Invasive Weed Optimization Technique-Based ANN to the Prediction of Rock Tensile Strength

Lei Huang,Danial Jahed Armaghani,Panagiotis G Asteris,Mohammadreza Koopialipoor,M M Tahir

doi:10.3390/app9245372

Abstract

In many site investigation phases of civil and mining engineering projects, the tensile strength of the rocks is one of the most significant parameters that must be identified. This parameter can be determined directly through laboratory tests. However, conducting such laboratory tests is costly and time consuming. In this paper, a new artificial neural network (ANN)-based model is developed to predict rock tensile strength, using the invasive weed optimization (IWO) technique. Granite samples for the purpose of this research were selected from a tunnel located in Malaysia and underwent appropriate laboratory tests (i.e., Schmidt hammer, point load, dry density, as well as the Brazilian tensile strength (BTS) as system output). A simple regression analysis was carried out, and the obtained results confirmed the need for developing a model with multiple inputs, rather than one with only a single input, in order to predict BTS values. Aiming to highlight the capability of an IWO-ANN model in estimating BTS, artificial bee colony (ABC)-ANN and imperialism competitive algorithm (ICA)-ANN were also applied and developed. The parameters required for the ANN-based models were obtained using different parametric studies. According to calculated performance indices, a new hybrid IWO-ANN model can provide a higher accuracy level for the prediction of BTS compared to the ABC-ANN and ICA-ANN models. The results showed that the IWO-ANN model is a suitable alternative solution for a robust and reliable engineering design.

Highlights

Rock tensile strength (TS) is extensively used as a significant parameter when designing a geotechnical construction, such as a tunnel [1]
The present paper develops three hybrid models for the models, invasive weed optimization (IWO), imperialist competitive algorithm (ICA), and artificial bee colony (ABC) are responsible for exploring global minimum; after that, artificial neural network (ANN) chooses it purpose of predicting TS: IWO-ANN, ICA-ANN, and ABC-ANN
Three neurons were utilized within the input layer, i.e., Rn, dry density (DD), and Is50, while the Brazilian tensile strength (BTS) values were utilized in the output layer

Summary

Introduction

Rock tensile strength (TS) is extensively used as a significant parameter when designing a geotechnical construction, such as a tunnel [1]. Literature is consisted of numerous methods attempting to predict the TS value, either directly or indirectly. The researchers or practitioners have to either make use of the empirical equations already present in the relevant literature, or gather rock specimens and test them in laboratory [1,2,3]. In the indirect approach, the whole process can be made faster, simpler, and less costly by predicting the TS value with the help of other, less demanding laboratory experiments, e.g., the Schmidt hammer, P-wave velocity, density, and point load tests [1,4,5]. The list can be further elaborated with the Brazilian tensile strength (BTS) method, which has been proposed by the International Society for Rock Mechanics (ISRM) as an efficient method for determining TS [6]

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Results

Conclusion