Closed-Form Equation for Estimating Unconfined Compressive Strength of Granite from Three Non-destructive Tests Using Soft Computing Models

TLDR

The study investigates predicting granite unconfined compressive strength using three artificial neural network models trained on pulse velocity, Schmidt hammer rebound number, and effective porosity data. A dataset of 274 samples was assembled to train and validate three ANN architectures—Levenberg–Marquardt, particle swarm optimization, and imperialist competitive algorithm—and a graphical user interface was created to deploy the best model. The Levenberg–Marquardt ANN achieved the highest accuracy (R = 0.9607, RMSE = 14.8272) and outperformed existing literature models, with the GUI enabling practical UCS estimation.

Abstract

Abstract The use of three artificial neural network (ANN)-based models for the prediction of unconfined compressive strength (UCS) of granite using three non-destructive test indicators, namely pulse velocity, Schmidt hammer rebound number, and effective porosity, has been investigated in this study. For this purpose, a sum of 274 datasets was compiled and used to train and validate three ANN models including ANN constructed using Levenberg–Marquardt algorithm (ANN-LM), a combination of ANN and particle swarm optimization (ANN-PSO), and a combination of ANN and imperialist competitive algorithm (ANN-ICA). The constructed ANN-LM model was proven to be the most accurate based on experimental findings. In the validation phase, the ANN-LM model has achieved the best predictive performance with R = 0.9607 and RMSE = 14.8272. Experimental results show that the developed ANN-LM outperforms a number of existing models available in the literature. Furthermore, a Graphical User Interface (GUI) has been developed which can be readily used to estimate the UCS of granite through the ANN-LM model. The developed GUI is made available as a supplementary material.