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Intelligent construction technology for reservoir dams
IF 9.6 1区 工程技术
Automation in Construction Pub Date : 2025-04-04 DOI: 10.1016/j.autcon.2025.106177
Yang Liu , Yuannan Gan , Zhihua Yang , Sheng Qiang
{"title":"Intelligent construction technology for reservoir dams","authors":"Yang Liu ,&nbsp;Yuannan Gan ,&nbsp;Zhihua Yang ,&nbsp;Sheng Qiang","doi":"10.1016/j.autcon.2025.106177","DOIUrl":"10.1016/j.autcon.2025.106177","url":null,"abstract":"<div><div>Driven by Industry 4.0, the construction of reservoir dams is entering a new phase characterized by both opportunities and challenges. The deep integration of technologies such as artificial intelligence, big data, and digital twins aims to enhance the safety, quality, efficiency, and sustainability of dam projects. This paper utilizes VOSviewer analysis software to conduct a bibliometric analysis of intelligent construction technologies within the field of reservoir dams. The research findings reveal current trends in this field, providing an in-depth examination of global publication patterns, national contributions, and keyword co-occurrence. Finally, this paper summarizes key emerging research topics and offers insights into future research directions along with potential challenges facing the field. It provides a comprehensive review and analytical framework to support innovation and sustainable development in intelligent dam construction.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106177"},"PeriodicalIF":9.6,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767836","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
Data-driven framework for pothole repair automation using unmanned ground vehicle fleets
IF 9.6 1区 工程技术
Automation in Construction Pub Date : 2025-04-03 DOI: 10.1016/j.autcon.2025.106176
Shripal Mehta, Abiodun B. Yusuf, Sepehr Ghafari
{"title":"Data-driven framework for pothole repair automation using unmanned ground vehicle fleets","authors":"Shripal Mehta,&nbsp;Abiodun B. Yusuf,&nbsp;Sepehr Ghafari","doi":"10.1016/j.autcon.2025.106176","DOIUrl":"10.1016/j.autcon.2025.106176","url":null,"abstract":"<div><div>Traditional pavement repair techniques are time-consuming, labour-intensive, prone to errors, and expose manpower to high-risk road traffic conditions. This paper proposes a data-driven solution for planning and automating the repair process for road potholes using a fleet of unmanned ground vehicles (UGVs). The project encompasses data mining, developing software tailored for fleet management, and enhanced fault tolerance. Additionally, it incorporates the integration of digital twins for advanced simulation purposes. The methodologies involve cross-industry standard processes for data mining (CRISP-DM) and preparation combined with rapid application development (RAD). To optimise repair schedules, the system takes parameters like fleet size, payload capacity, and material requirements based on pothole dimensions. This data-driven project concludes from simulations that a neighbourhood can be patched about 40 % faster and optimised to achieve a 12.5 % reduction in robot inter-travel time using three UGVs per defined residential area of 100,000 m<sup>2</sup> instead of two UGVs in the fleet.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106176"},"PeriodicalIF":9.6,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759092","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
Scalable and transparent automated sewer defect detection using weakly supervised object localization
IF 9.6 1区 工程技术
Automation in Construction Pub Date : 2025-04-03 DOI: 10.1016/j.autcon.2025.106152
Jianyu Yin , Xianfei Yin , Mi Pan , Long Li
{"title":"Scalable and transparent automated sewer defect detection using weakly supervised object localization","authors":"Jianyu Yin ,&nbsp;Xianfei Yin ,&nbsp;Mi Pan ,&nbsp;Long Li","doi":"10.1016/j.autcon.2025.106152","DOIUrl":"10.1016/j.autcon.2025.106152","url":null,"abstract":"<div><div>Deep learning methods for sewer defect detection face challenges due to their reliance on time-consuming bounding box annotations and lack of model interpretability. This paper proposed a framework leveraging weakly supervised object localization (WSOL) that requires only image-level annotations. Analysis showed that effective performance could be achieved with minimal training data (100 images per class) and validation examples (6 images per class). The proposed approach achieved robust performance across six defect classes, with ResNet50 and VGG16 models attaining average MaxBoxAccV2 scores of 64.56 % and 57.33 %, respectively. A two-round evaluation approach was introduced, improving localization accuracy by 10.67 % using ResNet50 backbone. The practical utility of the proposed method was improved through the development of AutoSewerLabeler, a trustworthy prototype tool for automatic bounding box labeling. This paper advances sewer inspection automation by providing a more scalable and transparent framework for defect detection.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106152"},"PeriodicalIF":9.6,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759094","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
