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 …

Machine learning techniques for structural health monitoring of heritage buildings: A state-of-the-art review and case studies

M Mishra - Journal of Cultural Heritage, 2021 - Elsevier
This paper performed a systematic review of the various machine learning (ML) techniques
applied to assess the health condition of heritage buildings. More robust predictive models …

Introducing stacking machine learning approaches for the prediction of rock deformation

M Koopialipoor, PG Asteris, AS Mohammed… - Transportation …, 2022 - Elsevier
Accurate and reliable predictions of rock deformations are crucial in many rock-based
projects in civil and mining engineering. In this research, a new system for the prediction of …

Closed-form equation for estimating unconfined compressive strength of granite from three non-destructive tests using soft computing models

AD Skentou, A Bardhan, A Mamou, ME Lemonis… - Rock Mechanics and …, 2023 - Springer
The use of three artificial neural network (ANN)-based models for the prediction of
unconfined compressive strength (UCS) of granite using three non-destructive test …

[HTML][HTML] Application of artificial intelligence to rock mechanics: An overview

AI Lawal, S Kwon - Journal of Rock Mechanics and Geotechnical …, 2021 - Elsevier
Different artificial intelligence (AI) methods have been applied to various aspects of rock
mechanics, but the fact that none of these methods have been used as a standard implies …

Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks

E Momeni, DJ Armaghani, M Hajihassani, MFM Amin - Measurement, 2015 - Elsevier
Many attempts have been made to predict unconfined compressive strength (UCS) of rocks
using back-propagation (BP) artificial neural network (ANN). However, BP-ANN suffers from …

Application of Six Metaheuristic Optimization Algorithms and Random Forest in the uniaxial compressive strength of rock prediction

J Li, C Li, S Zhang - Applied Soft Computing, 2022 - Elsevier
The uniaxial compressive strength (UCS) is one of the most important parameters for
judging the mechanical behaviour of rock mass in rock engineering design and excavation …

Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks

PG Asteris, A Mamou, M Hajihassani… - Transportation …, 2021 - Elsevier
This paper reports the results of soft computing-based models correlating L and N-type
Schmidt hammer rebound numbers of rock. A data-independent database was compiled …

Artificial intelligence forecasting models of uniaxial compressive strength

A Mahmoodzadeh, M Mohammadi, HH Ibrahim… - Transportation …, 2021 - Elsevier
The uniaxial compressive strength (UCS) is a vital rock geomechanical parameter widely
used in rock engineering projects such as tunnels, dams, and rock slope stability. Since the …

A new empirical formula for evaluating uniaxial compressive strength using the Schmidt hammer test

M Wang, W Wan - International Journal of Rock Mechanics and Mining …, 2019 - Elsevier
The uniaxial compressive strength (UCS) of rock is an important geotechnical parameter for
engineering applications. However, how to determine the UCS simply and accurately has …