Advancing Tunnel Construction Reliability with Automated Artificial Intelligence under Geotechnical and Aleatoric Uncertainties DOI Open Access
Jagendra Singh, Prabhishek Singh, Vinayakumar Ravi

и другие.

The Open Civil Engineering Journal, Год журнала: 2024, Номер 18(1)

Опубликована: Окт. 4, 2024

Aims This research seeks to improve the reliability and sustainability of tunnel construction by employing automated AI techniques manage geotechnical aleatoric uncertainties. It utilizes machine learning models, including Gradient Boosting Machines (GBM), AdaBoost, Hidden Markov Models (HMM), Deep Q-Networks for Reinforcement Learning, predict reduce environmental impacts. The effectiveness these algorithms is assessed using various performance metrics demonstrate their impact on enhancing processes. Background While vital modern infrastructure development, it poses significant challenges. Traditional methods assessing impacts often rely manual overly simplistic models that fail consider complex interactions inherent uncertainties factors. aims overcome limitations applying techniques, particularly algorithms, more accurately mitigate Objective goal this study increase AI-based address both focuses deploying such as GBM, HMM, Learning forecast negative algorithms' measured against criteria in optimizing outcomes. Methods applies Q-Networks, enhance construction's sustainability. These are designed while accounting models' evaluated like accuracy, precision, recall, F1 score, log loss, mean squared error (MSE), log-likelihood, cumulative reward, convergence rate, policy stability, indicating substantial improvements practices. Results shows significantly enhances GBM achieved a high accuracy 0.92 an score 0.90. Additionally, effectively identified optimal strategies, resulting reward 950. outcomes highlight capability uncertainties, leading safer, resilient development. Conclusion findings suggest integrating substantially improves projects. approaches with providing predictive scores strategies. Adopting technologies could result sustainable, infrastructure, underscoring potential transforming

Язык: Английский

Digital twins for urban underground space DOI Creative Commons
Nandeesh Babanagar, Brian Sheil, Jelena Ninić

и другие.

Tunnelling and Underground Space Technology, Год журнала: 2024, Номер 155, С. 106140 - 106140

Опубликована: Окт. 23, 2024

Язык: Английский

Процитировано

7

Collapse Mechanism and Mitigations of Mountain Tunnel Crossing Fault Fracture Zone: A Case Study from Southeast China DOI
Hao Chen, Bolong Liu, Hongpeng Lai

и другие.

Geotechnical and Geological Engineering, Год журнала: 2025, Номер 43(4)

Опубликована: Март 21, 2025

Язык: Английский

Процитировано

1

Automated framework for asphalt pavement design and analysis by integrating BIM and FEM DOI
Ziming Liu, Hao Huang, Yongdan Wang

и другие.

Automation in Construction, Год журнала: 2025, Номер 171, С. 105991 - 105991

Опубликована: Янв. 23, 2025

Язык: Английский

Процитировано

0

Surface Feature and Defect Detection Method for Shield Tunnel Based on Deep Learning DOI
Laikuang Lin, Hailong Zhu, Yingbo Ma

и другие.

Journal of Computing in Civil Engineering, Год журнала: 2025, Номер 39(3)

Опубликована: Фев. 7, 2025

Язык: Английский

Процитировано

0

Tunnel BIM Model Integration Construction Method and Engineering Application DOI

He Ruibing,

Cheng Yao, Jing Wang

и другие.

Iranian Journal of Science and Technology Transactions of Civil Engineering, Год журнала: 2025, Номер unknown

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

Integrated BIM, GIS and interoperable digital technologies in lifecycle management of building construction projects: systematic literature review DOI
Fatma Handan Sarigul, H. Murat Günaydın

