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

et al.

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

Data-driven and physics-informed neural network for predicting tunnelling-induced ground deformation with sparse data of field measurement DOI
Yingbin Liu, Shaoming Liao, Yaowen Yang

et al.

Tunnelling and Underground Space Technology, Journal Year: 2024, Volume and Issue: 152, P. 105951 - 105951

Published: July 5, 2024

Language: Английский

Citations

10
Two-stage surrogate modeling strategy for predicting foundation pit excavation-induced strata and tunnel deformation DOI
Z.-H. Liu, Qian Fang, Yi Shen

et al.

Tunnelling and Underground Space Technology, Journal Year: 2024, Volume and Issue: 151, P. 105845 - 105845

Published: June 5, 2024

Language: Английский

Citations

7
Physics-informed deep operator networks with stiffness-based loss functions for structural response prediction DOI Creative Commons
Bilal Ahmed, Yuqing Qiu, Diab Abueidda

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 144, P. 110097 - 110097

Published: Jan. 25, 2025

Language: Английский

Citations

1
Knowledge-integrated deep learning for predicting stochastic thermal regime of embankment in permafrost region DOI Creative Commons
Lei Xiao, Gang Mei, Nengxiong Xu

et al.

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: May 1, 2024

The warming and thawing of permafrost are the primary factors that impact stability embankments in cold regions. However, due to uncertainties thermal boundaries soil properties, stochastic modeling regimes is challenging computationally expensive. To address this, we propose a knowledge-integrated deep learning method for predicting regime Geotechnical knowledge embedded training data through numerical modeling, while neural network learns mapping from boundary property fields temperature field. effectiveness our verified comparison with monitoring analysis results. Experimental results show proposed achieves good accuracy small coefficient variation. It still provides satisfactory as variation increases. an efficient approach predict heterogeneous embankments. can also be used other engineering investigations require modeling.

Language: Английский

Citations

4
Design optimization of quasi-rectangular tunnels based on hyperstatic reaction method and ensemble learning DOI Creative Commons
Tien Trong Nguyen, Ba Trung Cao, Van Vi Pham

et al.

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Language: Английский

Citations

3
Data-driven methods DOI

O.C. Zienkiewicz,

Robert L. Taylor, Perumal Nithiarasu

et al.

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 485 - 500

Published: Jan. 1, 2025

Citations

0
Computational Hydro-Thermal Design of Ground Freezing in Tunneling: Optimization of Pipe Layout and Real-Time Prediction DOI

Rodolfo Javier Williams Moises,

Yaman Zendaki, Ba Trung Cao

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
A deep operator network method for high-precision and robust real-time ocean wave prediction DOI
Haicheng Zhang, Qi Zhang, Pengcheng Li

et al.

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(3)

Published: March 1, 2025

Real-time wave prediction is crucial for optimizing offshore renewable energy capture and ensuring the safety of floating structures. However, stochastic nonlinear nature waves presents significant challenges accurate robust predictions. This study proposes a model based on Deep Operator Network (DON-WP), which learns operator to map historical heights future heights. By leveraging this operator-learning framework, demonstrates strong generalization across function space, enabling it adapt previously unseen conditions. Specifically, branch net encodes data into functional representations, while trunk captures targets as evaluation points output function. These outputs are then combined through element-wise operations generate precise The model's ability robustness validated using tank experimental multiple sea states, its performance compared with Long Short-Term Memory network-based probabilistic (Deep-WP). Results show that DON-WP, trained single state, achieves over 30% higher accuracy most horizons up 60% improvement shorter steps Deep-WP, requires retraining each state. highlights DON-WP an effective approach dynamics modeling, potential advance systems enhance

Language: Английский

Citations

0
Exploration of deep operator networks for predicting the piezoionic effect DOI
Shuyu Wang,

Dingli Zhang,

A.H.-J. Wang

et al.

The Journal of Chemical Physics, Journal Year: 2025, Volume and Issue: 162(11)

Published: March 17, 2025

The piezoionic effect holds significant promise for revolutionizing biomedical electronics and ionic skins. However, modeling this multiphysics phenomenon remains challenging due to its high complexity computational limitations. To address problem, study pioneers the application of deep operator networks effectively model time-dependent effect. By leveraging a data-driven approach, our significantly reduces time compared traditional finite element analysis (FEA). In particular, we trained DeepONet using comprehensive dataset generated through FEA calibrated experimental data. Through rigorous testing with step responses, slow-changing forces, dynamic-changing show that captures intricate temporal dynamics in both horizontal vertical planes. This capability offers powerful tool real-time phenomena, contributing simplifying design tactile interfaces potentially complementing existing imaging technologies.

Language: Английский

Citations

0
Tunnel water inflow prediction using explainable machine learning and augmented partially missing dataset DOI Creative Commons
Ming‐Shaung Ju,

Guangzhao Ou,

Tao Peng

et al.

Frontiers in Earth Science, Journal Year: 2025, Volume and Issue: 13

Published: April 25, 2025

Accurate prediction of water inrush volumes is essential for safeguarding tunnel construction operations. This study proposes a method predicting volumes, leveraging the eXtreme Gradient Boosting (XGBoost) model optimized with Bayesian techniques. To maximize utility available data, 654 datasets missing values were imputed and augmented, forming robust dataset training validation XGBoost (BO-XGBoost) model. Furthermore, SHapley Additive explanations (SHAP) was employed to elucidate contribution each input feature predictive outcomes. The results indicate that: (1) constructed BO-XGBoost exhibited exceptionally high accuracy on test set, root mean square error (RMSE) 7.5603, absolute (MAE) 3.2940, percentage (MAPE) 4.51%, coefficient determination (R 2 ) 0.9755; (2) Compared performance support vector mechine (SVR), decision tree (DT), random forest (RF) models, demonstrates highest R smallest error; (3) importance yielded by SHAP groundwater level ( h > water-producing characteristics W burial depth H rock mass quality index RQD ). proposed volume dataset, thereby aiding managers in making informed decisions mitigate risks ensuring safe efficient advancement projects.

Language: Английский

Citations

0