Application of Data Science Techniques to Estimate Soluble Alkali Contribution from Fly Ashes for Determination of Concrete Pore Solution Chemistry

Anol Mukhopadhyay, Texas A&M University

Description

Application of Data Science Techniques to Estimate Soluble Alkali Contribution from Fly Ashes for Determination of Concrete Pore Solution Chemistry Authors Prof. Anol Mukhopadhyay - United States - Texas A&M University Abstract Traditionally, the available alkali (AA) tests following ASTM C 311 were primarily used to qualify Fly Ashes’ (FA) for suitability in Alkali Silica reaction (ASR) mitigation based on a 1.5% Na2Oeq AA limit. However, the test is currently being discontinued due to criticisms stemming from its lack of correlation with the ASR expansions and high operator/laboratory variability of test measurements. Therefore, a Bayesian machine learning model (ML) is developed in current research principles to estimate the AA from FA based on their bulk chemical composition. However, ML models only explain correlation but not the cause-effect. Subsequently, advanced analytical approaches coupled with thermodynamic modeling were used to evaluate the influence of composition, mineralogy, and reactivity on the contribution of AA to pore solution in fly ashes. Over 400 experimental data points for FA - AA measurements, phase mineralogy (QXRD), degree of reaction, water-soluble alkali measurements, pore solution extraction measurements, etc., were compiled based on laboratory measurements at TTI and 40 years of published research studies. The data were analyzed to understand alkali dissolution from FA, evaluate their overall soluble alkali (SA) contribution, and further refine the ML model predictions. Overall, research findings strongly justify the continuation of the ASTM C 311-based AA test for FA to evaluate their SA contribution to pore solution. Furthermore, predictive equations based on the ML model are incorporated in a new pore solution model (currently under development) to directly estimate the SA contribution from FA based on their bulk oxide composition.

 
May 14th, 3:00 PM May 14th, 3:30 PM

Application of Data Science Techniques to Estimate Soluble Alkali Contribution from Fly Ashes for Determination of Concrete Pore Solution Chemistry

Grand Rapids, Michigan

Application of Data Science Techniques to Estimate Soluble Alkali Contribution from Fly Ashes for Determination of Concrete Pore Solution Chemistry Authors Prof. Anol Mukhopadhyay - United States - Texas A&M University Abstract Traditionally, the available alkali (AA) tests following ASTM C 311 were primarily used to qualify Fly Ashes’ (FA) for suitability in Alkali Silica reaction (ASR) mitigation based on a 1.5% Na2Oeq AA limit. However, the test is currently being discontinued due to criticisms stemming from its lack of correlation with the ASR expansions and high operator/laboratory variability of test measurements. Therefore, a Bayesian machine learning model (ML) is developed in current research principles to estimate the AA from FA based on their bulk chemical composition. However, ML models only explain correlation but not the cause-effect. Subsequently, advanced analytical approaches coupled with thermodynamic modeling were used to evaluate the influence of composition, mineralogy, and reactivity on the contribution of AA to pore solution in fly ashes. Over 400 experimental data points for FA - AA measurements, phase mineralogy (QXRD), degree of reaction, water-soluble alkali measurements, pore solution extraction measurements, etc., were compiled based on laboratory measurements at TTI and 40 years of published research studies. The data were analyzed to understand alkali dissolution from FA, evaluate their overall soluble alkali (SA) contribution, and further refine the ML model predictions. Overall, research findings strongly justify the continuation of the ASTM C 311-based AA test for FA to evaluate their SA contribution to pore solution. Furthermore, predictive equations based on the ML model are incorporated in a new pore solution model (currently under development) to directly estimate the SA contribution from FA based on their bulk oxide composition.