Automation in Construction最新文献_第8页

Simulation as a decision-support tool in construction project management: Simphony-Dynamic-as-a-Service 模拟作为建设项目管理中的决策支持工具：模拟-动态即服务

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-30 DOI: 10.1016/j.autcon.2025.106198

Muhtasim Fuad Rafid , Stephen Hague , Simaan AbouRizk , Eleni Stroulia

{"title":"Simulation as a decision-support tool in construction project management: Simphony-Dynamic-as-a-Service","authors":"Muhtasim Fuad Rafid , Stephen Hague , Simaan AbouRizk , Eleni Stroulia","doi":"10.1016/j.autcon.2025.106198","DOIUrl":"10.1016/j.autcon.2025.106198","url":null,"abstract":"<div><div>Simulation can play a key role in construction project management, enabling decision-makers to forecast the impact of real-world events, alternative scheduling, and resource-allocation decisions. Such simulation-based forecasting can substantially inform project management but has not yet been widely adopted in the construction industry. This paper describes Simphony-Dynamic-as-a-Service, an advanced simulation tool that bridges the gap between simulation software and real-world construction practices. The contributions are the following: Re-architecting Simphony Dynamicinto a set of coordinated cloud-based micro-services. Grounding the construction project data in a well-defined project-modeling language — DiCon. Offering a variety of browser-accessible user interfaces, tailored to the needs of different stakeholders. Incorporating critical-path computation and comparisons to assist in identifying critical tasks and evaluating alternative schedules. The platform provides APIs for real-time project progress tracking, enhancing monitoring capabilities, and enabling model re-simulation from any point in time for exploring what-if scenarios and facilitating informed decision-making.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106198"},"PeriodicalIF":9.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891796","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 enhancement of customer-centric design for improved satisfaction and decision-making 人工智能驱动的以客户为中心的设计增强，以提高满意度和决策

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-30 DOI: 10.1016/j.autcon.2025.106220

Mehrshad Dorri , Soroush Hoseinpour , Mojtaba Maghrebi

{"title":"AI-driven enhancement of customer-centric design for improved satisfaction and decision-making","authors":"Mehrshad Dorri , Soroush Hoseinpour , Mojtaba Maghrebi","doi":"10.1016/j.autcon.2025.106220","DOIUrl":"10.1016/j.autcon.2025.106220","url":null,"abstract":"<div><div>Customer-centric design improves satisfaction by actively involving clients in the construction decision-making process. However, current Virtual Reality (VR) methods rely on manual processes, extensive human resources, and costly high-end hardware, restricting accessibility. This study proposes an AI-driven enhancement of customer-centric design in construction through an accessible smartphone-based VR platform integrated with an Intelligent Support System (ISS). The ISS employs an artificial neural network to predict customers' preferred design styles from user-uploaded images and utilizes the TOPSIS algorithm to rank design alternatives based on customer evaluations regarding cost, time, risk, and aesthetics. Selected designs are automatically communicated to the design team. A validation case study involving 30 construction industry participants indicated a satisfaction rate of 79 %, overall approval rating of 69 %, and an 80 % match between user-selected designs and ISS recommendations. The developed AI-driven VR platform effectively improves decision-making, customer satisfaction, and accessibility of customer-centric participation in construction.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106220"},"PeriodicalIF":9.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891798","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

Pavement thickness evaluation using GPR and fuzzy logic 基于探地雷达和模糊逻辑的路面厚度评价

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-30 DOI: 10.1016/j.autcon.2025.106236

Ali Fares, Tarek Zayed, Nour Faris, Abdul-Mugis Yussif, Sherif Abdelkhalek

{"title":"Pavement thickness evaluation using GPR and fuzzy logic","authors":"Ali Fares, Tarek Zayed, Nour Faris, Abdul-Mugis Yussif, Sherif Abdelkhalek","doi":"10.1016/j.autcon.2025.106236","DOIUrl":"10.1016/j.autcon.2025.106236","url":null,"abstract":"<div><div>Accurate pavement thickness evaluation is essential, as insufficient thickness can lead to surface distress and structural failures. However, pavement thickness is often overlooked in condition assessment models. While ground penetrating radar (GPR) offers a useful non-destructive solution, existing models are typically limited to surface layers and require significant user input. To address these challenges, this paper developed an integrated layer interface detection system and a Thickness Condition Index (TCI) using GPR and fuzzy logic. The TCI is designed to support the incorporation of thickness evaluation into pavement condition models. The developed models were tested on twelve diverse road sections from the Long-Term Pavement Performance (LTPP) database. The developed TCI enables informed decisions, facilitating efficient pavement maintenance. While the models demonstrated consistent performance, key limitations include addressing low-thickness layers and low dielectric constant contrast between layers. Future research should explore signal processing techniques, such as decomposition methods, to enhance models robustness.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106236"},"PeriodicalIF":9.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891797","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

