Predicting uniaxial compressive strength of rocks using ANN models: Incorporating porosity, compressional wave velocity, and schmidt hammer data.

Autor: Asteris PG; Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, GR 14121, Athens, Greece. Electronic address: asteris@aspete.gr., Karoglou M; School of Chemical Engineering, National Technical University of Athens, Zografou Campus, 15780 Athens, Greece. Electronic address: margo@central.ntua.gr., Skentou AD; Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, GR 14121, Athens, Greece. Electronic address: athanasiaskentou@hotmail.gr., Vasconcelos G; ISISE, Department of Civil Engineering, University of Minho, Portugal. Electronic address: graca@civil.uminho.pt., He M; State Key Laboratory of Eco-Hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an 710048, China. Electronic address: hemingming@xaut.edu.cn., Bakolas A; School of Chemical Engineering, National Technical University of Athens, Zografou Campus, 15780 Athens, Greece. Electronic address: abakolas@mail.ntua.gr., Zhou J; School of Resources and Safety Engineering, Central South University, Changsha 410083, China. Electronic address: j.zhou@csu.edu.cn., Armaghani DJ; School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia. Electronic address: danial.jahedarmaghani@uts.edu.au.
Jazyk: angličtina
Zdroj: Ultrasonics [Ultrasonics] 2024 Jul; Vol. 141, pp. 107347. Date of Electronic Publication: 2024 May 20.
DOI: 10.1016/j.ultras.2024.107347
Abstrakt: The unconfined compressive strength (UCS) of intact rocks is crucial for engineering applications, but traditional laboratory testing is often impractical, especially for historic buildings lacking sufficient core samples. Non-destructive tests like the Schmidt hammer rebound number and compressional wave velocity offer solutions, but correlating these with UCS requires complex mathematical models. This paper introduces a novel approach using an artificial neural network (ANN) to simultaneously correlate UCS with three non-destructive test indexes: Schmidt hammer rebound number, compressional wave velocity, and open-effective porosity. The proposed ANN model outperforms existing methods, providing accurate UCS predictions for various rock types. Contour maps generated from the model offer practical tools for geotechnical and geological engineers, facilitating decision-making in the field and enhancing educational resources. This integrated approach promises to streamline UCS estimation, improving efficiency and accuracy in engineering assessments of intact rock materials.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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