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A scientometrics review of soil properties prediction using soft computing approaches
In this world, several types of soils are available with their different engineering properties.
Determining each soil's engineering properties is difficult because the laboratory procedures …
Determining each soil's engineering properties is difficult because the laboratory procedures …
Assessment of the ground vibration during blasting in mining projects using different computational approaches
The investigation compares the conventional, advanced machine, deep, and hybrid learning
models to introduce an optimum computational model to assess the ground vibrations …
models to introduce an optimum computational model to assess the ground vibrations …
[HTML][HTML] Prediction of compaction parameters for fine-grained soil: Critical comparison of the deep learning and standalone models
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 …
parameters of soil is presented in this research. One hundred and ninety and fifty-three soil …
Hybrid soft computing models for predicting unconfined compressive strength of lime stabilized soil using strength property of virgin cohesive soil
This work introduces an optimal performance model for predicting the unconfined
compressive strength (UCS) of lime-stabilized soil using the machine (ensemble tree (ET) …
compressive strength (UCS) of lime-stabilized soil using the machine (ensemble tree (ET) …
Estimation of settlement of pile group in clay using soft computing techniques
The present research introduces an optimum performance soft computing model by
comparing deep (multi-layer perceptron neural network, support vector machine, least …
comparing deep (multi-layer perceptron neural network, support vector machine, least …
Prediction of UCS of fine-grained soil based on machine learning part 2: comparison between hybrid relevance vector machine and Gaussian process regression
The present research employs the models based on the relevance vector machine (RVM)
approach to predict the unconfined compressive strength (UCS) of the cohesive virgin (fine …
approach to predict the unconfined compressive strength (UCS) of the cohesive virgin (fine …
Assessment of hydraulic conductivity of compacted clayey soil using artificial neural network: An investigation on structural and database multicollinearity
This work reveals the effect of hidden layers (HL) and neurons (N) on the performance of
artificial neural network (ANN) models in predicting clayey soil's hydraulic conductivity (K) …
artificial neural network (ANN) models in predicting clayey soil's hydraulic conductivity (K) …
Prediction of uniaxial strength of rocks using relevance vector machine improved with dual kernels and metaheuristic algorithms
The uniaxial compressive strength (UCS) is an essential parameter to study rock
characteristics, determined by direct and indirect methods. However, the direct methods of …
characteristics, determined by direct and indirect methods. However, the direct methods of …
Assessment of the uniaxial compressive strength of intact rocks: An extended comparison between machine and advanced machine learning models
Rock strength is the most deterministic parameter for studying geological disasters in
resource development and underground engineering construction. However, the …
resource development and underground engineering construction. However, the …
Estimation of intact rock uniaxial compressive strength using advanced machine learning
The present investigation introduces an optimal computational model by comparing gene
expression programming (GEP), least square support vector machine (LSSVM), and …
expression programming (GEP), least square support vector machine (LSSVM), and …