Smart and Sustainable Built Environment, Год журнала: 2025, Номер unknown

Опубликована: Апрель 9, 2025

Purpose In the digital era, there is a growing interest in integrating building information modeling (BIM) and geographic system (GIS) technologies throughout construction lifecycle, including design, construction, operation, maintenance demolition, using interoperable technologies. This study aims to provide holistic assessment of potential BIM/GIS integration improve quality by comprehensively analyzing literature reveal application areas, opportunities, most urgent problems/obstacles, technical solutions, gaps future directions for construction. Design/methodology/approach review seeks answers five identified research questions conducts systematic process that includes content analysis bibliometric 141 articles published between 2007 2024 from Scopus database. Findings Integrated reveals leading topics cutting-edge technologies, lifecycle management, data disaster urban management activities (type location), are gaining significant momentum. Additionally, such as circularity, cultural heritage, waste, safety, life cycle (LCA) supply chain remain open further exploration. The results indicate challenge lies converting geometric semantic Industry Foundation Classes (IFC)/City Geography Markup Language (CityGML) or shapefile formats BIM/GIS. consensus suggests utilizing single/third-party platform, with Infraworks software being prominent option. Originality/value offers practical recommendations tackle current challenges based on analyses conducted while highlighting should aim integrate BIM/GIS-enabled twins buildings cities.

Язык: Английский

Процитировано

0

Intelligent design and evaluation of tunnel support structure systems DOI
Ziquan Chen, Chuan He, Zihan Zhou

и другие.

Automation in Construction, Год журнала: 2025, Номер 175, С. 106215 - 106215

Опубликована: Апрель 18, 2025

Процитировано

0

Construction Cost Optimization of Prefabricated Buildings Based on BIM Technology DOI Creative Commons

Liwei Fang,

Masahiro Arakawa

Information Resources Management Journal, Год журнала: 2024, Номер 37(1), С. 1 - 14

Опубликована: Июль 18, 2024

Prefabricated assemblies are popular in the construction industry due to their minimal carbon footprint, enhanced safety, and reliability. A combination of software, including Revit, Navisworks, Practical Structural Design Construction (PKPM) is used reduce costs by refining specifications dimensions components, streamlining variety molds, enhancing design performance through rigorous component collision inspections structural optimization, ensuring cost-effectiveness. The integration building modeling (BIM) visualization significantly diminishes errors attributable information asymmetry minimized material wastage stemming from production inaccuracies. implementation a Radio Frequency Identification (RFID) exchange platform enables real-time tracking, provides insights into transportation dynamics these facilitates cost optimization during phase. Moreover, simulations conducted using Fuzor software preemptively identify potential site issues. There substantial savings 710,000 yuan.

Язык: Английский

Процитировано

1

Advancing Tunnel Construction Reliability with Automated Artificial Intelligence under Geotechnical and Aleatoric Uncertainties DOI Open Access
Jagendra Singh, Prabhishek Singh, Vinayakumar Ravi

и другие.

The Open Civil Engineering Journal, Год журнала: 2024, Номер 18(1)

Опубликована: Окт. 4, 2024

Aims This research seeks to improve the reliability and sustainability of tunnel construction by employing automated AI techniques manage geotechnical aleatoric uncertainties. It utilizes machine learning models, including Gradient Boosting Machines (GBM), AdaBoost, Hidden Markov Models (HMM), Deep Q-Networks for Reinforcement Learning, predict reduce environmental impacts. The effectiveness these algorithms is assessed using various performance metrics demonstrate their impact on enhancing processes. Background While vital modern infrastructure development, it poses significant challenges. Traditional methods assessing impacts often rely manual overly simplistic models that fail consider complex interactions inherent uncertainties factors. aims overcome limitations applying techniques, particularly algorithms, more accurately mitigate Objective goal this study increase AI-based address both focuses deploying such as GBM, HMM, Learning forecast negative algorithms' measured against criteria in optimizing outcomes. Methods applies Q-Networks, enhance construction's sustainability. These are designed while accounting models' evaluated like accuracy, precision, recall, F1 score, log loss, mean squared error (MSE), log-likelihood, cumulative reward, convergence rate, policy stability, indicating substantial improvements practices. Results shows significantly enhances GBM achieved a high accuracy 0.92 an score 0.90. Additionally, effectively identified optimal strategies, resulting reward 950. outcomes highlight capability uncertainties, leading safer, resilient development. Conclusion findings suggest integrating substantially improves projects. approaches with providing predictive scores strategies. Adopting technologies could result sustainable, infrastructure, underscoring potential transforming

Язык: Английский

Процитировано

0