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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
{"title":"Real-time lightweight YOLO model for grouting defect detection in external post-tensioned ducts via infrared thermography","authors":"","doi":"10.1016/j.autcon.2024.105830","DOIUrl":"10.1016/j.autcon.2024.105830","url":null,"abstract":"<div><div>It is challenging to distinguish the defective areas using infrared thermography to automatically analyze external post-tensioned tendon duct grouting defects. To achieve efficient and stable automated detection, a lightweight real-time grouting defects detection method based on YOLO deep learning is proposed. Firstly, the Cutpaste data augmentation method was used to effectively alleviate the problem of overfitting. Then, the C3Ghost module was introduced into the neck network, and the number of channels in the network layers was adjusted to 50 % of those in the YOLOv5s model, reducing the number of parameters and computational resources. Finally, the SGD optimizer and GIOU loss function, as well as the Sim attention module, were used to improve detection accuracy. Based on instance analysis and comparison, this method achieves [email protected] of 96.9 % and detection speed of 66FPS. Compared with YOLOv5s, it reduces the number of parameters by 79 % and FLOPs by 77 %.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534918","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
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
{"title":"Paving block displacement detection and measurement using 3D laser sensors on unmanned ground vehicles","authors":"","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":null,"pages":null},"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
{"title":"Impact of environmental pollutants on work performance using virtual reality","authors":"","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":null,"pages":null},"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
{"title":"Artificial intelligence driven tunneling-induced surface settlement prediction","authors":"","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":null,"pages":null},"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
{"title":"Automated progress monitoring of land development projects using unmanned aerial vehicles and machine learning","authors":"","doi":"10.1016/j.autcon.2024.105827","DOIUrl":"10.1016/j.autcon.2024.105827","url":null,"abstract":"<div><div>In land development projects, effective control of the engineering progress is crucial for managing construction quality and costs. However, the conventional approach to monitoring progress is inadequate for large-scale projects. This paper proposes a technique that utilizes UAV images and machine learning techniques to monitor land development projects. The object detection and image segmentation techniques were used to detect essential construction objects. The detected objects were automatically compared to design drawings for checking the progress of the project. Moreover, to ensure personnel safety during construction, an automated process for identifying locations requiring safety barriers was also designed in the study. The effectiveness of all the proposed techniques was evaluated in a real case study. It is illustrated that this fully automated approach for land development monitoring is efficient and thus can contribute to construction safety, cost reduction, and quality assurance in a land development project.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442010","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
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
{"title":"Weakly supervised 3D point cloud semantic segmentation for architectural heritage using teacher-guided consistency and contrast learning","authors":"","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":null,"pages":null},"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
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
{"title":"Fully automated extraction of railtop centerline from mobile laser scanning data","authors":"","doi":"10.1016/j.autcon.2024.105812","DOIUrl":"10.1016/j.autcon.2024.105812","url":null,"abstract":"<div><div>Digitization is an important part of efficient infrastructure maintenance. Means to achieve a digital asset database include precise 3D surveys of the physical assets and advanced automated recognition of objects of interest for documenting, maintenance and further analysis purposes. To this end, fast data collection of railway infrastructure environments can be obtained using a mobile laser scanner mounted on a service locomotive, permitting uninterruptive service. This paper presents an algorithm that extracts the railtop centerlines of up to seven parallel tracks with a single measurement pass and achieves an accuracy of 0.3<!--> <!-->cm to 0.8<!--> <!-->cm on non-intersecting rails, which improves the state of the art by 55%–85%. On intersecting rails, the railtop location accuracy is comparable to that of existing methods. The proposed method uses only geometric data and performs in real time in two-track railroad configurations.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441930","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
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
{"title":"Generation of LOD4 models for buildings towards the automated 3D modeling of BIMs and digital twins","authors":"","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":null,"pages":null},"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
Quantitative assessment of cracks in concrete structures using active-learning-integrated transformer and unmanned robotic platform 利用主动学习集成变压器和无人机器人平台对混凝土结构裂缝进行定量评估
IF 9.6 1区 工程技术
Automation in Construction Pub Date : 2024-10-15 DOI: 10.1016/j.autcon.2024.105829
{"title":"Quantitative assessment of cracks in concrete structures using active-learning-integrated transformer and unmanned robotic platform","authors":"","doi":"10.1016/j.autcon.2024.105829","DOIUrl":"10.1016/j.autcon.2024.105829","url":null,"abstract":"<div><div>Quantitative assessment of cracks in concrete bridges is crucial for structural health monitoring and digital twin. However, the training of crack segmentation models relies heavily on annotation resources, and their segmentation capabilities are often unsatisfactory in terms of the accuracy of boundary location of thin cracks encountered in practice. In this paper, an active-learning-integrated crack segmentation transformer (ACS-Former) framework is proposed to maximize segmentation performance with limited annotation resources. The two-branch ACS-Former includes (1) a feature pyramid transformer (FPT) for multi-scale crack segmentation and (2) boundary difficulty-aware active learning (BDAL) to select informative images for labeling and incorporation into FPT training. Additionally, an adhesive climbing robot is proposed for image collection of hard-to-access components of large bridges. The on-site operational feasibility and practicability of the proposed ACS-Former and climbing robot are demonstrated by field experiments performed on in-service bridges, including the quantification of cracks narrower than 0.2 mm, as required by engineering codes.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433452","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
Fast 3D site reconstruction using multichannel dynamic and static object separation 利用多通道动态和静态物体分离快速重建三维场地
IF 9.6 1区 工程技术
Automation in Construction Pub Date : 2024-10-14 DOI: 10.1016/j.autcon.2024.105807
{"title":"Fast 3D site reconstruction using multichannel dynamic and static object separation","authors":"","doi":"10.1016/j.autcon.2024.105807","DOIUrl":"10.1016/j.autcon.2024.105807","url":null,"abstract":"<div><div>Three-dimensional (3D) models, characterized by their visualization, accuracy, and interactive information presentation, effectively facilitate collaboration and optimize management throughout the construction process. However, existing 3D reconstruction methods frequently fail to simultaneously satisfy the requirements for onsite applicability and fast performance. To address this challenge, this paper proposes a monocular camera-based 3D reconstruction method designed for onsite applicability and introduces dynamic–static separation to reduce the computational burden for faster processing. This approach enables the preestablishment of 3D models for static and dynamic objects. The positioning, pose, and orientation information of objects can be quickly integrated from multiple channels for fast 3D site reconstruction. Experimental results demonstrate that target objects can be identified across multiple channels and quickly integrated into 3D models. This paper offers both theoretical and practical contributions by enabling 3D reconstruction of construction sites using monocular cameras, which enhances project safety management and supports the implementation of digital twins.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433449","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
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