Automation in Construction最新文献_第3页

Ontology for holistic building performance modeling and analysis 本体用于整体建筑性能建模和分析

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-18 DOI: 10.1016/j.autcon.2025.106197

Duygu Utkucu, Rafael Sacks

{"title":"Ontology for holistic building performance modeling and analysis","authors":"Duygu Utkucu, Rafael Sacks","doi":"10.1016/j.autcon.2025.106197","DOIUrl":"10.1016/j.autcon.2025.106197","url":null,"abstract":"<div><div>Building performance modeling and analysis using Building Information Modeling (BIM) platforms remains fragmented, requiring various software applications to address different disciplines. Challenges in data extraction, transfer, and integration arise due to inconsistencies in vendor-specific data schemas and limited interoperability. Moreover, OpenBIM data schemas lack comprehensive object definitions and semantics, compromising data integrity. While ontological frameworks have been proposed to address these issues, a unified ontology that integrates multiple performance disciplines has yet to be developed. This paper designed and developed a holistic building performance ontology (HBPO) focusing on acoustic, lighting, and energy domains as subsets to represent a range of sufficiently different domains. This ontology comprises 28 classes, 26 object properties, and 183 data properties, encapsulating essential information, data requirements, and object relationships within and across these domains. Additionally, a series of proof-of-concept experiments were conducted to test, demonstrate, validate, and evaluate the feasibility and applicability of the HBPO.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106197"},"PeriodicalIF":9.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843844","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 scour morphology identification and reconstruction using 3D sonar point cloud data 基于三维声纳点云数据的桥梁冲刷形态识别与重建

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-18 DOI: 10.1016/j.autcon.2025.106205

Zelin Huang, Yanjie Zhu, Wen Xiong, C.S. Cai

引用次数: 0

Personalized construction safety training system using conversational AI in virtual reality 基于虚拟现实对话AI的个性化施工安全培训系统

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-18 DOI: 10.1016/j.autcon.2025.106207

Aqsa Sabir , Rahat Hussain , Akeem Pedro , Chansik Park

引用次数: 0

Intelligent design and evaluation of tunnel support structure systems 隧道支护结构系统的智能化设计与评价

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-18 DOI: 10.1016/j.autcon.2025.106215

Ziquan Chen , Chuan He , Zihan Zhou , Xuefu Zhang , Yuanfu Zhou , Fenglei Han , Wei Meng

{"title":"Intelligent design and evaluation of tunnel support structure systems","authors":"Ziquan Chen , Chuan He , Zihan Zhou , Xuefu Zhang , Yuanfu Zhou , Fenglei Han , Wei Meng","doi":"10.1016/j.autcon.2025.106215","DOIUrl":"10.1016/j.autcon.2025.106215","url":null,"abstract":"<div><div>With the rapid development of artificial intelligence, intelligent algorithms for parameter nonlinear mapping provide a new design approach to address the long-term reliance on empirical design in tunnel engineering. This paper proposes an intelligent model for predicting support structure parameters based on tunnel background information. After comparing the characteristics of machine learning and deep learning algorithms applied in the intelligent design model, the generated model is validated using tunnel deformation indicators. The results show the overall accuracies of the machine learning CLS-PSO-SVM and deep learning HRNet algorithms used are 81.1 % and 88.5 %, respectively. Converting the maximum deformation as the only output indicator, the prediction accuracies of the vault and haunch deformations are 85.2 % and 82.8 %, respectively, verifying the reliability of the intelligent model. The research results can provide theoretical support for the intelligent design of tunnel engineering. Meanwhile, intelligent design models will develop towards finer prediction parameters in the future.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106215"},"PeriodicalIF":9.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843845","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

Automating motor grader leveling operations: Kinematic analysis for blade pose control 自动平地机调平作业：叶片位姿控制的运动学分析

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-17 DOI: 10.1016/j.autcon.2025.106199

Jisu Jeon , Jangho Bae , Oyoung Kwon , Yeonho Ko , Chanwoo Kim , Seonghyeon Won , Woochul Shin , Daehie Hong

引用次数: 0

Learning semantic keypoints for diverse point cloud completion 学习不同点云补全的语义关键点

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-16 DOI: 10.1016/j.autcon.2025.106192

