The microbiologically induced calcite precipitation (MICP) has been extensively studied for geotechnical engineering through simultaneous action of natural phenomena and engineering processes. The focus of bacterial contribution to the MICP has been directed to calcium carbonate productivity, while the additional bacterial role as a crystal nucleation center was not explained especially from a mathematical prediction modeling point of view. Therefore, this study provides explanations and a mathematical modeling approach of bacterial influence on the MICP induced by newly-isolated ureolytic Bacillus strains and Sporosarcina pasteurii DSM 33. Using the obtained results of low-cost, rapid, and simple assays, artificial neural network modeling was applied for cell surface predispositions, pH changes as well as calcium-involved function in biofilm formation during the MICP, for the first time. Based on the obtained contribution of the alkalophilic/alkaloresistant bacteria, calcite precipitation can be significantly directed by the presence, of ureolytic bacterial cells as nucleation centers during CaCO3 precipitation as well as their morphology, surface characteristics, potential to form a biofilm, and/or generate pH changes.
Keywords: alkaline environment; biocalcifying bacteria; biomineralization; predictive modeling.
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