Automation in Construction最新文献_第6页

Dynamic optimization of maintenance strategy for bridges in regional transportation network through semi-Markov processes 基于半马尔可夫过程的区域交通网络桥梁养护策略动态优化

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

引用次数: 0

Vision-based damage identification for beam-type structures using area scanning without calibration 基于视觉的无标定区域扫描梁式结构损伤识别

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

引用次数: 0

Generative adversarial network for real-time identification and pixel-level annotation of highway pavement distresses 基于生成对抗网络的公路路面损伤实时识别与像素级标注

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, 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

Asphalt concrete density monitoring during compaction using roller-mounted GPR 用滚轮式探地雷达监测压实过程中沥青混凝土的密度

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-01 DOI: 10.1016/j.autcon.2025.106158

Lama Abufares, Yihan Chen, Imad L. Al-Qadi

引用次数: 0

Lifecycle framework for AI-driven parametric generative design in industrialized construction 工业化建筑中人工智能驱动参数生成设计的生命周期框架

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-31 DOI: 10.1016/j.autcon.2025.106146

Maggie Y. Gao , Chao Li , Frank Petzold , Robert L.K. Tiong , Yaowen Yang

{"title":"Lifecycle framework for AI-driven parametric generative design in industrialized construction","authors":"Maggie Y. Gao , Chao Li , Frank Petzold , Robert L.K. Tiong , Yaowen Yang","doi":"10.1016/j.autcon.2025.106146","DOIUrl":"10.1016/j.autcon.2025.106146","url":null,"abstract":"<div><div>In the Architecture, Engineering, and Construction (AEC) industry, design processes remain fragmented across architectural, structural, and mechanical domains, limiting integration and optimization opportunities throughout building lifecycles. This paper investigates how artificial intelligence can be leveraged to create a comprehensive framework for parametric generative design in industrialized construction that integrates multiple design disciplines and optimization criteria. The methodology employs knowledge graph question answering (KGQA) enabled by large language models (LLMs) to acquire design requirements and constraints, implements multi-objective optimization algorithms to balance competing criteria, and establishes a three-tier priority hierarchy to resolve conflicts in cross-domain design processes. The framework demonstrates significant improvements in a real-world case study, achieving 15.8 % reduction in lifecycle costs, 21.2 % decrease in energy consumption, and significantly reducing preliminary design modelling time. These findings provide valuable insights for AEC practitioners seeking to implement human-AI collaborative design workflows and illustrate pathways for integrating domain-specific knowledge with advanced AI systems.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106146"},"PeriodicalIF":9.6,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746560","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

Parametric modeling and evolutionary method for predictive maintenance of marine reinforced concrete structures 海洋钢筋混凝土结构预测维修的参数化建模与演化方法

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-29 DOI: 10.1016/j.autcon.2025.106154

Ren-jie Wu , Jin-quan Wang , Jin Xia

{"title":"Parametric modeling and evolutionary method for predictive maintenance of marine reinforced concrete structures","authors":"Ren-jie Wu , Jin-quan Wang , Jin Xia","doi":"10.1016/j.autcon.2025.106154","DOIUrl":"10.1016/j.autcon.2025.106154","url":null,"abstract":"<div><div>The absence of an efficient maintenance method has incurred substantial additional costs, emerging as the primary impediment to the advancement of marine reinforced concrete (RC) structures. This paper proposes a parametric modeling and evolutionary optimization method to improve the cost-effectiveness ratio of structural maintenance. The deterioration risk distribution of the entire structural system is established through parametric modeling. An evolutionary optimization method grounded in genetic algorithm (GA) is utilized to determine the optimal maintenance sizes, followed by the space-time-dependent survival probability route (STSPR) method to refine the maintenance times for each specific maintenance size. The Hangzhou Bay cross-sea Bridge in China is used to illustrate the practicality of the proposed method. The results indicate a cost-effectiveness ratio reduction of 63.3 %, 58.1 %, and 3.1 % and a lifetime extension of 9.1 %, 24.7 %, and 1.7 % for bridge piers, bridge wet joints, and bridge caps, respectively, compared to the sequential failure limit method.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106154"},"PeriodicalIF":9.6,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725242","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

Named entity recognition for construction documents based on fine-tuning of large language models with low-quality datasets 基于低质量数据集的大型语言模型微调的建筑文档命名实体识别

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-28 DOI: 10.1016/j.autcon.2025.106151

Junyu Zhou, Zhiliang Ma

{"title":"Named entity recognition for construction documents based on fine-tuning of large language models with low-quality datasets","authors":"Junyu Zhou, Zhiliang Ma","doi":"10.1016/j.autcon.2025.106151","DOIUrl":"10.1016/j.autcon.2025.106151","url":null,"abstract":"<div><div>Named Entity Recognition (NER) is a fundamental task for automatically processing and reusing documents. In traditional methods, machine learning has been used relying on costly high-quality datasets. This paper proposed an NER method based on fine-tuning Large Language Models (LLMs) with low-quality datasets for construction documents. Firstly, low-quality datasets were semi-automatically generated from national standards, qualification textbooks, and lexicons, including datasets of generation-type, tagging-type and question-answering type. Then, they were used to fine-tune an LLM for NER of structural elements to obtain optimal parametric fine-tuning conditions. Next, the results of optimally fine-tuned LLM were used to iterate the low-quality dataset to improve the performance. The F1 finally reached 0.756. Similar results were obtained on two other types of named entities, illustrating the generalizability. This paper provided a more effective and efficient method for the construction documents reuse. Future research should explore how to achieve better results by using other methods.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106151"},"PeriodicalIF":9.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725241","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