Mingyue Dong , Ziyin Zeng , Xianwei Zheng , Jianya Gong

{"title":"Learning semantic keypoints for diverse point cloud completion","authors":"Mingyue Dong , Ziyin Zeng , Xianwei Zheng , Jianya Gong","doi":"10.1016/j.autcon.2025.106192","DOIUrl":"10.1016/j.autcon.2025.106192","url":null,"abstract":"<div><div>Raw point clouds collected from real-world scenes are sparse, incomplete and noisy, posing significant challenges for their integration into automation workflows in construction. Thus, completing plausible and fine-grained point clouds is a critical prerequisite for downstream applications. Current methods primarily focus on learning patch-level features and modeling their relationships for inferring complete object shapes. However, the significant disparity between real-world scenarios and clean synthetic datasets limits their representation ability of local structures, especially when facing noises and irregular missing patterns. This paper proposes a semantic keypoint guided completion network (SKPNet) to enhance the generalization ability of point cloud completion in diverse construction scenarios in a semantic-guided manner. The key insight is to build a connection between the object geometric structure and its global semantic feature, which is more robust to point-level disruptions. Accordingly, a semantic keypoint generation module is developed to learn representative keypoints based on the global semantic vector encoded from the input points. These keypoints then serve as the control points for searching the neighboring point-level features with rich local pattern information, simultaneously filtering out the noises during the process. By progressively incorporating multi-scale point-level features, this paper gradually refines and upsamples the keypoints to the final fine-grained completion. Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate the competitive and robust performance of SKPNet in completing high-quality shapes.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106192"},"PeriodicalIF":9.6,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838360","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

Systematic analysis of large language models for automating document-to-smart contract transformation 系统分析用于自动化文档到智能合约转换的大型语言模型

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-15 DOI: 10.1016/j.autcon.2025.106209

Erfan Moayyed , Chimay Anumba , Azita Morteza

{"title":"Systematic analysis of large language models for automating document-to-smart contract transformation","authors":"Erfan Moayyed , Chimay Anumba , Azita Morteza","doi":"10.1016/j.autcon.2025.106209","DOIUrl":"10.1016/j.autcon.2025.106209","url":null,"abstract":"<div><div>Fragmentation and poor collaboration in contract-heavy industries hinder innovation. While smart contracts offer promising automation for digital documents, the transformation process presents significant challenges. Current approaches are promising but are often constrained by technical limitations, domain-specific requirements, and limited flexibility, restricting widespread adoption. This paper systematically reviews the development of smart contracts using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to examine methodologies, challenges, and solutions through a thematic analysis of 30 key studies. The findings are grouped into three categories: Natural Language Processing (NLP)-based, template-based and ontology-based, and model-driven approaches. After analyzing the cross-industrial challenges of each category, this paper proposes a Large Language Model (LLM)-based smart contract generation solution to address the identified challenges validated through real-world use cases. This comprehensive analysis contributes to the ongoing dialogue on smart contracting, offering directions for future research and practical implementation in the digital infrastructure.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106209"},"PeriodicalIF":9.6,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829662","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

Conceptual model for aligning construction logistics capacity through simulation 基于仿真的建筑物流能力调整概念模型

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-14 DOI: 10.1016/j.autcon.2025.106190

Müge Tetik , Nicolas Brusselaers , Anna Fredriksson

引用次数: 0

Automatic inspection and assessment of a cross-passage twin tunnel using UAV 基于无人机的跨通道双隧道自动检测与评估

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-14 DOI: 10.1016/j.autcon.2025.106200

Ran Zhang , Chao Wang , Zili Li

引用次数: 0

Automated material-aware BIM generation using deep learning for comprehensive indoor element reconstruction 利用深度学习自动生成材料感知的 BIM，实现全面的室内元素重建

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-14 DOI: 10.1016/j.autcon.2025.106196

Mostafa Mahmoud , Yaxin LI , Mahmoud Adham , Wu CHEN

{"title":"Automated material-aware BIM generation using deep learning for comprehensive indoor element reconstruction","authors":"Mostafa Mahmoud , Yaxin LI , Mahmoud Adham , Wu CHEN","doi":"10.1016/j.autcon.2025.106196","DOIUrl":"10.1016/j.autcon.2025.106196","url":null,"abstract":"<div><div>Automating 3D reconstruction of indoor environments is essential for scene understanding in Building Information Modeling (BIM). This paper addresses the challenge of integrating geometric and material attributes in scan-to-BIM processes. A deep learning-based framework is developed to automatically extract and integrate geometric and material attributes from point clouds, incorporating an enhanced instance segmentation network, a material classification model, and an automated BIM integration workflow for accurate indoor modeling. The proposed framework reconstructs accurate 3D BIM models of space-forming and space-occupying elements while preserving key attributes. Experimental results show significant improvements in instance segmentation accuracy, with reconstructed 3D BIM models achieving over 98 % <span><math><mtext>correctness</mtext></math></span> and <span><math><mtext>completeness</mtext></math></span>, while the material classification model attains a point-based weighted <span><math><mi>F</mi><mn>1</mn><mo>−</mo><mtext>score</mtext></math></span> of 0.973 and an object-based accuracy of 94.70 %. These findings advance automated BIM generation, enhancing building planning, asset management, and sustainable design while inspiring further developments in scan-to-BIM automation.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106196"},"PeriodicalIF":9.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825481","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