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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 …
Prediction of ultimate bearing capacity of shallow foundations on cohesionless soil using hybrid LSTM and RVM approaches: An extended investigation of …
This research presents the optimum performance model for predicting the shallow
foundation ultimate bearing capacity (UBC). Twenty-one models are employed, trained …
foundation ultimate bearing capacity (UBC). Twenty-one models are employed, trained …
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 …
CBR prediction of pavement materials in unsoaked condition using LSSVM, LSTM-RNN, and ANN approaches
The present research introduces the best architecture model for predicting the unsoaked
California bearing ratio (CBRu) of soil by comparing the models based on the least square …
California bearing ratio (CBRu) of soil by comparing the models based on the least square …
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 …
Prediction of UCS of fine-grained soil based on machine learning part 1: multivariable regression analysis, gaussian process regression, and gene expression …
The present research introduces the best architecture approach and model for predicting the
unconfined compressive strength (UCS) of cohesive virgin soil by comparing the …
unconfined compressive strength (UCS) of cohesive virgin soil by comparing the …
Estimation of California bearing ratio for hill highways using advanced hybrid artificial neural network algorithms
California bearing ratio (CBR) is one of the important parameters that is used to express the
strength of the pavement subgrade of railways, roadways, and airport runways. CBR is …
strength of the pavement subgrade of railways, roadways, and airport runways. CBR is …
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 …