Abstract
The Brazilian indirect tensile strength (BIT) of intact limestone rocks is a critical factor in the design of structures on or within these rocks. Nonetheless, acquiring and analyzing core samples in a laboratory environment to determine BIT can be a time-consuming and costly task, bearing in mind that it is not always possible to obtain intact rock samples. Thus, there is a need to develop models that predict BIT based on simple physical properties and non-destructive tests. In this study, an extensive program was developed to collect samples of limestone rocks from different locations in the northern part of Iraq. These samples were then subjected to destructive and nondestructive tests in dry and saturated conditions. The results were analyzed using simple regression and genetic algorithm-based regression analyses to develop new prediction models. Overall, it has been found that saturation of the pores of the rock reduces the BIT with a percentage reduction between 15 and 47%. Importantly, it has been noticed that the prediction models developed using genetic algorithm-based regression analysis provided slightly better accuracy compared to simple regression analysis, with an R2 of 0.80 for the dry condition and between 0.85 and 0.87 for the saturated condition, and a MAE range of between 3.11 and 3.15 for the dry condition and 2.04 and 2.20 for the saturated condition. Notably, the developed models could help professionals who do not have access to BIT equipment or face difficulty acquiring intact samples with the required length-to-diameter ratio.
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SA: Conceptualization, Methodology, Validation, Formal Analysis, and Writing—Original Draft. YMA: Conceptualization, Methodology, Writing—review & editing. DAM: data, Experimental testing, Methodology, and writing—review & editing. SK: Methodology, Writing—review & editing.
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Alzabeebee, S., Alshkane, Y.M., Mohammed, D.A. et al. Comparing 1D Regression and Evolutionary Polynomial Analyses for Predicting Brazilian Tensile Strength of Limestone in Dry and Saturated Conditions. Geotech Geol Eng 42, 2495–2515 (2024). https://doi.org/10.1007/s10706-023-02687-z
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DOI: https://doi.org/10.1007/s10706-023-02687-z