Contrastive learning method for leak detection in water distribution networks DOI Creative Commons
Rongsheng Liu, Tarek Zayed, Rui Xiao

et al.

npj Clean Water, Journal Year: 2024, Volume and Issue: 7(1)

Published: Nov. 27, 2024

Language: Английский

Development of Machine Learning Based Surrogate Models Using Fully Coupled 3D Numerical Analysis of Scour around Slab-on-Grade Foundations DOI
Hiramani Chimauriya, Nripojyoti Biswas, Amit Gajurel

et al.

Geotechnical Frontiers 2017, Journal Year: 2025, Volume and Issue: unknown, P. 21 - 29

Published: Feb. 27, 2025

Language: Английский

Citations

0
Probabilistic analysis of active earth pressures in spatially variable soils using machine learning and confidence intervals DOI Creative Commons

Tran Vu-Hoang,

Tan Nguyen, Jim Shiau

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 14, 2025

This study introduces a probabilistic framework for assessing active earth pressures in soils exhibiting spatial variability friction angles and unit weight. These properties are modeled using random fields with log-normal distributions correlation lengths. Monte Carlo simulations (MCS) integrated finite element limit analysis (FELA) to evaluate the failure probability under different design safety factors. To improve computational efficiency prediction accuracy, machine learning models, such as Multivariate Adaptive Regression Splines (MARS), utilized predict probabilities based on key parameters. A two-phase optimization approach, combining Random Search Sampling, is employed refine hyperparameters of model. Confidence intervals incorporated quantify reliability, providing engineers robust decision-making tools uncertainty. Furthermore, adaptive meshes applied capture irregular stochastic mechanisms, offering deeper insights into impact variability. The produces parametric results form practical contour charts, aiding optimizing margins while accounting soil By methods, learning, uncertainty quantification, this research enhances geotechnical practices, ensuring more reliable cost-effective solutions.

Language: Английский

Citations

0
An adaptive physics-informed deep learning approach for structural nonlinear response prediction DOI
Zheqian Wu, Yingmin Li

The Journal of Supercomputing, Journal Year: 2024, Volume and Issue: 81(1)

Published: Oct. 28, 2024

Language: Английский

Citations

2
An end-to-end multi-task network for early prediction of the instrumental intensity and magnitude in the north–south seismic belt of China DOI
Qingxu Zhao, Mianshui Rong, Jixin Wang

et al.

Journal of Asian Earth Sciences, Journal Year: 2024, Volume and Issue: unknown, P. 106369 - 106369

Published: Oct. 1, 2024

Language: Английский

Citations

1
Contrastive learning method for leak detection in water distribution networks DOI Creative Commons
Rongsheng Liu, Tarek Zayed, Rui Xiao

et al.

npj Clean Water, Journal Year: 2024, Volume and Issue: 7(1)

Published: Nov. 27, 2024

Language: Английский

Citations

0