Automation in Construction最新文献_第4页

Virtual audit of microscale environmental components and materials using streetscape images with panoptic segmentation and image classification

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

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

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

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

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

Additive manufacturing in the construction industry

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-11-28 DOI: 10.1016/j.autcon.2024.105888

Eric Forcael , Moisés Medina , Alexander Opazo-Vega , Francisco Moreno , Gonzalo Pincheira

{"title":"Additive manufacturing in the construction industry","authors":"Eric Forcael , Moisés Medina , Alexander Opazo-Vega , Francisco Moreno , Gonzalo Pincheira","doi":"10.1016/j.autcon.2024.105888","DOIUrl":"10.1016/j.autcon.2024.105888","url":null,"abstract":"<div><div>New sustainable technologies, such as additive manufacturing (AM), have recently been adopted in the construction industry, significantly reducing construction completion times and effectively repairing and remanufacturing components. While AM's implementation in construction is still diffuse, this review contributes to a better understanding of how this technology is interpreted and utilized, exploring the main trends in AM within the construction industry. The systematic review of recent publications identified eight critical topics for AM, whose processes are characterized by their materials: aggregate-based materials, metals, and polymers. The findings reveal an increase in the published works, with the United States, Germany, and China rising as the prominent contributors, where AM technologies are mainly defined by processes such as Material extrusion, Particle bed, Powder bed fusion, Directed energy deposition and Vat photopolymerization. Finally, other challenges regarding AM in the construction industry, such as its role in large-scale construction and prefabrication, are also discussed.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"169 ","pages":"Article 105888"},"PeriodicalIF":9.6,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746235","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

Meta-interaction: Deployable framework integrating the metaverse and generative AI for participatory building design 元交互：整合元宇宙和生成式人工智能的可部署框架，用于参与式建筑设计

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-11-27 DOI: 10.1016/j.autcon.2024.105893

Hongda An, Weisheng Lu, Liupengfei Wu, Ziyu Peng, Jinfeng Lou

引用次数: 0

Real-time safety and worker self-assessment: Sensor-based mobile system for critical unsafe behaviors 实时安全和工人自我评估：针对关键不安全行为的传感器移动系统

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-11-23 DOI: 10.1016/j.autcon.2024.105879

Hanjing Zhu, Bon-Gang Hwang

{"title":"Real-time safety and worker self-assessment: Sensor-based mobile system for critical unsafe behaviors","authors":"Hanjing Zhu, Bon-Gang Hwang","doi":"10.1016/j.autcon.2024.105879","DOIUrl":"10.1016/j.autcon.2024.105879","url":null,"abstract":"<div><div>Human error significantly contributes to construction accidents, exacerbated by the lack of a digital tool for assessing and improving workers' safety performance. This paper addresses this gap by developing: 1) a Real-Time Safety Performance Assessment and Report System; 2) a Safety Behavior Self-Assessment and Improvement System; and 3) a Sensor-Based Safety Performance Analytic Mobile System (SBSPAMS) to detect, assess, and modify workers' unsafe behaviors. Through a comprehensive literature review, this paper developed a real-time safety performance assessment method, a safety behavior self-assessment questionnaire, and tailored recommendations for workers' safety performance improvement. The SBSPAMS was developed by integrating the safety performance assessment and safety behavior self-assessment functions into a Sensor-Based Safety Monitoring Mobile System (SBSMMS), along with a framework for the practical implementation of the SBSPAMS. This paper enriches construction safety knowledge by providing a performance assessment and improvement approach and contributes to the construction industry by developing a tool for worker-oriented safety management.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"169 ","pages":"Article 105879"},"PeriodicalIF":9.6,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698960","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

Optimizing printing and rheological parameters for 3D printing with cementitious materials 优化水泥基材料 3D 打印的打印和流变参数

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-11-23 DOI: 10.1016/j.autcon.2024.105881

Qingwei Wang , Song Han , Junhao Yang , Ziang Li , Mingzhe An

引用次数: 0

Impact of selective environmental sound attenuation on operator performance, stress, attention, and task engagement in teleoperated demolition 选择性环境声音衰减对遥控爆破中操作员的表现、压力、注意力和任务参与度的影响

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-11-23 DOI: 10.1016/j.autcon.2024.105876

Patrick Borges Rodrigues , Burcin Becerik-Gerber , Lucio Soibelman , Gale M. Lucas , Shawn C. Roll

{"title":"Impact of selective environmental sound attenuation on operator performance, stress, attention, and task engagement in teleoperated demolition","authors":"Patrick Borges Rodrigues , Burcin Becerik-Gerber , Lucio Soibelman , Gale M. Lucas , Shawn C. Roll","doi":"10.1016/j.autcon.2024.105876","DOIUrl":"10.1016/j.autcon.2024.105876","url":null,"abstract":"<div><div>The noise produced in demolition sites can mask safety-critical sounds that inform operators about task conditions and hazards. These problems are exacerbated in teleoperated demolition, where the separation between operator and site compromises operators' situation awareness and cognitive loads. This paper assessed the effects of environmental sounds with and without attenuation on the operators' performance and response (e.g., stress, attention, task engagement) during teleoperated demolition. Eighty participants completed three virtual demolition tasks under different environmental sound conditions, i.e., no sound (NS), unfiltered sound (US), and filtered sound (FS) with 20-dB attenuation of background noise and robot's sounds to allow focus on safety and task conditions. The results show that US induced more stress than NS and FS. Also, FS resulted in fewer collisions, faster reaction times, and greater attention and task engagement than US. These results can support the design of sound feedback interfaces for teleoperation in construction.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"169 ","pages":"Article 105876"},"PeriodicalIF":9.6,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698959","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