Semi-supervised method for automated detection and quantitative assessment of corrosion states in structural members
IF 9.6 1区 工程技术
Automation in Construction Pub Date : 2025-04-03 DOI: 10.1016/j.autcon.2025.106155
Yonghui An , Lingxue Kong , Chuanchuan Hou , Jinping Ou
{"title":"Semi-supervised method for automated detection and quantitative assessment of corrosion states in structural members","authors":"Yonghui An ,&nbsp;Lingxue Kong ,&nbsp;Chuanchuan Hou ,&nbsp;Jinping Ou","doi":"10.1016/j.autcon.2025.106155","DOIUrl":"10.1016/j.autcon.2025.106155","url":null,"abstract":"<div><div>Accurate detection and comprehensive assessment of corrosion states are essential for bridge safety and durability. Deep learning-based semantic segmentation methods show significant potential for corrosion detection. However, supervised methods confront substantial challenges in labor-intensive annotation and limited datasets. To address these challenges, a semi-supervised method for corrosion state segmentation (Model A) and structural member segmentation (Model B) is proposed. It adopts the weak-to-strong semi-supervised framework with SE attention and a random cut strategy, outperforming supervised methods with only 40 % labeled corrosion and 20 % labeled member images. New evaluation metrics are established to evaluate the integrated results of Model A and Model B. A smartphone-based mobile detection platform is developed to achieve automatic corrosion detection and quantitative assessments. The proposed method achieves high accuracy with limited manual annotations, offering an advanced and intelligent solution for detecting, quantifying, and managing corrosion states on bridge structural members.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106155"},"PeriodicalIF":9.6,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759093","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
Towards an integrative framework for BIM and artificial intelligence capabilities in smart architecture, engineering, construction, and operations projects 为智能建筑、工程、施工和运营项目中的 BIM 和人工智能能力制定综合框架
IF 9.6 1区 工程技术
Automation in Construction Pub Date : 2025-04-03 DOI: 10.1016/j.autcon.2025.106168
Josivan Leite Alves , Rachel Perez Palha , Adiel Teixeira de Almeida Filho
{"title":"Towards an integrative framework for BIM and artificial intelligence capabilities in smart architecture, engineering, construction, and operations projects","authors":"Josivan Leite Alves ,&nbsp;Rachel Perez Palha ,&nbsp;Adiel Teixeira de Almeida Filho","doi":"10.1016/j.autcon.2025.106168","DOIUrl":"10.1016/j.autcon.2025.106168","url":null,"abstract":"<div><div>The Architecture, Engineering, Construction, and Operations (AECO) sector gains significant advantages through generating and effectively managing BIM data. This increased available data can be fundamental in deriving innovative advances by processing them through artificial intelligence (AI) models. In this context, this paper investigates how BIM and AI capabilities can benefit the development of smart AECO projects. The research design is a systematic literature review, applying bibliometric and content analysis. First, the paper explores the relationships between the topics of AI and BIM applications, identifies seven core domains of BIM and AI finds application, explores research contributions, the problems addressed, and their primary outcomes. Second, the paper maps out 14 BIM and 16 AI capabilities fundamental to developing smart projects. Third, three propositions that sustain an integrative framework are suggested. The article also suggests that practitioners identify critical organizational capabilities to be built and strengthened.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106168"},"PeriodicalIF":9.6,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759095","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
Generative AI in architectural design: Application, data, and evaluation methods
IF 9.6 1区 工程技术
