Accuracy of Point Load Index and Brazilian Tensile Strength in Predicting the Uniaxial Compressive Strength of the Rocks: A Comparative Study

Uniaxial compressive strength (UCS) of rocks is one of the main parameters required in the design of geotechnical projects such as tunnels, dams, or rock slopes. According to the literature, there are a large number of predictive regression equations to evaluate the UCS from the point load index (PLI) and Brazilian tensile strength (BTS). However, the equations developed in previous studies have different accuracies in UCS prediction. A more accurate prediction of the UCS will result in a more appropriate design of the geotechnical project, and thus ensure its success during operation. In the present paper, a comparative study was conducted between the accuracy of PLI and BTS in predicting the UCS of the limestone and sandstone. Moreover, the role of porosity (n) on the accuracy of predicting the UCS from PLI and BTS was investigated. Some statistical indices were used to investigating the accuracy of predictive regression equations of UCS. The results revealed that the UCS of rocks can be predicted with a higher accuracy using BTS compared with PLI. Also, the findings showed that the n had a significant role in increasing the accuracy of PLI- and BTS-based regression equations of the UCS predictive. The predictive equations established in the present study can be used in practical applications for indirect evaluation of limestone and sandstone UCS in the site of a geotechnical project.

Authors

Institutions

Publication Details

Journal
Materials
Published
2024-10-18
DOI
https://doi.org/10.3390/ma17205081
Citations
3
Primary Topic
Rock Mechanics and Modeling
Type
article
Field-Weighted Citation Impact
0.81
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Accuracy of Point Load Index and Brazilian Tensile Strength in Predicting the Uniaxial Compressive Strength of the Rocks: A Comparative Study

Luís Sousa, Amin Jamshidi
3 citations
Materials
Rock Mechanics and Modeling
0.81
article

Accuracy of Point Load Index and Brazilian Tensile Strength in Predicting the Uniaxial Compressive Strength of the Rocks: A Comparative Study

Luís Sousa, Amin Jamshidi
article en
3 citations

Abstract

Uniaxial compressive strength (UCS) of rocks is one of the main parameters required in the design of geotechnical projects such as tunnels, dams, or rock slopes. According to the literature, there are a large number of predictive regression equations to evaluate the UCS from the point load index (PLI) and Brazilian tensile strength (BTS). However, the equations developed in previous studies have different accuracies in UCS prediction. A more accurate prediction of the UCS will result in a more appropriate design of the geotechnical project, and thus ensure its success during operation. In the present paper, a comparative study was conducted between the accuracy of PLI and BTS in predicting the UCS of the limestone and sandstone. Moreover, the role of porosity (n) on the accuracy of predicting the UCS from PLI and BTS was investigated. Some statistical indices were used to investigating the accuracy of predictive regression equations of UCS. The results revealed that the UCS of rocks can be predicted with a higher accuracy using BTS compared with PLI. Also, the findings showed that the n had a significant role in increasing the accuracy of PLI- and BTS-based regression equations of the UCS predictive. The predictive equations established in the present study can be used in practical applications for indirect evaluation of limestone and sandstone UCS in the site of a geotechnical project.

MaterialsVol. 17(20)
University of Trás-os-Montes and Alto Douro (PT), Lorestan University (IR)
Sustainable cities and communities
Openalex Percentile: Top 32%
Rock Mechanics and Modeling
0.81
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.