistency and contrastive learning weakly supervised (TCCWS) framework for architectural heritage point cloud semantic segmentation, which can fully utilize limited labeled points to train network. Specifically, a teacher-student framework is designed to generate pseudo labels and a pseudo label dividing module is proposed to distinguish reliable and ambiguous point sets. Based on it, a consistency and contrastive learning strategy is designed to fully utilize supervision signals to learn the features of point clouds. The framework is tested on the ArCH dataset and self-collected point cloud, which demonstrates that the proposed method can achieve effective semantic segmentation of architectural heritage using only 0.1 % of annotated points.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105831"},"PeriodicalIF":9.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442011","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

Generation of LOD4 models for buildings towards the automated 3D modeling of BIMs and digital twins 生成建筑物 LOD4 模型，实现 BIM 和数字双胞胎的自动 3D 建模

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-16 DOI: 10.1016/j.autcon.2024.105822

B.G. Pantoja-Rosero , A. Rusnak , F. Kaplan , K. Beyer

{"title":"Generation of LOD4 models for buildings towards the automated 3D modeling of BIMs and digital twins","authors":"B.G. Pantoja-Rosero , A. Rusnak , F. Kaplan , K. Beyer","doi":"10.1016/j.autcon.2024.105822","DOIUrl":"10.1016/j.autcon.2024.105822","url":null,"abstract":"<div><div>An image-based methodology is presented for the automatic generation of geometric building models at LOD4, incorporating both interior and exterior geometrical information. Existing approaches often focus on simplified geometries for either exteriors or interiors, leading to integration challenges due to data complexity and processing demands. This methodology addresses these challenges by utilizing three structure-from-motion models: one for the building exterior, one for the interior, and one for the entrance. The exterior and interior data are processed separately using planar primitives, and the models are subsequently aligned through a 3D point cloud registration method based on 2D image features. This ensures a unified coordinate system and accurate generation of the LOD4 model. The framework achieved a mean relative error of 3.06% and a mean absolute error of 0.05 m, underscoring its robustness for applications such as numerical modeling, construction management, and structural health monitoring, making it valuable for further advancements in building information models and digital twins.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105822"},"PeriodicalIF":9.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0