Automation in Construction最新文献_第5页

Associative reasoning for engineering drawings using an interactive attention mechanism 基于交互注意机制的工程图关联推理

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2024.105942

Xu Xuesong , Xiao Gang , Sun Li , Zhang Xia , Wu Peixi , Zhang Yuanming , Cheng Zhenbo

{"title":"Associative reasoning for engineering drawings using an interactive attention mechanism","authors":"Xu Xuesong , Xiao Gang , Sun Li , Zhang Xia , Wu Peixi , Zhang Yuanming , Cheng Zhenbo","doi":"10.1016/j.autcon.2024.105942","DOIUrl":"10.1016/j.autcon.2024.105942","url":null,"abstract":"<div><div>In infrastructure construction, engineering drawings combine graphic and textual information, with text playing a critical role in retrieving and measuring the similarity of these drawings in practical applications. However, existing research primarily focuses on graphics, neglecting the extraction and semantic representation of text. Existing Optical Character Recognition (OCR)-based methods face challenges in clustering text into coherent semantic modules, frequently dispersing related text across different regions. Therefore, this paper proposes a deep learning framework for the semantic extraction of text from engineering drawings. By integrating textual, positional, and image features, this framework enables semantic extraction and represents engineering drawings as knowledge graphs. An interactive attention-based approach is employed for associative retrieval of engineering drawings via subgraph matching. Evaluation on datasets from a transportation design institute and public sources demonstrates the framework's effectiveness in both semantic extraction and relational reasoning.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"170 ","pages":"Article 105942"},"PeriodicalIF":9.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918043","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

AI-driven computer vision-based automated repair activity identification for seismically damaged RC columns 基于ai驱动计算机视觉的地震损伤RC柱自动修复活动识别

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2024.105959

Samira Azhari, Sara Jamshidian, Mohammadjavad Hamidia

{"title":"AI-driven computer vision-based automated repair activity identification for seismically damaged RC columns","authors":"Samira Azhari, Sara Jamshidian, Mohammadjavad Hamidia","doi":"10.1016/j.autcon.2024.105959","DOIUrl":"10.1016/j.autcon.2024.105959","url":null,"abstract":"<div><div>Manual visual inspection is the conventional method for post-earthquake damage assessment, which is unsafe, subjective, and prone to human error. This paper presents an automated rapid and non-contact seismic damage state prediction methodology for reinforced concrete columns using crack image analysis. For surface damage quantification, three features of crack texture complexity including percolation, heterogeneity, and Renyi entropy-based dimensions are measured. Various shallow- and deep-learning-rooted algorithms are trained using a large collected experimental database to develop FEMA P-58-compliant repair activity predictive models. Based on the structural parameters, geometric features, and image-extracted indices, 10 groups of input features are defined. For the overfitting assessment and generalizability evaluation of models, five-fold cross-validations are conducted. Among shallow learning-based algorithms, CatBoost algorithm performs best for the scenarios that rely on vision-derived intricacy indices. Using the deep learning-based multilayer perceptron model as a feedforward artificial neural network, 92 % accuracy is achieved for the testing dataset.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"170 ","pages":"Article 105959"},"PeriodicalIF":9.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939667","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

Loss function inversion for improved crack segmentation in steel bridges using a CNN framework

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2024.105896

Andrii Kompanets , Remco Duits , Gautam Pai , Davide Leonetti , H.H. (Bert) Snijder

引用次数: 0

Ensemble learning framework for forecasting construction costs 预测建筑成本的集成学习框架

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2024.105903

Omar Habib , Mona Abouhamad , AbdElMoniem Bayoumi

引用次数: 0

Automatic crack defect detection via multiscale feature aggregation and adaptive fusion 基于多尺度特征聚合和自适应融合的裂纹缺陷自动检测

