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Neural tree for estimating the uniaxial compressive strength of rock materials

Ojha, V. ORCID: https://orcid.org/0000-0002-9256-1192 and Amban Mishra, D. (2018) Neural tree for estimating the uniaxial compressive strength of rock materials. In: International Conference on Hybrid Intelligent Systems, pp. 1-10, https://doi.org/10.1007/978-3-319-76351-4_1.

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To link to this item DOI: 10.1007/978-3-319-76351-4_1

Abstract/Summary

Uniaxial Compressive Strength (UCS) is the most important parameter that quantifies the rock strength. However, determination of the UCS in the laboratory is very expensive and time-consuming. Therefore, common index tests like point load (Is-50), ultrasonic velocity test (VP), block punch index (BPI) test, rebound hardness (SRH) test, physical properties, etc., have been used to predict the UCS. The objective of this work is to develop a predictive model using a neural tree predictor that estimate the UCS with high accuracy and assess the effectiveness of different index tests in predicting the UCS of rock materials. UCS and indices such as BPI, Is-50, SRH, VP, effective porosity and density were determined for the granite, schist, and sandstone. The constructed model predicted the UCS with high accuracy and in a quick time (9 seconds). Additionally, the destructive mechanical rock indices BPI and Is-50 proved to be the best index tests to estimate the UCS.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:93553
Publisher:Springer International Publishing

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