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A platform for research: civil engineering, architecture and urbanism
Correlating the Unconfined Compressive Strength of Rock with the Compressional Wave Velocity Effective Porosity and Schmidt Hammer Rebound Number Using Artificial Neural Networks
https://orcid.org/0000-0002-7142-4981
Abstract In this research, a series of artificial neural networks for the prediction of the unconfined compressive strength of rock were trained and developed. A data and site independent database were compiled from 367 datasets reported in the literature, using the Schmidt hammer number Rn, compressional wave velocity Vp, and effective porosity ne as input parameters. Different types of Schmidt hammer numbers were consolidated using the artificial neural network developed by the authors, which correlates N with L-type Schmidt hammer numbers with less than ± 20% deviation from the experimental data for 97.27% of the specimens. Of the various soft computing models developed in this study (ANN-LM, ANN-PSO, and ANN-ICA), the highest accuracy was obtained with the ANN-ICA, which predicts the unconfined compressive strength of various rock types and formation methods with less than ± 20% deviation from the experimental data for 86.36%. The closed-form equation of the ANN-ICA model is incorporated into a graphical user interface, which is made available as supplementary material, allowing the verification of the reported results by different researchers.
Highlights The unconfined compressive strength of rock was correlated with the compressional wave velocity, effective porosity and Schmidt hammer rebound.A parametric analysis was performed to obtain a suitable architecture for the artificial neural network.Metaheuristic-optimization techniques were applied to optimize the weights and biases of the artificial neural network model.The prediction accuracy of the hybrid machine learning model developed in this research is higher than that currently reported in the literature.The optimum developed artificial neural network is presented as a closed-form analytical equation, which has also been integrated into a graphical user interface and made available as supplementary material
Correlating the Unconfined Compressive Strength of Rock with the Compressional Wave Velocity Effective Porosity and Schmidt Hammer Rebound Number Using Artificial Neural Networks
https://orcid.org/0000-0002-7142-4981
Abstract In this research, a series of artificial neural networks for the prediction of the unconfined compressive strength of rock were trained and developed. A data and site independent database were compiled from 367 datasets reported in the literature, using the Schmidt hammer number Rn, compressional wave velocity Vp, and effective porosity ne as input parameters. Different types of Schmidt hammer numbers were consolidated using the artificial neural network developed by the authors, which correlates N with L-type Schmidt hammer numbers with less than ± 20% deviation from the experimental data for 97.27% of the specimens. Of the various soft computing models developed in this study (ANN-LM, ANN-PSO, and ANN-ICA), the highest accuracy was obtained with the ANN-ICA, which predicts the unconfined compressive strength of various rock types and formation methods with less than ± 20% deviation from the experimental data for 86.36%. The closed-form equation of the ANN-ICA model is incorporated into a graphical user interface, which is made available as supplementary material, allowing the verification of the reported results by different researchers.
Highlights The unconfined compressive strength of rock was correlated with the compressional wave velocity, effective porosity and Schmidt hammer rebound.A parametric analysis was performed to obtain a suitable architecture for the artificial neural network.Metaheuristic-optimization techniques were applied to optimize the weights and biases of the artificial neural network model.The prediction accuracy of the hybrid machine learning model developed in this research is higher than that currently reported in the literature.The optimum developed artificial neural network is presented as a closed-form analytical equation, which has also been integrated into a graphical user interface and made available as supplementary material
Correlating the Unconfined Compressive Strength of Rock with the Compressional Wave Velocity Effective Porosity and Schmidt Hammer Rebound Number Using Artificial Neural Networks
https://orcid.org/0000-0002-7142-4981
Abstract In this research, a series of artificial neural networks for the prediction of the unconfined compressive strength of rock were trained and developed. A data and site independent database were compiled from 367 datasets reported in the literature, using the Schmidt hammer number Rn, compressional wave velocity Vp, and effective porosity ne as input parameters. Different types of Schmidt hammer numbers were consolidated using the artificial neural network developed by the authors, which correlates N with L-type Schmidt hammer numbers with less than ± 20% deviation from the experimental data for 97.27% of the specimens. Of the various soft computing models developed in this study (ANN-LM, ANN-PSO, and ANN-ICA), the highest accuracy was obtained with the ANN-ICA, which predicts the unconfined compressive strength of various rock types and formation methods with less than ± 20% deviation from the experimental data for 86.36%. The closed-form equation of the ANN-ICA model is incorporated into a graphical user interface, which is made available as supplementary material, allowing the verification of the reported results by different researchers.
Highlights The unconfined compressive strength of rock was correlated with the compressional wave velocity, effective porosity and Schmidt hammer rebound.A parametric analysis was performed to obtain a suitable architecture for the artificial neural network.Metaheuristic-optimization techniques were applied to optimize the weights and biases of the artificial neural network model.The prediction accuracy of the hybrid machine learning model developed in this research is higher than that currently reported in the literature.The optimum developed artificial neural network is presented as a closed-form analytical equation, which has also been integrated into a graphical user interface and made available as supplementary material
Correlating the Unconfined Compressive Strength of Rock with the Compressional Wave Velocity Effective Porosity and Schmidt Hammer Rebound Number Using Artificial Neural Networks
Le, Tien-Thinh (author) / Skentou, Athanasia D. (author) / Mamou, Anna (author) / Asteris, Panagiotis G. (author)
2022
Article (Journal)
Electronic Resource
English
BKL:
38.58
Geomechanik
/
56.20
Ingenieurgeologie, Bodenmechanik
/
38.58$jGeomechanik
/
56.20$jIngenieurgeologie$jBodenmechanik
RVK:
ELIB41
Schmidt Rebound Hammer Test for Rock Characterization
| British Library Conference Proceedings | 1997
| British Library Online Contents | 2015