Parametric deep learning model for predicting bearing capacity of strip foundation via neural operator

AI in Civil Engineering, Journal Year: 2025, Volume and Issue: 4(1)

Published: May 1, 2025

Language: Английский

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Moment method-based reliability analysis of random vibration in rolling bearings under insufficient probability information

Mechanics Based Design of Structures and Machines, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 23

Published: May 4, 2025

Language: Английский

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A lightweight physics‐data‐driven method for real‐time prediction of subgrade settlements induced by shield tunneling

Computer-Aided Civil and Infrastructure Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: May 19, 2025

Abstract Real‐time prediction of subgrade settlement caused by shield tunneling is crucial in engineering applications. However, data‐driven methods are prone to overfitting, while physical rely on certain assumptions, making it difficult select satisfactory parameters. Although there currently physics‐data‐driven methods, they typically require extensive iterative calculations with models, which makes them unavailable for real‐time prediction. This paper introduces a lightweight method predicting tunneling. The core concept involves using single calculation the model provide weak constraint. A deep learning network then designed capture spatiotemporal correlations based ConvLSTM. By iteratively incorporating data, constraints further enhanced. combines predictive power reasonable laws, validated good performance practical project. results demonstrate that this meets requirements engineering, achieving an coefficient determination 0.980, root mean square error 0.22 mm, and absolute 0.15 mm. Furthermore, outperforms both models demonstrates generalization performance. study provides effective guidance practices.

Language: Английский

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Advancing Tunnel Construction Reliability with Automated Artificial Intelligence under Geotechnical and Aleatoric Uncertainties

The Open Civil Engineering Journal, Journal Year: 2024, Volume and Issue: 18(1)

Published: Oct. 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

Language: Английский

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An advanced physics-informed neural operator for comprehensive design optimization of highly-nonlinear systems: An aerospace composites processing case study

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 142, P. 109886 - 109886

Published: Dec. 27, 2024

Language: Английский

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