Developing accurate soft computing methods for groundwater level (GWL) forecasting is
essential for enhancing the planning and management of water resources. Over the past two …

Geotechnical engineering deals with materials (eg soil and rock) that, by their very nature,
exhibit varied and uncertain behavior due to the imprecise physical processes associated …

A comparison between deep learning and standalone models in predicting the compaction
parameters of soil is presented in this research. One hundred and ninety and fifty-three soil …

The pile bearing capacity is considered as the most essential factor in designing deep
foundations. Direct determination of this parameter in site is costly and difficult. Hence, this …

The present research introduces an optimum performance soft computing model by
comparing deep (multi-layer perceptron neural network, support vector machine, least …

This paper presents a system probabilistic stability evaluation method for slopes based on
Gaussian process regression (GPR) and Latin hypercube sampling. The analysis is …

Forecasting of drought can be very useful in preparing to reduce its impacts, especially in
the agricultural sector. Three machine learning models of MLP neural network, GRNN …

Accurate estimation of the bearing capacity of piles requires complex modelling techniques
which are not justified by timeframe, budget, or scope of the projects. In this study, six …

Nowadays, strengthening of reinforced concrete structures with a new class of sustainable
materials is the possible solution to retrofit the aged deteriorated structures. It is difficult to …

Optimization techniques have drawn much attention for solving geotechnical engineering
problems in recent years. Particle swarm optimization (PSO) is one of the most widely used …