Title:
Exploring Machine Learning to Predict Concrete Field Performance Against Alkali-Aggregate Reaction (AAR)
Author(s):
Bergmann
Publication:
Web Session
Volume:
ws_F23_Bergmann.pdf
Issue:
Appears on pages(s):
Keywords:
DOI:
Date:
10/29/2023
Abstract:
As one of the more harmful deterioration mechanisms affecting concrete infrastructures worldwide, the alkali-aggregate reaction (AAR) has been reported in over 50 countries. Among the several testing methods developed in laboratories to assess aggregate reactivity and the effectiveness of supplementary cementitious materials (SCMs) in mitigating AAR, the accelerated mortar bar test (AMBT) and the concrete prism test (CPT) are the most used around the globe. Moreover, field studies have been extensively developed to correlate laboratory tests with structures exposed to a real environment. Yet, current outcomes show significant discrepancies involving the mentioned laboratory tests, indicating no clear thresholds regarding aggregate reactivity potential for new structures. Nevertheless, although extensive work has explored the diagnosis of AAR on existing structures, there is still a lack of defining an accurate model for the prognosis stage. In this sense, the extensive current data on outdoor exposure sites requires implementing elaborated data analysis techniques (i.e., machine learning) to predict AAR development on both existing and new structures. Therefore, this work aims to explore how each variable affects AAR development through probabilistic approaches enhancing the accuracy of management protocols to assess the aggregate reactivity potential via laboratory tests to reduce the risks associated with AAR.