Automation in Construction最新文献_第9页

Tracking multiple construction workers using pose estimation and feature-assisted re-identification model 利用姿势估计和特征辅助再识别模式跟踪多个建筑工人

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-09-21 DOI: 10.1016/j.autcon.2024.105771

Nasrullah Khan , Syed Farhan Alam Zaidi , Muhammad Sibtain Abbas , Doyeop Lee , Dongmin Lee

{"title":"Tracking multiple construction workers using pose estimation and feature-assisted re-identification model","authors":"Nasrullah Khan , Syed Farhan Alam Zaidi , Muhammad Sibtain Abbas , Doyeop Lee , Dongmin Lee","doi":"10.1016/j.autcon.2024.105771","DOIUrl":"10.1016/j.autcon.2024.105771","url":null,"abstract":"<div><div>Tracking construction workers is crucial for ensuring worker safety, productivity, appropriate resource allocation, and regulatory compliance. However, when multiple workers resemble each other or temporary obstructions occur, maintaining accurate identification of individual workers with computer-vision-based tracking techniques is challenging. This paper proposes a multi-worker tracking framework comprising three key components: 1) a pose estimation model that localizes and generates keypoints for each worker, 2) a selective region algorithm with unique visual signatures and a re-identification (ReID) model that extracts features to distinguish workers, and 3) data association techniques that accurately track multiple workers simultaneously. The evaluation results obtained by using the higher-order tracking accuracy (HOTA) and multi-object tracking accuracy (MOTA) metrics on 16 annotated videos demonstrate the effectiveness of the framework. The selective region algorithm, combined with different configurations of trackers and ReID models, achieves an HOTA index of 85.83 % across various scenarios. This pre-emptive intermediation fosters multi-worker monitoring in dynamic construction environments.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105771"},"PeriodicalIF":9.6,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275782","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

Lightweight pruning model for road distress detection using unmanned aerial vehicles 利用无人驾驶飞行器进行道路状况检测的轻量级修剪模型

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-09-21 DOI: 10.1016/j.autcon.2024.105789

Shengchuan Jiang , Hui Wang , Zhipeng Ning , Shenglin Li

引用次数: 0

Automatic assessment of concrete cracks in low-light, overexposed, and blurred images restored using a generative AI approach 利用生成式人工智能方法自动评估低照度、曝光过度和模糊图像中的混凝土裂缝修复情况

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-09-21 DOI: 10.1016/j.autcon.2024.105787

Pengwei Guo , Xiangjun Meng , Weina Meng , Yi Bao

{"title":"Automatic assessment of concrete cracks in low-light, overexposed, and blurred images restored using a generative AI approach","authors":"Pengwei Guo , Xiangjun Meng , Weina Meng , Yi Bao","doi":"10.1016/j.autcon.2024.105787","DOIUrl":"10.1016/j.autcon.2024.105787","url":null,"abstract":"<div><div>Deep learning-based computer vision techniques have high efficiency in assessing concrete cracks from images, and the assessment can be automated using robots for higher efficiency. However, assessment accuracy is often compromised by low-quality images. This paper presents a Conditional Generative Adversarial Network (CGAN)-based approach to restore low-light, overexposed, and blurred images. The approach integrates attention mechanisms and residual learning and uses Wasserstein loss with gradient penalty. Crack assessment results show that the proposed approach outperforms state-of-the-art methods, regarding structural similarity (SSIM: 0.78 for deblurring, 0.95 for low-light enhancement, and 0.96 for overexposure correction) and peak signal-to-noise ratio (PSNR: 28.6 for deblurring, 31.4 for low-light enhancement, and 31.6 for overexposure correction). Restored images have been used to train a deep learning model for assessing concrete cracks. The Intersection over Union (IoU) and F1 score of crack segmentation are higher than 0.98 and 0.99, respectively, revealing high accuracy in crack assessment tasks.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105787"},"PeriodicalIF":9.6,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275780","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

Multi-scalar robotic fabrication system for on-site press gluing in multi-storey timber buildings 用于多层木质建筑现场压胶的多尺度机器人制造系统

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-09-20 DOI: 10.1016/j.autcon.2024.105774

Nils Opgenorth , Daniel Nunes Locatelli , Samuel Leder , Hans Jakob Wagner , Achim Menges

引用次数: 0

Bolt loosening angle measurement along full range of screw exposure length based on 3D point cloud 基于三维点云，在螺钉暴露长度的全范围内测量螺栓松动角度

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-09-20 DOI: 10.1016/j.autcon.2024.105785

Shengyuan Li , Yushan Le , Jiachen Gao , Xian Li , Xuefeng Zhao

引用次数: 0

Vision-based construction robot for real-time automated welding with human-robot interaction 基于视觉的建筑机器人，可实现人机交互的实时自动焊接

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-09-19 DOI: 10.1016/j.autcon.2024.105782

Doyun Lee , Kevin Han

引用次数: 0

Effectiveness of alarm sounds in preventing operator habituation to auditory warnings in construction equipment 警报声在防止操作员对建筑设备中的听觉警报习以为常方面的效果

