Cover Image

Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets

The tensile strength (TS) of the rock is one the most key parameters in designing process of foundations and tunnels structures. However, direct techniques for TS determination (laboratory investigations) are not efficient with respect to cost and time. This investigation attempts to develop an inno...

Full description

Saved in:
Bibliographic Details
Main Authors: Harandizadeh, Hooman, Armaghani, Danial Jahed, Mohamad, Edy Tonnizam
Format: Article
Published: Springer-Verlag London Ltd. 2020
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/90580/
http://dx.doi.org/10.1007/s00521-020-04803-z
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.90580
record_format eprints
spelling my.utm.905802021-04-30T14:55:20Z http://eprints.utm.my/id/eprint/90580/ Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets Harandizadeh, Hooman Armaghani, Danial Jahed Mohamad, Edy Tonnizam TA Engineering (General). Civil engineering (General) The tensile strength (TS) of the rock is one the most key parameters in designing process of foundations and tunnels structures. However, direct techniques for TS determination (laboratory investigations) are not efficient with respect to cost and time. This investigation attempts to develop an innovative hybrid intelligent model, i.e. fuzzy-group method of data handling (GMDH) optimized by the gravitational search algorithm (GSA), fuzzy-GMDH-GSA, for prediction of the rock TS. To establish a database, the rock samples collected from a tunnel site were evaluated in the laboratory and a database (with the Schmidt hammer test, dry density test, and point load test as inputs and Brazilian tensile strength, BTS, as output) was prepared for modelling. Then, a fuzzy-GMDH-GSA model was developed to predict BTS of the rock considering the most influential of this predictive model. In addition, a fuzzy model as well as a GMDH model were constructed to predict BTS for comparison purposes. The performances of the proposed predictive models were evaluated by comparing the values of several statistical metrics such as correlation coefficient (R). R values of 0.90, 0.86, and 0.86 were obtained for testing datasets of fuzzy-GMDH-GSA, GMDH, and fuzzy models, respectively, which show that the fuzzy-GMDH-GSA predictive model is able to deliver greater prediction performance compared to other constructed models. The results confirmed the effective role of the GSA, as a powerful optimization algorithm in efficiency of hybrid fuzzy-GMDH-GSA model. Moreover, results of sensitivity analysis showed that the point load index is the most effective input on output of this study. Springer-Verlag London Ltd. 2020-09-01 Article PeerReviewed Harandizadeh, Hooman and Armaghani, Danial Jahed and Mohamad, Edy Tonnizam (2020) Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets. Neural Computing and Applications, 32 (17). pp. 14047-14067. ISSN 0941-0643 http://dx.doi.org/10.1007/s00521-020-04803-z DOI:10.1007/s00521-020-04803-z
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Harandizadeh, Hooman
Armaghani, Danial Jahed
Mohamad, Edy Tonnizam
Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets
description The tensile strength (TS) of the rock is one the most key parameters in designing process of foundations and tunnels structures. However, direct techniques for TS determination (laboratory investigations) are not efficient with respect to cost and time. This investigation attempts to develop an innovative hybrid intelligent model, i.e. fuzzy-group method of data handling (GMDH) optimized by the gravitational search algorithm (GSA), fuzzy-GMDH-GSA, for prediction of the rock TS. To establish a database, the rock samples collected from a tunnel site were evaluated in the laboratory and a database (with the Schmidt hammer test, dry density test, and point load test as inputs and Brazilian tensile strength, BTS, as output) was prepared for modelling. Then, a fuzzy-GMDH-GSA model was developed to predict BTS of the rock considering the most influential of this predictive model. In addition, a fuzzy model as well as a GMDH model were constructed to predict BTS for comparison purposes. The performances of the proposed predictive models were evaluated by comparing the values of several statistical metrics such as correlation coefficient (R). R values of 0.90, 0.86, and 0.86 were obtained for testing datasets of fuzzy-GMDH-GSA, GMDH, and fuzzy models, respectively, which show that the fuzzy-GMDH-GSA predictive model is able to deliver greater prediction performance compared to other constructed models. The results confirmed the effective role of the GSA, as a powerful optimization algorithm in efficiency of hybrid fuzzy-GMDH-GSA model. Moreover, results of sensitivity analysis showed that the point load index is the most effective input on output of this study.
format Article
author Harandizadeh, Hooman
Armaghani, Danial Jahed
Mohamad, Edy Tonnizam
author_facet Harandizadeh, Hooman
Armaghani, Danial Jahed
Mohamad, Edy Tonnizam
author_sort Harandizadeh, Hooman
title Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets
title_short Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets
title_full Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets
title_fullStr Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets
title_full_unstemmed Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets
title_sort development of fuzzy-gmdh model optimized by gsa to predict rock tensile strength based on experimental datasets
publisher Springer-Verlag London Ltd.
publishDate 2020
url http://eprints.utm.my/id/eprint/90580/
http://dx.doi.org/10.1007/s00521-020-04803-z
_version_ 1698696956348465152
score 13.244369

Similar Items