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

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Faculty of Engineering and Architecture, Universidad Arturo Prat, Almirante Juan José Latorre 2901, Antofagasta 1244260, Chile
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Department of Metallurgical and Mining Engineering, Universidad Católica del Norte, Avenida Angamos 0610, Antofagasta 1270709, Chile
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Geotechnical Laboratory, CEDEX, 28014 Madrid, Spain
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Departamento de Ingeniería Química y Procesos de Minerales, Facultad de Ingeniería, Universidad de Antofagasta, Antofagasta 1240000, Chile
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Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(13), 4565; https://doi.org/10.3390/app10134565
Received: 31 May 2020 / Revised: 24 June 2020 / Accepted: 25 June 2020 / Published: 30 June 2020
(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)
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. View Full-Text
Keywords: travertine; P-wave velocity; uniaxial compressive strength; neural networks; regression analysis travertine; P-wave velocity; uniaxial compressive strength; neural networks; regression analysis
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Saldaña, M.; González, J.; Pérez-Rey, I.; Jeldres, M.; Toro, N. Applying Statistical Analysis and Machine Learning for Modeling the UCS from P-Wave Velocity, Density and Porosity on Dry Travertine. Appl. Sci. 2020, 10, 4565.

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