Open Access

ARTICLE

Rock Strength Estimation Using Several Tree-Based ML Techniques

Zida Liu¹, Danial Jahed Armaghani^2,*, Pouyan Fakharian³, Diyuan Li⁴, Dmitrii Vladimirovich Ulrikh⁵, Natalia Nikolaevna Orekhova⁶, Khaled Mohamed Khedher^7,8

1 School of Resources and Safety Engineering, Central South University, Changsha, 410083, China
2 Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, Chelyabinsk, 454080, Russia
3 Faculty of Civil Engineering, Semnan University, Semnan, 35131-19111, Iran
4 School of Resources and Safety Engineering, Central South University, Changsha, 410083, China
5 Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, Chelyabinsk, 454080, Russia
6 Department of Geology, Mine Surveying and Mineral Processing, Nosov Magnitogorsk State Technical University, Magnitogorsk, 455000, Russia
7 Department of Civil Engineering, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia
8 Department of Civil Engineering, High Institute of Technological Studies, Mrezgua University Campus, Nabeul, 8000, Tunisia

* Corresponding Author: Danial Jahed Armaghani. Email:

(This article belongs to the Special Issue: Soft Computing Techniques in Materials Science and Engineering)

Computer Modeling in Engineering & Sciences 2022, 133(3), 799-824. https://doi.org/10.32604/cmes.2022.021165

Received 30 December 2021; Accepted 21 April 2022; Issue published 03 August 2022

Abstract

The uniaxial compressive strength (UCS) of rock is an essential property of rock material in different relevant applications, such as rock slope, tunnel construction, and foundation. It takes enormous time and effort to obtain the UCS values directly in the laboratory. Accordingly, an indirect determination of UCS through conducting several rock index tests that are easy and fast to carry out is of interest and importance. This study presents powerful boosting trees evaluation framework, i.e., adaptive boosting machine, extreme gradient boosting machine (XGBoost), and category gradient boosting machine, for estimating the UCS of sandstone. Schmidt hammer rebound number, P-wave velocity, and point load index were chosen as considered factors to forecast UCS values of sandstone samples. Taylor diagrams and five regression metrics, including coefficient of determination (R²), root mean square error, mean absolute error, variance account for, and A-20 index, were used to evaluate and compare the performance of these boosting trees. The results showed that the proposed boosting trees are able to provide a high level of prediction capacity for the prepared database. In particular, it was worth noting that XGBoost is the best model to predict sandstone strength and it achieved 0.999 training R² and 0.958 testing R². The proposed model had more outstanding capability than neural network with optimization techniques during training and testing phases. The performed variable importance analysis reveals that the point load index has a significant influence on predicting UCS of sandstone.

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Keywords

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