Transfer learning for smart construction: Advances and future directions 智能建筑的迁移学习：进展与未来方向

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-30 DOI: 10.1016/j.autcon.2025.106238

Yu Gao , Xiaoxiao Xu , Tak Wing Yiu , Jiayuan Wang

{"title":"Transfer learning for smart construction: Advances and future directions","authors":"Yu Gao , Xiaoxiao Xu , Tak Wing Yiu , Jiayuan Wang","doi":"10.1016/j.autcon.2025.106238","DOIUrl":"10.1016/j.autcon.2025.106238","url":null,"abstract":"<div><div>Transfer learning has emerged as a powerful tool and rapidly advanced numerous fields with cutting-edge technologies. This paper provides a comprehensive review of transfer learning applications in smart construction, analyzing its utilization to enrich the construction industry's knowledge. A systematic analysis of 366 publications from 2015 to 2024 highlights the growth and importance of transfer learning in the field. This review establishes a foundational framework by exploring key questions: “Why transfer learning”, “What to transfer”, “How to transfer”, and “When to transfer”. The findings reveal that transfer learning is predominantly applied in seven key construction domains, but it faces four major challenges: “transfer strategy”, “interpretability, security and privacy”, “modality transfer”, and “cross-domain adaptability”. Corresponding future research directions are proposed to address these challenges. This paper serves as a crucial reference point for researchers, practitioners, and stakeholders aiming to harness the transformative potential of transfer learning in the construction industry.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106238"},"PeriodicalIF":9.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891878","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

Underwater vision-enhanced image segmentation for supporting automated inspection of underwater bridge components 支持水下桥梁构件自动检测的水下视觉增强图像分割

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-30 DOI: 10.1016/j.autcon.2025.106230

Saeed Talamkhani, Kaijian Liu

{"title":"Underwater vision-enhanced image segmentation for supporting automated inspection of underwater bridge components","authors":"Saeed Talamkhani, Kaijian Liu","doi":"10.1016/j.autcon.2025.106230","DOIUrl":"10.1016/j.autcon.2025.106230","url":null,"abstract":"<div><div>Vision-based robotic systems for automated bridge inspection are limited in analyzing underwater inspection images, which present a set of unique visual challenges caused by light scattering, light attenuation, and low-light conditions in underwater environments. To address this limitation, this paper proposes an underwater vision-enhanced image segmentation method: (1) underwater vision-based quality enhancement is proposed to simultaneously mitigate quality degradations of underwater inspection images caused by light scattering, light attenuation, and low-light conditions; and (2) semantic segmentation is proposed to analyze quality-enhanced underwater images to localize bridge components, enabling effective component localization for subsequent damage detection and characterization in underwater inspection images. Baseline and ablation experiments were conducted for performance evaluation. The results showed that the proposed method achieved a mean, structure, and background IoUs of 91.7 %, 88.5 % and 94.8 % – outperforming state-of-the-art methods in segmenting underwater inspection images and demonstrating its potential to enable vision-based robotic systems for cost-effective underwater inspection.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106230"},"PeriodicalIF":9.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891879","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

Optimization of the shotcrete spraying process in blast-based tunnel construction: Insights for enhancing spraying quality 爆破隧道施工喷射混凝土喷淋工艺优化：提高喷淋质量的启示

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-30 DOI: 10.1016/j.autcon.2025.106234

