Application of artificial intelligence in geotechnical engineering: A state-of-the-art review

A Baghbani, T Choudhury, S Costa, J Reiner - Earth-Science Reviews, 2022 - Elsevier
Geotechnical engineering deals with soils and rocks and their use in engineering
constructions. By their nature, soils and rocks exhibit complex behaviours and a high level of …
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D Asmare - Scientific African, 2023 - Elsevier
The present research was conducted around Choke Mountain, East Gojjam, Northwestern
Ethiopia. This study aimed to apply and validate analytical hierarchy process (AHP) and …

Comparison of tree-based machine learning algorithms for predicting liquefaction potential using canonical correlation forest, rotation forest, and random forest based …

S Demir, EK Sahin - Soil Dynamics and Earthquake Engineering, 2022 - Elsevier
This research investigates and compares the performance of three tree-based Machine
Learning (ML) methods, Canonical Correlation Forest (CCF), Rotation Forest (RotFor), and …

Evaluation of soil liquefaction using AI technology incorporating a coupled ENN/t-SNE model

PGA Njock, SL Shen, A Zhou, HM Lyu - Soil Dynamics and earthquake …, 2020 - Elsevier
This paper presents a new evolutionary neural network (ENN) algorithm coupled with the
dimensionality reduction technique 't-distributed stochastic neighbour embedding'(t-SNE) …

An exploration of the use of machine learning to predict lateral spreading

MG Durante, EM Rathje - Earthquake Spectra, 2021 - journals.sagepub.com
The recent availability of large amounts of high-quality data from post-disaster field
reconnaissance enables an exploration of the use of machine learning (ML) approaches to …

Why “AI” models for predicting soil liquefaction have been ignored, plus some that shouldn't be

BW Maurer, MD Sanger - Earthquake Spectra, 2023 - journals.sagepub.com
Soil liquefaction remains an important and interesting problem that has attracted the
development of enumerable prediction models. Increasingly, these models are utilizing …

An extreme learning machine approach for slope stability evaluation and prediction

Z Liu, J Shao, W Xu, H Chen, Y Zhang - Natural hazards, 2014 - Springer
This paper presents slope stability evaluation and prediction with the approach of a fast
robust neural network named the extreme learning machine (ELM). The circular failure …