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This Week's Featured Presentation

Unification of Standards - An Industry Goal
Presented by: Vojtech Tauber, Sika Corporation

Presentation details

International Session: Bridging the Gaps: One Concrete World - Multiples Standards (ACI Fall 2023, Boston, MA) Concrete is the most popular building material in the world and the second most consumed substance of all after water. It is widely available, economical, durable and easy to work with. During the presentation, several European Standard for the concrete testing will be compared to US standards. There is a lot of global players in the construction industry. Unification of the standards across the different countries, regions is a very important step to follow the same or a very similar standards across the globe, this should be our long-term industry goal.


Upcoming Presentation

February 26 - March 3

Exploring Machine Learning to Predict Concrete Field Performance Against Alkali-Aggregate Reaction (AAR)
Presented by: Ana Bergmann, University of Ottawa

Presentation details

Making the Most of our Data: ACI 135: Machine Learning-Informed Construction and Design (ACI Fall 2023, Boston, MA) 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.

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