Automation in Construction最新文献_第7页

Self-training method for structural crack detection using image blending-based domain mixing and mutual learning 基于图像混合的区域混合和相互学习的结构裂纹检测自训练方法

IF 10.3 1区工程技术

Automation in Construction Pub Date : 2024-12-05 DOI: 10.1016/j.autcon.2024.105892

Quang Du Nguyen, Huu-Tai Thai, Son Dong Nguyen

{"title":"Self-training method for structural crack detection using image blending-based domain mixing and mutual learning","authors":"Quang Du Nguyen, Huu-Tai Thai, Son Dong Nguyen","doi":"10.1016/j.autcon.2024.105892","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105892","url":null,"abstract":"Deep learning-based structural crack detection utilizing fully supervised methods requires laborious labeling of training data. Moreover, models trained on one dataset often experience significant performance drops when applied to others due to domain shifts prompted by diverse structures, materials, and environmental conditions. This paper addresses the issues by introducing a robust self-training domain adaptive segmentation (STDASeg) pipeline. STDASeg incorporates an image blending-based domain mixing module to minimize domain discrepancies. Additionally, STDASeg involves a two-stage self-training framework characterized by the mutual learning scheme between Convolutional Neural Networks and Transformers, effectively learning domain invariant features from the two domains. Comprehensive evaluations across three challenging cross-dataset crack detection scenarios highlight the superiority of STDASeg over traditional supervised training approaches and current state-of-the-art methods. These results confirm the stability of STDASeg, thus supporting more efficient infrastructure assessments.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"34 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788866","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

Bridge defect detection using small sample data with deep learning and Hyperspectral imaging 利用深度学习和高光谱成像的小样本数据进行桥梁缺陷检测

IF 10.3 1区工程技术

Automation in Construction Pub Date : 2024-12-05 DOI: 10.1016/j.autcon.2024.105900

Xiong Peng, Pengtao Wang, Kun Zhou, Zhipeng Yan, Xingu Zhong, Chao Zhao

引用次数: 0

Augmented reality in U.S. Construction: Trends and future directions 增强现实在美国建筑：趋势和未来方向

IF 10.3 1区工程技术

Automation in Construction Pub Date : 2024-12-05 DOI: 10.1016/j.autcon.2024.105895

James O. Toyin, Anoop Sattineni, Eric M. Wetzel, Ayodele A. Fasoyinu, Jeff Kim

引用次数: 0

Automated legal consulting in construction procurement using metaheuristically optimized large language models 基于元启发式优化大型语言模型的建筑采购自动化法律咨询

IF 10.3 1区工程技术

Automation in Construction Pub Date : 2024-12-04 DOI: 10.1016/j.autcon.2024.105891

Chi-Yun Liu, Jui-Sheng Chou

引用次数: 0

Plug-and-play method for segmenting concrete bridge cracks using the segment anything model with a fractal dimension matrix prompt 利用分形维数矩阵提示的任意分段模型进行混凝土桥梁裂缝分段的即插即用方法

IF 10.3 1区工程技术

Automation in Construction Pub Date : 2024-12-04 DOI: 10.1016/j.autcon.2024.105906

Shuai Teng, Airong Liu, Zuxiang Situ, Bingcong Chen, Zhihua Wu, Yixiao Zhang, Jialin Wang

引用次数: 0

Virtual audit of microscale environmental components and materials using streetscape images with panoptic segmentation and image classification 利用全景分割和图像分类的街景图像对微尺度环境成分和材料进行虚拟审计

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-12-02 DOI: 10.1016/j.autcon.2024.105885

Meesung Lee , Hyunsoo Kim , Sungjoo Hwang

{"title":"Virtual audit of microscale environmental components and materials using streetscape images with panoptic segmentation and image classification","authors":"Meesung Lee , Hyunsoo Kim , Sungjoo Hwang","doi":"10.1016/j.autcon.2024.105885","DOIUrl":"10.1016/j.autcon.2024.105885","url":null,"abstract":"<div><div>Microscale environmental components, such as street furniture, sidewalks, and green spaces, significantly enhance street quality when properly identified and managed. Traditional in-person audits are time-consuming, so virtual audits using streetscape images and computer vision have been explored as alternatives. However, these often lack a comprehensive range of microscale components and do not consider attributes like materials. This paper proposes an automatic virtual audit method that recognizes microscale component types and materials in streetscape images using panoptic segmentation and material classification of segmented images of detected components. By surveying components affecting pedestrian-perceived street quality to include as many essential components as possible, 33 types of microscale components, as well as materials of sidewalk pavement, architectural elements, and street furniture, were identified with an overall F1 score of 0.946, demonstrating significantly improved performance compared with previous studies. This approach helps enhance street quality by evaluating built environments through an automatic virtual audit.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"170 ","pages":"Article 105885"},"PeriodicalIF":9.6,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759188","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

