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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 17/2020

27.02.2020 | Original Article

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

verfasst von: Hooman Harandizadeh, Danial Jahed Armaghani, Edy Tonnizam Mohamad

Erschienen in: Neural Computing and Applications | Ausgabe 17/2020

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Abstract

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.
Vorheriger Artikel Multi-attribute group decision-making using double hierarchy hesitant fuzzy linguistic preference information
Nächster Artikel An application of parametric approach for interval differential equation in inventory model for deteriorating items with selling-price-dependent demand

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Metadaten
Titel
Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets
verfasst von
Hooman Harandizadeh
Danial Jahed Armaghani
Edy Tonnizam Mohamad
Publikationsdatum
27.02.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 17/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-04803-z

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