Automation in Construction Pub Date : 2025-04-03 DOI: 10.1016/j.autcon.2025.106174
Suhyung Jang, Hyunsung Roh, Ghang Lee
{"title":"Generative AI in architectural design: Application, data, and evaluation methods","authors":"Suhyung Jang,&nbsp;Hyunsung Roh,&nbsp;Ghang Lee","doi":"10.1016/j.autcon.2025.106174","DOIUrl":"10.1016/j.autcon.2025.106174","url":null,"abstract":"<div><div>This paper presents a systematic review of generative artificial intelligence (AI) use in architectural design from 2014 to 2024, focusing on 1) AI models and theory-application gaps, 2) design phases, tasks, and objectives, 3) data types and contents, and 4) evaluation methods. Based on 161 journal papers selected using preferred reporting items for systematic reviews and meta-analysis (PRISMA), the analysis reveals the theory-application gap has been reduced by 96.09 %, from 62 to 2.5 years, highlighting rapid AI adoption since 2021 with generative adversarial networks (GANs) leading, and transformers and diffusion models gaining traction. For its application, AI is employed in schematic design phases in 68.94 %, while later phases remain underexplored. Regarding types of data used, images dominate at both input (52.8 %) and output (68.32 %), with multimodal and graph data showing promise. For evaluation, comparative evaluation was most utilized (60.9 %) supported by subjective assessment by authors (34.2 %) and third parties (17.4 %).</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106174"},"PeriodicalIF":9.6,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759091","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
IFC framework for inspection and maintenance representation in facility management
IF 9.6 1区 工程技术
Automation in Construction Pub Date : 2025-04-02 DOI: 10.1016/j.autcon.2025.106157
Fernando Gussão Bellon, Ana Carolina Pereira Martins, José Maria Franco de Carvalho, Christian Alexandre Feitosa de Souza, José Carlos Lopes Ribeiro, Kléos Magalhães Lenz César Júnior, Diôgo Silva de Oliveira
{"title":"IFC framework for inspection and maintenance representation in facility management","authors":"Fernando Gussão Bellon,&nbsp;Ana Carolina Pereira Martins,&nbsp;José Maria Franco de Carvalho,&nbsp;Christian Alexandre Feitosa de Souza,&nbsp;José Carlos Lopes Ribeiro,&nbsp;Kléos Magalhães Lenz César Júnior,&nbsp;Diôgo Silva de Oliveira","doi":"10.1016/j.autcon.2025.106157","DOIUrl":"10.1016/j.autcon.2025.106157","url":null,"abstract":"<div><div>Effectively managing inspection and maintenance data in facility management remains challenging due to the lack of structured and interoperable data representation. This paper explores how the IFC schema can be leveraged to standardize inspection, damage, maintenance, and maintenance cost data representation. To this end, an IFC-based framework was developed to ensure semantic consistency and interoperability in the representation of inspection and maintenance data. The framework was validated through IFC schema verification and semantic evaluation of generated case studies across multiple BIM software, demonstrating its applicability for facility management, maintenance planning, and asset monitoring of buildings, industrial plants, and infrastructure projects. By enabling and standardizing structured information exchange, the proposed framework enhances decision-making in facility management workflows. Future work should focus on extending its application in real-world scenarios, specifically through integration with facility management systems and automated data acquisition technologies.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106157"},"PeriodicalIF":9.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746565","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
Dynamic optimization of maintenance strategy for bridges in regional transportation network through semi-Markov processes
IF 9.6 1区 工程技术
Automation in Construction Pub Date : 2025-04-02 DOI: 10.1016/j.autcon.2025.106175
Xiaoling Liu , Ying Liu , Zhe Sun , Bing Wang , Yinfei Zhao