Damage detection in concrete structures with multi-feature backgrounds using the YOLO network family 基于YOLO网络族的多特征背景下混凝土结构损伤检测

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-12-01 DOI: 10.1016/j.autcon.2024.105887

Rakesh Raushan , Vaibhav Singhal , Rajib Kumar Jha

引用次数: 0

High-precision 3D reconstruction of underwater concrete using integrated line structured light and stereo vision 基于线结构光和立体视觉的水下混凝土高精度三维重建

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-11-30 DOI: 10.1016/j.autcon.2024.105883

Haitao Lin , Hua Zhang , Jianwen Huo , Jialong Li , Huan Zhang , Yonglong Li

引用次数: 0

Dual-encoder network for pavement concrete crack segmentation with multi-stage supervision 基于双编码器网络的路面混凝土裂缝分段多级监控

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-11-29 DOI: 10.1016/j.autcon.2024.105884

Jing Wang , Haizhou Yao , Jinbin Hu , Yafei Ma , Jin Wang

{"title":"Dual-encoder network for pavement concrete crack segmentation with multi-stage supervision","authors":"Jing Wang , Haizhou Yao , Jinbin Hu , Yafei Ma , Jin Wang","doi":"10.1016/j.autcon.2024.105884","DOIUrl":"10.1016/j.autcon.2024.105884","url":null,"abstract":"<div><div>Cracks are a prevalent disease on pavement concrete materials. Timely assessment and repair of concrete materials can significantly extend their service life. However, accurate segmentation has always been difficult due to their random distribution, tortuous geometry, and varying degrees of severity. To address these challenges, a Multi-stage Supervised Dual-encoder network for Crack segmentation on pavement concrete (MSDCrack) was proposed based on an encoder–decoder architecture. In this network, attention collapse is mitigated through the addition of self-attention pooling. Furthermore, a feature fusion module was designed to address differences in encoding characteristics across branches. Additionally, a multi-stage supervision strategy was implemented to enhance the network’s predictive performance. Comparative experiments demonstrated that MSDCrack achieved the highest Dice coefficient, F1-score, and IoU on multiple datasets, with F1-score and IoU surpassing other state-of-the-art segmentation networks by over 3.1% and 2.89%, respectively, in generalization performance.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"169 ","pages":"Article 105884"},"PeriodicalIF":9.6,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746237","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

Deep learning for safety risk management in modular construction: Status, strengths, challenges, and future directions 模块化建筑中安全风险管理的深度学习：现状、优势、挑战和未来方向

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-11-28 DOI: 10.1016/j.autcon.2024.105894

Yin Junjia Ph.D., Aidi Hizami Alias Ph.D., Nuzul Azam Haron Ph.D., Nabilah Abu Bakar Ph.D.

{"title":"Deep learning for safety risk management in modular construction: Status, strengths, challenges, and future directions","authors":"Yin Junjia Ph.D., Aidi Hizami Alias Ph.D., Nuzul Azam Haron Ph.D., Nabilah Abu Bakar Ph.D.","doi":"10.1016/j.autcon.2024.105894","DOIUrl":"10.1016/j.autcon.2024.105894","url":null,"abstract":"<div><div>Occupational health risks such as falls from height, electrocution, object strikes, mechanical injuries, and collapses have plagued the construction industry. Deep learning algorithms are exploding due to their outstanding analytical capabilities and are believed to improve safety management significantly. Therefore, this paper systematically reviewed the literature on DL algorithms from 2015 to 2024 in modular construction. It found that the six most popular DL algorithms in this area are “Convolutional Neural Network (CNN),” “Recurrent Neural Network (RNN),” “Generative Adversarial Network (GAN),” “Auto-Encoder (AE),” “Deep Belief Network (DBN)” and “Transformer.” However, in addition to each algorithm's limitations, problems like data constraints, talent gaps, and a lack of guidance frameworks also exist. To address these issues, three strategies are proposed. They are “establishing a multi-modal data sharing platform,” “proposing a paradigm framework for the application of DL algorithms,” and “constructing a compound construction talent training mechanism,” which provide researchers with future references.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"169 ","pages":"Article 105894"},"PeriodicalIF":9.6,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746236","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