BIM, IoT, and GIS integration in construction resource monitoring BIM、IoT、GIS在建筑资源监控中的融合

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-28 DOI: 10.1016/j.autcon.2025.106149

Xiang Liu , Maxwell Fordjour Antwi-Afari , Jue Li , Yongcheng Zhang , Patrick Manu

{"title":"BIM, IoT, and GIS integration in construction resource monitoring","authors":"Xiang Liu , Maxwell Fordjour Antwi-Afari , Jue Li , Yongcheng Zhang , Patrick Manu","doi":"10.1016/j.autcon.2025.106149","DOIUrl":"10.1016/j.autcon.2025.106149","url":null,"abstract":"<div><div>In recent years, the advancement of digital technologies such as building information modeling (BIM), internet of things (IoT), and geographic information system (GIS) has had many impacts on the construction industry. However, limited research has been conducted on the integration of BIM, IoT, and GIS technologies, especially in construction resource monitoring. Therefore, this paper presents a state-of-the-art review of BIM, IoT, and GIS integration by focusing on their applications, challenges, research gaps, and future research directions. A systematic literature review and science mapping analysis were adopted in this study. The results identified the gaps in BIM, IoT, and GIS integration in construction resource monitoring, which include interoperability, data security, real-time dynamic monitoring, complex environmental data processing, environmental sustainability studies, prediction models, and convenience for the users. Moreover, challenges and future research directions were proposed. This paper contributes to extending the integrated applications of digital technologies in construction resource monitoring.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106149"},"PeriodicalIF":9.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725243","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

Feature weights in contractor safety performance assessment: Comparative study of expert-driven and analytics-based approaches 承包商安全绩效评估中的特征权重：专家驱动和基于分析的方法的比较研究

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-28 DOI: 10.1016/j.autcon.2025.106142

Say Hong Kam, Tianxiang Lan, Kailai Sun, Yang Miang Goh

{"title":"Feature weights in contractor safety performance assessment: Comparative study of expert-driven and analytics-based approaches","authors":"Say Hong Kam, Tianxiang Lan, Kailai Sun, Yang Miang Goh","doi":"10.1016/j.autcon.2025.106142","DOIUrl":"10.1016/j.autcon.2025.106142","url":null,"abstract":"<div><div>Current expert-based approaches to determining the weights of different safety management elements during contractor safety performance are time-consuming and potentially biased.Hence, this paper evaluates analytics-based approaches, i.e., supervised learning, cluster-then-predict and two-level variable weighting K-Means (TWKM) (an extension of the traditional K-Means clustering algorithm), against the Delphi method. In collaboration with an infrastructure developer, a dataset of 461 data points and 12 features describing subcontractors' inherent risks and safety assurance performance were collected. This paper showed that supervised learning improves recall by 21 % when compared with the Delphi method. This paper also highlights that changes in input features' distributions (or covariate shifts) across construction stages and projects can reduce the recall of the supervised learning model from 93 % to 50 %. Key academic and practical contributions include the analytics-based approaches to develop weights for measuring contractors' safety performance, and strategies to manage the impact of covariate shifts on accuracy of feature weights.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106142"},"PeriodicalIF":9.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715003","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

Real-time bridge disaster management: Enabling technology and application framework 实时桥梁灾难管理：启用技术和应用程序框架

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-27 DOI: 10.1016/j.autcon.2025.106150

Hairong Deng , Haijiang Li , Lueqin Xu , Ali Khudhair , Honghong Song , Yu Gao

{"title":"Real-time bridge disaster management: Enabling technology and application framework","authors":"Hairong Deng , Haijiang Li , Lueqin Xu , Ali Khudhair , Honghong Song , Yu Gao","doi":"10.1016/j.autcon.2025.106150","DOIUrl":"10.1016/j.autcon.2025.106150","url":null,"abstract":"<div><div>Bridges are susceptible to severe damage from natural disasters, heavy traffic loads, and material degradation, necessitating timely and accurate information for effective emergency response. Current bridge disaster management systems often fail to meet real-time requirements due to interoperability challenges and fragmented functionalities across different phases. This paper systematically reviews 146 research articles on bridge disaster management, summarising the key challenges and potential disasters that bridges may face during their operational lifespan and highlighting the technological applications and decision-making requirements for effective disaster management. Key limitations in existing systems include inadequate technology integration, delayed response times, and insufficient coordination. To address these gaps, this paper proposes a Real-time Bridge Disaster Management (RtBDM) framework, which integrates advanced digital technologies to enable real-time monitoring, data analysis, and adaptive decision-making. The proposed framework offers practical solutions to enhance bridge resilience and safety during disasters and provides valuable insights for future research in this field.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106150"},"PeriodicalIF":9.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706276","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