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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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Main Authors:	Panagiotis G.Asteris, Anna Mamoua, Mohse Hajihassani, Mahdi Hasanipanah, Mohammadreza Koopialipoor, Tien-Thinh Le, Navid Kardanig, Danial J.Armaghanih
Format:	Bài trích
Language:	eng
Published:	Transportation Geotechnics 2021
Subjects:	Artificial neural networks Genetic programming Machine learning
Online Access:	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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spelling	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
_version_	1751856264869052416
score	8.891145

Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks

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