Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks
This paper reports the results of soft computing-based models correlating L and N-type Schmidt hammer rebound numbers of rock. A data-independent database was compiled from available measurements reported in the literature, which was used to train and develop back propagating neural networks, geneti...
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Transportation Geotechnics
2021
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Truy cập trực tuyến: | https://www.sciencedirect.com/science/article/abs/pii/S2214391221000787?via%3Dihub https://dlib.phenikaa-uni.edu.vn/handle/PNK/2836 https://doi.org/10.1016/j.trgeo.2021.100588 |
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oai:localhost:PNK-28362022-08-17T05:54:47Z Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks Panagiotis G.Asteris Anna Mamoua Mohse Hajihassani Mahdi Hasanipanah Mohammadreza Koopialipoor Tien-Thinh Le Navid Kardanig Danial J.Armaghanih Artificial neural networks Genetic programming Machine learning This paper reports the results of soft computing-based models correlating L and N-type Schmidt hammer rebound numbers of rock. A data-independent database was compiled from available measurements reported in the literature, which was used to train and develop back propagating neural networks, genetic programming and least square method models for the prediction of L-type Schmidt hammer rebound numbers. The results show that the highest predictive accuracy was obtained for the neural network model, which predicts the L type Schmidt hammer rebound number, with less than ±20% deviation from the experimental data for 97.27% of the samples. The optimum neural network is presented as a closed form equation and is also incorporated into an Excel-based graphical user interface, which directly calculates the Rn(L) number for any input Rn(N) = 12.40–75.97 and which is made available as supplementary material. 2021-09-13T04:24:49Z 2021-09-13T04:24:49Z 2021 Bài trích https://www.sciencedirect.com/science/article/abs/pii/S2214391221000787?via%3Dihub https://dlib.phenikaa-uni.edu.vn/handle/PNK/2836 https://doi.org/10.1016/j.trgeo.2021.100588 eng Transportation Geotechnics |
institution |
Digital Phenikaa |
collection |
Digital Phenikaa |
language |
eng |
topic |
Artificial neural networks Genetic programming Machine learning |
spellingShingle |
Artificial neural networks Genetic programming Machine learning Panagiotis G.Asteris Anna Mamoua Mohse Hajihassani Mahdi Hasanipanah Mohammadreza Koopialipoor Tien-Thinh Le Navid Kardanig Danial J.Armaghanih Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks |
description |
This paper reports the results of soft computing-based models correlating L and N-type Schmidt hammer rebound numbers of rock. A data-independent database was compiled from available measurements reported in the literature, which was used to train and develop back propagating neural networks, genetic programming and least square method models for the prediction of L-type Schmidt hammer rebound numbers. The results show that the highest predictive accuracy was obtained for the neural network model, which predicts the L type Schmidt hammer rebound number, with less than ±20% deviation from the experimental data for 97.27% of the samples. The optimum neural network is presented as a closed form equation and is also incorporated into an Excel-based graphical user interface, which directly calculates the Rn(L) number for any input Rn(N) = 12.40–75.97 and which is made available as supplementary material. |
format |
Bài trích |
author |
Panagiotis G.Asteris Anna Mamoua Mohse Hajihassani Mahdi Hasanipanah Mohammadreza Koopialipoor Tien-Thinh Le Navid Kardanig Danial J.Armaghanih |
author_facet |
Panagiotis G.Asteris Anna Mamoua Mohse Hajihassani Mahdi Hasanipanah Mohammadreza Koopialipoor Tien-Thinh Le Navid Kardanig Danial J.Armaghanih |
author_sort |
Panagiotis G.Asteris |
title |
Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks |
title_short |
Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks |
title_full |
Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks |
title_fullStr |
Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks |
title_full_unstemmed |
Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks |
title_sort |
soft computing based closed form equations correlating l and n-type schmidt hammer rebound numbers of rocks |
publisher |
Transportation Geotechnics |
publishDate |
2021 |
url |
https://www.sciencedirect.com/science/article/abs/pii/S2214391221000787?via%3Dihub https://dlib.phenikaa-uni.edu.vn/handle/PNK/2836 https://doi.org/10.1016/j.trgeo.2021.100588 |
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1751856264869052416 |
score |
8.891145 |