Utilization of Machine-Learning-Based model Hybridized with Meta-Heuristic Frameworks for estimation of Unconfined Compressive Strength

She  Wang; Qi  Zhang

doi:10.6180/jase.202508_28(8).0015

Utilization of Machine-Learning-Based model Hybridized with Meta-Heuristic Frameworks for estimation of Unconfined Compressive Strength

Electrical Engineering Computer Science and Information Engineering

She Wang¹This email address is being protected from spambots. You need JavaScript enabled to view it. and Qi Zhang²

¹School of Computer and Electronic Information Engineering, Wuhan City Polytechnic, Wuhan 430000, Hubei, China

²Department of Commerce and Trade, Wuhan Instrument and Electronic Technical School, Wuhan 430205, Hubei, China

Received: July 7, 2024
Accepted: September 30, 2024
Publication Date: November 16, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202508_28(8).0015

Unconfined compressive strength (UCS) is one of the rocks’ most valuable mechanical properties in constructing an accurate geo-mechanical model. It has traditionally been determined through laboratory core sample testing or by analysis of well-log data. After a great deal of effort and growing investment in time, the proper adoption of machine learning methods, especially the radial basis function (RBF), opens a route to promising alternatives against empirical methods for better real-time prediction of UCS. The current study considers the RBF-based machine learning model, whose parameters have been optimized using two enhanced meta heuristic frameworks: Improved Arithmetic Optimization Algorithm (IAOA) and Flying Foxes Optimization (FFO). Based on an extensive dataset already used in previous studies and applying some soft computing techniques, vigorous performance metrics such as RMSE, R², MAE, U95, and MNB were used to test the developed frameworks. The outcomes indicate a significant outperformance of the hybrid RBFF technique over the solo RBF and RBF-IA frameworks. Specifically, the RBFF model resulted in an R² of 0.998, an RMSE of 1.313, and an MNBof-0.003, reflecting its better performance in UCS prediction. This study indicates the efficiency of integrating RBF with meta-heuristic optimization to enhance UCS predictions in geotechnical studies.

Keywords: Unconfined compressive strength; Radial Basis Function; Improved Arithmetic optimization algorithm; Flying Foxes Optimization.

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