{"title":"Dynamic optimization of maintenance strategy for bridges in regional transportation network through semi-Markov processes","authors":"Xiaoling Liu ,&nbsp;Ying Liu ,&nbsp;Zhe Sun ,&nbsp;Bing Wang ,&nbsp;Yinfei Zhao","doi":"10.1016/j.autcon.2025.106175","DOIUrl":"10.1016/j.autcon.2025.106175","url":null,"abstract":"<div><div>Aging bridges within regional transportation networks often suffer from structural deficiencies, requiring effective maintenance strategies under budget constraints. This paper aims to determine optimal maintenance strategies at the system level using a semi-Markov process-based dynamic optimization approach. The proposed method models bridge condition evolution with a semi-Markov matrix and integrates a probabilistic deterioration model to simulate structural aging. An optimization model is formulated to identify the best maintenance strategies considering budget limitations. Case studies on a Chinese regional transportation network demonstrate that the method can maintain 76.45 % of bridges in level A condition with only four interventions over ten years. These findings provide a practical decision-aid tool for transportation agencies to enhance bridge network sustainability. Future research can extend this approach by incorporating real-time monitoring data for adaptive maintenance planning.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106175"},"PeriodicalIF":9.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746566","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
Vision-based damage identification for beam-type structures using area scanning without calibration
IF 9.6 1区 工程技术
Automation in Construction Pub Date : 2025-04-01 DOI: 10.1016/j.autcon.2025.106156
Panjie Li , Shuaihui Yan , Menghao Hu , Can Cui , Jinke Li , Yuyang Pang
{"title":"Vision-based damage identification for beam-type structures using area scanning without calibration","authors":"Panjie Li ,&nbsp;Shuaihui Yan ,&nbsp;Menghao Hu ,&nbsp;Can Cui ,&nbsp;Jinke Li ,&nbsp;Yuyang Pang","doi":"10.1016/j.autcon.2025.106156","DOIUrl":"10.1016/j.autcon.2025.106156","url":null,"abstract":"<div><div>The conversion of image coordinates to physical coordinates, usually through camera calibration, is necessary to obtain the accurate displacement when using the vision-based measurement system. This paper proposes a vision-based damage identification for beam-type structures using area scanning without calibration. First, the modal parameter identification using the uncalibrated pixel displacement is derived to verify the feasibility of omitting the calibration process. Then, a flexible area damage detection is proposed through area scanning tactic, which focus on the desired detection area without the consideration of the overlapping region measurement and the baseline mode shape data. Finally, an experiment validation involving two scanning strategies is performed to verify and demonstrate the effectiveness and accessibility of the proposed method. The results indicate that the fluctuation of mode shape curvature (MSC) caused by damage needs to be greater than that of MSC itself to make the damage identification successful.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106156"},"PeriodicalIF":9.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746564","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
Generative adversarial network for real-time identification and pixel-level annotation of highway pavement distresses
IF 9.6 1区 工程技术
Automation in Construction Pub Date : 2025-04-01 DOI: 10.1016/j.autcon.2025.106122
Mark Amo-Boateng, Yaw Adu-Gyamfi
{"title":"Generative adversarial network for real-time identification and pixel-level annotation of highway pavement distresses","authors":"Mark Amo-Boateng,&nbsp;Yaw Adu-Gyamfi","doi":"10.1016/j.autcon.2025.106122","DOIUrl":"10.1016/j.autcon.2025.106122","url":null,"abstract":"<div><div>Efficient analysis of road pavement distresses is crucial for infrastructure management and safety. Traditional methods are labor-intensive, and recent deep-learning approaches face challenges such as overlapping bounding boxes and poor pixel localization. This paper presents PaveGAN, a real-time method to identify and annotate pavement distresses using generative adversarial networks. While trained on several loss functions, PaveGAN achieved its best mean absolute percentage errors of 1.49%, 1.15%, and 3.26% for the mean squared error, Huber loss, and structured similarity index, respectively, at the pixel level. With intersection over union scores exceeding 90% for several distresses, and operating at 89.15 frames per second, PaveGAN is suitable for real-time applications. Independent evaluations show its annotation accuracy aligns closely with human experts, achieving 84% accuracy versus 86% for manual annotations. By automating pixel-level annotation in widely used formats such as COCO and LabelMe, PaveGAN provides a scalable, cost-effective solution for pavement distress monitoring.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106122"},"PeriodicalIF":9.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746563","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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