Fenghua Liu , Jiajing Liu , Botao Zhong , Jun Sun

{"title":"Optimization of the shotcrete spraying process in blast-based tunnel construction: Insights for enhancing spraying quality","authors":"Fenghua Liu , Jiajing Liu , Botao Zhong , Jun Sun","doi":"10.1016/j.autcon.2025.106234","DOIUrl":"10.1016/j.autcon.2025.106234","url":null,"abstract":"<div><div>Shotcrete is important to form the primary lining of blast-based tunnel construction, but this process heavily relies on operator expertise. Therefore, this research investigates the following research question: <em>How to effectively model and optimize the shotcrete spraying process to construct high-quality primary linings?</em> This paper proposes a spraying process optimization method, comprising three key components: (1) a spraying trajectories parameterization model; (2) a dynamic spraying deposition model; and (3) a spraying process parameter optimization algorithm. Validation through a railway tunnel case study demonstrates the method's effectiveness, yielding optimal spraying parameters with a mean absolute error of 0.6 cm against a target thickness of 19.4 cm. These findings provide actionable solutions for the spraying process, and reduce reliance on expertise. Future research directions can focus on integrating advanced material characterization into the spraying deposition model and developing intelligent sprayers for precise spraying parameter implementation</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106234"},"PeriodicalIF":9.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891799","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

UAV-based automated earthwork progress monitoring using deep learning with image inpainting 基于无人机的自动化土方工程进度监测，使用深度学习和图像绘制

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-28 DOI: 10.1016/j.autcon.2025.106211

Ahmet Bahaddin Ersoz, Onur Pekcan

引用次数: 0

Design and application of permanent magnetic tools for magnetically driven concrete crack filling technology 磁力驱动混凝土裂缝充填技术中永磁工具的设计与应用

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-27 DOI: 10.1016/j.autcon.2025.106229

Onur Ozturk, Sriramya Duddukuri Nair

引用次数: 0

Enhancing deep learning in structural damage identification with 3D-engine synthetic data 利用3d引擎合成数据增强结构损伤识别中的深度学习

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-27 DOI: 10.1016/j.autcon.2025.106203

Pa Pa Win Aung , Kaung Myat Sam , Almo Senja Kulinan , Gichun Cha , Minsoo Park , Seunghee Park

{"title":"Enhancing deep learning in structural damage identification with 3D-engine synthetic data","authors":"Pa Pa Win Aung , Kaung Myat Sam , Almo Senja Kulinan , Gichun Cha , Minsoo Park , Seunghee Park","doi":"10.1016/j.autcon.2025.106203","DOIUrl":"10.1016/j.autcon.2025.106203","url":null,"abstract":"<div><div>Structural damage identification is crucial for maintaining infrastructure safety and durability. While deep learning-based computer vision has shown promise in this process, the scarcity of high-quality annotated data remains a challenge. To address this, synthetic data has emerged as a promising solution, enabling the creation of large and diverse datasets. This paper presents an approach that uses a 3D engine to generate synthetic crack images with controlled variations in morphology and environment, including automatic annotations. The synthetic dataset, calibrated to match real-world scales, was used to train models and significantly improved performance in detection and segmentation tasks. Experimental results showed nearly double the detection accuracy and over 2.5 times improvement in segmentation precision compared to models trained only on real data. These results demonstrate the potential of simulation-based synthetic data to improve generalization in data-scarce scenarios. This paper offers a scalable solution for structural damage detection in civil infrastructure monitoring.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106203"},"PeriodicalIF":9.6,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877122","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

Efficient fine-tuning of large language models for automated building energy modeling in complex cases 复杂情况下自动化建筑能源建模的大型语言模型的高效微调

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-04-27 DOI: 10.1016/j.autcon.2025.106223

Gang Jiang , Jianli Chen

{"title":"Efficient fine-tuning of large language models for automated building energy modeling in complex cases","authors":"Gang Jiang , Jianli Chen","doi":"10.1016/j.autcon.2025.106223","DOIUrl":"10.1016/j.autcon.2025.106223","url":null,"abstract":"<div><div>Building energy modeling (BEM) requires extensive time and efforts in building development. Automated building energy modeling (ABEM) is significant to reduce this burden, promoting widespread adoption of building energy modeling in building design and operation practice. This paper presents an efficient fine-tuning approach to tailor large language models (LLMs) for ABEM. Leveraging Low-Rank Adapter (LoRA) and a comprehensive training dataset (490 k samples), the proposed approach enhances LLM customization while maintaining computational efficiency. Model quantization and mixed-precision training further optimize efficiency without compromising performance. Using this approach, the developed platform (EPlus-LLMv2) can auto-generate complex buildings with varying geometries, thermal zones, materials, hourly schedules, operation settings, etc. Tests on 402 modeling cases demonstrate 100 % accuracy of modeling while reducing modeling efforts by >98 %. Additionally, an interactive human-AI interface is developed to further enhance the platform's accessibility. Finally, insights and future work to customize LLMs in ABEM and other building applications are discussed.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106223"},"PeriodicalIF":9.6,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876795","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