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2024.105934

Hanyun Huang , Mingyang Ma , Suli Bai, Lei Yang, Yanhong Liu

{"title":"Automatic crack defect detection via multiscale feature aggregation and adaptive fusion","authors":"Hanyun Huang , Mingyang Ma , Suli Bai, Lei Yang, Yanhong Liu","doi":"10.1016/j.autcon.2024.105934","DOIUrl":"10.1016/j.autcon.2024.105934","url":null,"abstract":"<div><div>In this paper, a multi-scale feature aggregation and adaptive fusion network, is proposed for automatic and accurate pavement crack defect segmentation. Specifically, faced with the linear characteristic of pavement crack defects, a multiple-dimension attention (MDA) module is proposed to effectively capture long-range correlation from three directions, including space, width and height, and help identify the pavement crack defect boundaries. On this basis, a multi-scale skip connection (MSK) module is proposed, which can effectively utilize the feature information from multiple receptive fields to support accurate feature reconstruction in the decoding stage. Furthermore, a multi-scale attention fusion (MSAF) module is proposed to realize effective multi-scale feature representation and aggregation. Finally, an adaptive weight fusion (AWL) module is proposed to dynamically fuse the output features across different network layers for accurate multi-scale crack defect segmentation. Experiments indicate that proposed network is superior to other mainstream segmentation networks on pixelwise crack defect detection task.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"170 ","pages":"Article 105934"},"PeriodicalIF":9.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142887890","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

Graph neural networks for classification and error detection in 2D architectural detail drawings 基于图神经网络的二维建筑详图分类与错误检测

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2024.105936

Jaechang Ko , Donghyuk Lee

引用次数: 0

Egocentric-video-based construction quality supervision (EgoConQS): Application of automatic key activity queries 基于自我中心视频的建筑质量监督（EgoConQS）：关键活动自动查询的应用

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2024.105933

Jingjing Guo , Lu Deng , Pengkun Liu , Tao Sun

引用次数: 0

Enabling scalable Model Predictive Control design for building HVAC systems using semantic data modelling 使用语义数据建模为构建HVAC系统启用可扩展模型预测控制设计

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2024.105929

Lu Wan , Ferdinand Rossa , Torsten Welfonder , Ekaterina Petrova , Pieter Pauwels

{"title":"Enabling scalable Model Predictive Control design for building HVAC systems using semantic data modelling","authors":"Lu Wan , Ferdinand Rossa , Torsten Welfonder , Ekaterina Petrova , Pieter Pauwels","doi":"10.1016/j.autcon.2024.105929","DOIUrl":"10.1016/j.autcon.2024.105929","url":null,"abstract":"<div><div>Model Predictive Control (MPC) is a promising optimal control technique to reduce the energy consumption of Heating, Ventilation, and Air Conditioning systems in buildings. However, MPC currently involves significant manual efforts in data preparation, control model design, and software interface design. Better semantic representations of buildings, their systems, and telemetry data could help address these challenges. This paper proposes a standard semantic information model and tooling, tailored to BIM software, to streamline MPC design. The approach is tested in an office building, and the generated semantic graph is validated against a use case, where an MPC controller uses Resistance and Capacitance (RC) models that need to be parameterized. The results show that the automatically identified RC models achieve three-hour-ahead temperature predictions for two different rooms within 0.3 °C accuracy. This indicates that semantic data modelling can enable a scalable MPC configuration workflow and more efficient algorithm development and deployment in the future.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"170 ","pages":"Article 105929"},"PeriodicalIF":9.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918045","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 large-scale additive manufacturing of structural formwork with rapid fibre-reinforced polymer tape lamination

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2025.105978

Zhuo-Yang Xin , Guan-Qi Zhu , Joseph M. Gattas , Dan Luo

{"title":"Automated large-scale additive manufacturing of structural formwork with rapid fibre-reinforced polymer tape lamination","authors":"Zhuo-Yang Xin , Guan-Qi Zhu , Joseph M. Gattas , Dan Luo","doi":"10.1016/j.autcon.2025.105978","DOIUrl":"10.1016/j.autcon.2025.105978","url":null,"abstract":"<div><div>A fully automated additive lamination manufacturing (ALM) system was developed for producing large-scale fibre-reinforced polymer (FRP) structural formworks. Automation is achieved through the integration of a UV-curable resin matrix with glass fibre tape and a robotic arm end-effector. This paper presents details of the hardware and material development for the UV-based ALM system, as well as performance benchmarks evaluated for two sets of prototypes. The first set compared the geometrical quality and fabrication efficiency of the automated ALM system to the existing semi-automated ALM system, demonstrating improved part quality and faster build speeds. The second set studied the impact of the layer height process parameter on fabrication performance. The design domain for the automatic ALM system was discussed and its fabrication productivity measures were compared with industrial standards and other FRP additive manufacturing approaches. Results highlight the ALM system’s potential for cost-effective, bespoke, and large-scale FRP component construction.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"171 ","pages":"Article 105978"},"PeriodicalIF":9.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143099107","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

Enhanced vision-based 6-DoF pose estimation for robotic rebar tying

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2025.105999

Mi Liu , Jingjing Guo , Lu Deng , Songyue Wang , Huiguang Wang

引用次数: 0