Applying Statistical Analysis and Machine Learning for Modeling the UCS from P-Wave Velocity, Density and Porosity on Dry Travertine

Manuel Saldaña,Matías Jeldres,Javier González,Ignacio Pérez-Rey,Norman Toro

doi:10.3390/app10134565

Abstract

In the rock mechanics and rock engineering field, the strength parameter considered to characterize the rock is the uniaxial compressive strength (UCS). It is usually determined in the laboratory through a few statistically representative numbers of specimens, with a recommended minimum of five. The UCS can also be estimated from rock index properties, such as the effective porosity, density, and P-wave velocity. In the case of a porous rock such as travertine, the random distribution of voids inside the test specimen (not detectable in the density-porosity test, but in the compressive strength test) causes large variations on the UCS value, which were found in the range of 62 MPa for this rock. This fact complicates a sufficiently accurate determination of experimental results, also affecting the estimations based on regression analyses. Aiming to solve this problem, statistical analysis, and machine learning models (artificial neural network) was developed to generate a reliable predictive model, through which the best results for a multiple regression model between uniaxial compressive strength (UCS), P-wave velocity and porosity were obtained.

Highlights

The uniaxial compressive strength (UCS) is one of the most important parameters used in the rock mechanics and rock engineering field for design
The determination of UCS in the laboratory can be time-consuming and, in some cases, unaffordable, due to the specimen preparation often requires relatively high-quality cores in order to fulfill geometric and surface specifications, as recommended by the ISRM [5] or ASTM [10]. This may be one of the reasons behind the research of correlations between this parameter and others come from non-destructive methods like P-wave velocity
The aim of this paper is to develop a comparative analysis between conventional statistical fitting methods and the machine learning (ANN) of the UCS of travertine, by means of an experimental correlation between UCS and vp, ρdry and n for a minimum of stress tests in a controlled environment

Summary

Introduction

The uniaxial compressive strength (UCS) is one of the most important parameters used in the rock mechanics and rock engineering field for design. The determination of UCS in the laboratory can be time-consuming and, in some cases, unaffordable, due to the specimen preparation often requires relatively high-quality cores in order to fulfill geometric and surface specifications, as recommended by the ISRM [5] or ASTM [10]. This may be one of the reasons behind the research of correlations between this parameter and others come from non-destructive methods like P-wave velocity (vp ). The UCS depends mainly on ρ and n [17], and on vp , so many researchers have determined different

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Conclusion