A hybrid machine learning approach in prediction and uncertainty quantification of ultimate compressive strength of RCFST columns

MST Nguyen, SE Kim - Construction and Building Materials, 2021 - Elsevier
This article introduces a machine learning-based approach to estimate the ultimate
compressive strength of rectangular concrete-filled steel tube (RCFST) columns, and to …

Evaluation of geological conditions and clogging of tunneling using machine learning

XD Bai, WC Cheng, DEL Ong, G Li - Geomechanics and …, 2021 - koreascience.kr
There frequently exists inadequacy regarding the number of boreholes installed along
tunnel alignment. While geophysical imaging techniques are available for pre-tunnelling …

Hybrid ELM and MARS-based prediction model for bearing capacity of shallow foundation

M Kumar, V Kumar, R Biswas, P Samui, MR Kaloop… - Processes, 2022 - mdpi.com
The nature of soil varies horizontally as well as vertically, owing to the process of the
formation of soil. Thus, ensuring the safe design of geotechnical structures has been a major …

Estimating the unconfined compression strength of low plastic clayey soils using gene-expression programming

MN Nawaz, SH Chong, MM Nawaz… - Geomechanics and …, 2023 - koreascience.kr
The unconfined compression strength (UCS) of soils is commonly used either before or
during the construction of geo-structures. In the pre-design stage, UCS as a mechanical …

Machine learning models for predicting shear strength and identifying failure modes of rectangular RC columns

VT Phan, VL Tran, VQ Nguyen, DD Nguyen - Buildings, 2022 - mdpi.com
The determination of shear strength and the identification of potential failure modes are the
crucial steps in designing and evaluating the structural performance of reinforced concrete …

New model to predict bearing capacity of shallow foundations resting on cohesionless soil

S Alzabeebee, YMA Alshkane… - Geotechnical and …, 2023 - Springer
Predicting the bearing capacity is one of the tasks that geotechnical engineers do on a daily
basis, yet the accuracy of the available methods needs to be further improved. This paper …

Hybrid BART-based models optimized by nature-inspired metaheuristics to predict ultimate axial capacity of CCFST columns

NV Luat, J Shin, K Lee - Engineering with Computers, 2022 - Springer
The goal of this study was to investigate a novel approach of predicting the ultimate capacity
of axially loaded circular concrete-filled steel tube (CCFST) columns. A hybrid intelligent …

Genetic algorithm hybridized with eXtreme gradient boosting to predict axial compressive capacity of CCFST columns

NV Luat, SW Han, K Lee - Composite Structures, 2021 - Elsevier
The study aimed to propose a robust method for predicting the axial compressive capacity
(N u) of circular concrete-filled steel tube (CCFST) columns. For this purpose, a hybrid …

[PDF][PDF] A long-term tunnel settlement prediction model based on BO-GPBE with SHM data

Y Ding, YJ Wei, PS **, PP Ang, Z Han - Smart Struct. Syst, 2024 - researchgate.net
The new metro crossing the existing metro will cause the settlement or floating of the existing
structures, which will have safety problems for the operation of the existing metro and the …

Development of an optimized model to compute the undrained shaft friction adhesion factor of bored piles

S Alzabeebee, AA Zuhaira… - Geomechanics and …, 2022 - koreascience.kr
Accurate prediction of the undrained shaft resistance is essential for robust design of bored
piles in undrained condition. The undrained shaft resistance is calculated using the …