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-09-19 DOI: 10.1016/j.autcon.2024.105784

Jeonghyeun Chae , Sungjoo Hwang , Youngcheol Kang

引用次数: 0

Automatic geometric digital twin of box girder bridge using a laser-scanned point cloud 利用激光扫描点云自动生成箱梁桥的几何数字双胞胎

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-09-19 DOI: 10.1016/j.autcon.2024.105781

Jiangpeng Shu , Ziyue Zeng , Wenhao Li , Shukang Zhou , Congguang Zhang , Caie Xu , He Zhang

{"title":"Automatic geometric digital twin of box girder bridge using a laser-scanned point cloud","authors":"Jiangpeng Shu , Ziyue Zeng , Wenhao Li , Shukang Zhou , Congguang Zhang , Caie Xu , He Zhang","doi":"10.1016/j.autcon.2024.105781","DOIUrl":"10.1016/j.autcon.2024.105781","url":null,"abstract":"<div><div>Geometric modeling is a pivotal step in creating a digital twin for existing bridge structures. Its deficiency of automation makes geometric modeling step time-consuming and laborious. This paper presents a solution for automatically modeling box girder bridges, including external and internal structures, based on laser-scanned point cloud. The solution includes three vital methods: component segmentation, key points extraction of cross-section, and internal structure reconstruction. The results indicate that the established segmentation model, BCR-Net, exhibited better performance than PointNet++ in component segmentation, as demonstrated by mIoU of 0.9751 on the test set. The mean absolute error on the dimension of the pier, bent cap and external and internal structure of the box girder is 0.27 %, 0.38 %, 0.47 %, and 0.48 %, respectively. It means the proposed methods possessed excellent modeling accuracy while ensuring high efficiency than manual modeling, providing a promising solution for digital twin modeling of bridge structures.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105781"},"PeriodicalIF":9.6,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275787","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

Voxel-based path-driven 3D concrete printing process simulation framework embedding interlayer behavior 嵌入层间行为的基于体素路径驱动的 3D 混凝土打印过程模拟框架

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-09-18 DOI: 10.1016/j.autcon.2024.105776

Baixi Chen, Xueqi Zhao, Xiaoping Qian

{"title":"Voxel-based path-driven 3D concrete printing process simulation framework embedding interlayer behavior","authors":"Baixi Chen, Xueqi Zhao, Xiaoping Qian","doi":"10.1016/j.autcon.2024.105776","DOIUrl":"10.1016/j.autcon.2024.105776","url":null,"abstract":"<div><p>This paper introduces a numerical framework to model the 3D concrete printing process, considering critical factors, particularly the print path and interlayer interactions. Within this framework, a finite element model is automatically generated for an arbitrary 3D-printed object. This is achieved by voxelizing the bounding space, incorporating a zero-thickness interlayer cohesive zone, and pinpointing the active elements. Additionally, a print path-driven element segment algorithm is developed, allowing for sequential element placement in alignment with the print path during simulation, thereby mirroring the actual printing process. The model efficacy is demonstrated through two benchmarks, focusing on elastic buckling and plastic failure, where it agrees with existing experimental and numerical data. Using this validated model, the impacts of various printing parameters, such as print width, speed, path, and interlayer behaviors are explored, and an integrated toolbox for both educational and academic purposes is created. This toolbox is available at <span><span>https://github.com/Baixi-Chen/3DCPProcessSimulaion.git</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105776"},"PeriodicalIF":9.6,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239202","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

Intelligent ergonomic optimization in bimanual worker-robot interaction: A Reinforcement Learning approach 工人与机器人双臂互动中的智能人体工学优化：强化学习方法

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-09-17 DOI: 10.1016/j.autcon.2024.105741

Mani Amani , Reza Akhavian

{"title":"Intelligent ergonomic optimization in bimanual worker-robot interaction: A Reinforcement Learning approach","authors":"Mani Amani , Reza Akhavian","doi":"10.1016/j.autcon.2024.105741","DOIUrl":"10.1016/j.autcon.2024.105741","url":null,"abstract":"<div><p>Robots have the potential to enhance safety on construction job sites by assuming hazardous tasks. While existing safety research on physical human-robot interaction (pHRI) primarily addresses collision risks, ensuring inherently safe collaborative workflows is equally important. For example, ergonomic optimization in co-manipulation is an important safety consideration in pHRI. While frameworks such as Rapid Entire Body Assessment (REBA) have been an industry standard for these interventions, their lack of a rigorous mathematical structure poses challenges for using them with optimization algorithms. Previous works have tackled this gap by developing approximations or statistical approaches that are error-prone or data-dependent. This paper presents a framework using Reinforcement Learning for precise ergonomic optimization that generalizes to different types of tasks. To ensure practicality and safe experimentations, the training leverages Inverse Kinematics in virtual reality to simulate human movement mechanics. Results of a comparison between the developed framework and ergonomically naive approaches are presented.</p></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105741"},"PeriodicalIF":9.6,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0926580524004771/pdfft?md5=2b2b2036cf1904a46046f0d067750809&pid=1-s2.0-S0926580524004771-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239124","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