A series of regression models to predict the weathering index of tropical granite rock mass

Abstract In the recent past, several weathering indicators have been developed to describe its state of weathering. The state of rock weathering is a useful indicator to estimate the integrity of tropically weathered rock material and mass which weatherability plays an important role in a tropical region. Through a ground assessment tool, the strength and durability of the rock mass could be estimated and complex or adopted to simplify the early prediction of the complex engineering parameter. This paper presents several models of the Weathering Index (WI) using selected significant parameters using statistical analysis. For this purpose, several sites have been chosen to represent granitic rock mass. Forty (40) numbers of samples were collected and tested comprising from four (4) sites in Malaysia. Several laboratory tests have been conducted such as Point Load Index (Is (50) ), dry density, Slake Durability 1 (SD1), Slake Durability 2 (SD2) and moisture content. The field and laboratory data sets are used to determine the WI by using simple regression and MLR analysis Significant parameters found to be useful in determining the WI are selected namely SD1, dry density, Is (50) , and block volume. These parameters were selected based on stepwise analysis using Statistical Package for the Social Sciences (SPSS). Following the models’ implementation, the models were evaluated and the best prediction model was selected after considering statistical coefficients, such as coefficient of determination (R2), variance account for (VAF), and root mean squared error (RMSE), as well as utilizing a straightforward ranking approach. The findings of this study could contribute to the more accurate prediction of WI using a more simplistic field and laboratory parameters. Therefore, the WI is useful during the initial stages and planning of rock excavation work and provides a good description of weathering grade and rock mass properties, which will affect excavatability in granitic areas.

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Publication Details

Journal
Environmental Earth Sciences
Published
2024-08-29
DOI
https://doi.org/10.1007/s12665-024-11742-8
Citations
4
Primary Topic
Landslides and related hazards
Type
article
Field-Weighted Citation Impact
2.84

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article

A series of regression models to predict the weathering index of tropical granite rock mass

Danial Jahed Armaghani, Vynotdni Rathinasamy, Edy Tonnizam Mohamad, Mariatul Kiftiah Ahmad Legiman et al.
4 citations
Environmental Earth Sciences
Landslides and related hazards
2.84
article

A series of regression models to predict the weathering index of tropical granite rock mass

Danial Jahed Armaghani, Vynotdni Rathinasamy, Edy Tonnizam Mohamad, Mariatul Kiftiah Ahmad Legiman, Mohd Firdaus Md Dan Azlan, Eka Kusmawati Suparmanto, Fazleen Slamat, Nurul Eilmy Zainuddin, Zuraini Zainal
article en
4 citations

Abstract

Abstract In the recent past, several weathering indicators have been developed to describe its state of weathering. The state of rock weathering is a useful indicator to estimate the integrity of tropically weathered rock material and mass which weatherability plays an important role in a tropical region. Through a ground assessment tool, the strength and durability of the rock mass could be estimated and complex or adopted to simplify the early prediction of the complex engineering parameter. This paper presents several models of the Weathering Index (WI) using selected significant parameters using statistical analysis. For this purpose, several sites have been chosen to represent granitic rock mass. Forty (40) numbers of samples were collected and tested comprising from four (4) sites in Malaysia. Several laboratory tests have been conducted such as Point Load Index (Is (50) ), dry density, Slake Durability 1 (SD1), Slake Durability 2 (SD2) and moisture content. The field and laboratory data sets are used to determine the WI by using simple regression and MLR analysis Significant parameters found to be useful in determining the WI are selected namely SD1, dry density, Is (50) , and block volume. These parameters were selected based on stepwise analysis using Statistical Package for the Social Sciences (SPSS). Following the models’ implementation, the models were evaluated and the best prediction model was selected after considering statistical coefficients, such as coefficient of determination (R2), variance account for (VAF), and root mean squared error (RMSE), as well as utilizing a straightforward ranking approach. The findings of this study could contribute to the more accurate prediction of WI using a more simplistic field and laboratory parameters. Therefore, the WI is useful during the initial stages and planning of rock excavation work and provides a good description of weathering grade and rock mass properties, which will affect excavatability in granitic areas.

Environmental Earth SciencesVol. 83(17)
University of Technology Sydney (AU), University of Technology Malaysia (MY), Tun Hussein Onn University of Malaysia (MY)
University of Technology Sydney
Sustainable cities and communities
Openalex Percentile: Top 9%
Landslides and related hazards
2.84
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