Application of multi-algorithm ensemble methods in high-dimensional and small-sample data of geotechnical engineering: A case study of swelling pressure of …

C Li, L Wang, J Li, Y Chen - Journal of Rock Mechanics and Geotechnical …, 2024 - Elsevier
Geotechnical engineering data are usually small-sample and high-dimensional, which
brings a lot of challenges in predictive modeling. This paper uses a typical high-dimensional …

Predicting uniaxial tensile strength of expansive soil with ensemble learning methods

Y Chen, Y Xu, B Jamhiri, L Wang, T Li - Computers and Geotechnics, 2022 - Elsevier
Tensile stress is a major parameter controlling the desiccation cracking, which may incur the
tensile failure of expansive soil slopes. In this study, a series of experiments was performed …

[HTML][HTML] Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms

P Zhang, ZY Yin, YF Jin, THT Chan, FP Gao - Geoscience Frontiers, 2021 - Elsevier
Compression index C c is an essential parameter in geotechnical design for which the
effectiveness of correlation is still a challenge. This paper suggests a novel modelling …

Application of boosting-based ensemble learning method for the prediction of compression index

K Mamudur, MR Kattamuri - Journal of The Institution of Engineers (India) …, 2020 - Springer
Obtaining geotechnical design parameters by conducting in situ or laboratory testing has
always been challenging because of difficulty involved with handling, transportation, release …

[HTML][HTML] Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization

W Zhang, C Wu, H Zhong, Y Li, L Wang - Geoscience Frontiers, 2021 - Elsevier
Accurate assessment of undrained shear strength (USS) for soft sensitive clays is a great
concern in geotechnical engineering practice. This study applies novel data-driven extreme …

Invasive weed optimization algorithm for prediction of compression index of lime-treated expansive clays

T Vamsi Nagaraju, C Durga Prasad… - Soft Computing for …, 2020 - Springer
With the recent emphasis on large-scale civil engineering constructions, artificial intelligence
in the construction activities has received importance. Compressibility behavior is an …

[PDF][PDF] Machine learning-based prediction of soil compression modulus with application of 1D settlement

DM Zhang, JZ Zhang, HW Huang, CC Qi… - Journal of Zhejiang …, 2020 - researchgate.net
The compression modulus (Es) is one of the most significant soil parameters that affects the
compressive deformation of geotechnical systems, such as foundations. However, it is …

[HTML][HTML] Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil …

A Bardhan, N Kardani, AK Alzo'ubi, B Roy… - Journal of Rock …, 2022 - Elsevier
The study proposes an improved Harris hawks optimization (IHHO) algorithm by integrating
the standard Harris hawks optimization (HHO) algorithm and mutation-based search …

A Novel Approach to Swell Mitigation: Machine-Learning-Powered Optimal Unit Weight and Stress Prediction in Expansive Soils

A Alnmr, R Ray, MO Alzawi - Applied Sciences, 2024 - mdpi.com
Expansive soils pose significant challenges to structural integrity, primarily due to volumetric
changes that can lead to detrimental consequences and substantial economic losses. This …

Robust models to predict the secondary compression index of fine-grained soils using multi objective evolutionary polynomial regression analysis

S Alzabeebee, S Keawsawasvong - Modeling Earth Systems and …, 2024 - Springer
The secondary consolidation settlement occurs in soft-grained soils after the primary
consolidation settlement. This settlement affects the serviceability performance of surface …