Automation in Construction最新文献_第2页

Asphalt concrete density monitoring during compaction using roller-mounted GPR

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

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

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

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

Feature weights in contractor safety performance assessment: Comparative study of expert-driven and analytics-based approaches

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

BIM, IoT, and GIS integration in construction resource monitoring

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

Real-time bridge disaster management: Enabling technology and application framework

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

Local search-based online learning algorithm for shape and cross-section optimization of free-form single-layer reticulated shells

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-27 DOI: 10.1016/j.autcon.2025.106144

Qiang Zeng , Makoto Ohsaki , Kazuki Hayashi , Shaojun Zhu , Xiaonong Guo

{"title":"Local search-based online learning algorithm for shape and cross-section optimization of free-form single-layer reticulated shells","authors":"Qiang Zeng , Makoto Ohsaki , Kazuki Hayashi , Shaojun Zhu , Xiaonong Guo","doi":"10.1016/j.autcon.2025.106144","DOIUrl":"10.1016/j.autcon.2025.106144","url":null,"abstract":"<div><div>Reasonable shape and cross-section design of free-form Single-Layer Reticulated Shells (SLRSs) are crucial for their superior static performance and material efficiency. However, traditional metaheuristics face high computational costs and are prone to converging to local optima when optimizing these factors simultaneously, often leading to necessity of carrying out decoupled design processes. This paper introduces a Local Search-based Online Learning Algorithm (LSOLA) for simultaneous shape and cross-section optimization of free-form SLRSs. LSOLA builds deep learning models in various sub-regions of the solution space and uses a hybrid query strategy to actively select promising samples, iteratively improving prediction accuracy near potentially optimal solutions for more efficient exploration. Numerical examples show that LSOLA delivers more diverse and superior solutions at lower computational costs compared to the existing global search-based online learning algorithms and metaheuristics. This paper also offers a reference for other optimization problems involving numerous variables and nonlinear constraints.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106144"},"PeriodicalIF":9.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706357","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

Multi-task deep reinforcement learning for dynamic scheduling of large-scale fleets in earthmoving operations

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-27 DOI: 10.1016/j.autcon.2025.106123

Yunuo Zhang, Jun Zhang, Xiaoling Wang, Tuocheng Zeng

{"title":"Multi-task deep reinforcement learning for dynamic scheduling of large-scale fleets in earthmoving operations","authors":"Yunuo Zhang, Jun Zhang, Xiaoling Wang, Tuocheng Zeng","doi":"10.1016/j.autcon.2025.106123","DOIUrl":"10.1016/j.autcon.2025.106123","url":null,"abstract":"<div><div>Large-scale earthwork transportation encounters queuing congestion and dynamic uncertainties, while existing methods ignore complex traffic behaviors and exhibit limited responsiveness and generalization. This paper proposes a multi-task Deep Reinforcement Learning (DRL) framework for the dynamic scheduling of large fleets across supply sites and traffic networks. In the framework, multiple agents interact in complex environments modeled by discrete-event simulation, utilizing long short-term memory networks that consider queuing behaviors and dynamic trends of transportation systems to allocate rational materials, supply sites, and routes collaboratively, with an invariant update strategy to balance generalization and task-specific optimization during training. Experiments demonstrate that the model generates dynamic schedules within 7 min, reducing transportation time by 24 %. The trained agent can adapt to the changing transportation demand in complex construction environments and enhance transportation efficiency. This paper demonstrates the potential of DRL in scheduling more complex construction projects and promoting real-time lean control of modern logistics.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106123"},"PeriodicalIF":9.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706359","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

3D wireframe model reconstruction of buildings from multi-view images using neural implicit fields

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-27 DOI: 10.1016/j.autcon.2025.106145

Weiwei Fan , Xinyi Liu , Yongjun Zhang , Dong Wei , Haoyu Guo , Dongdong Yue

引用次数: 0