On-Demand Course: Innovation Day: AI Odyssey Session - Building Smarter: Leveraging AI to Engineer Concrete Innovations, Part 1 of 2

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On-Demand Course: Innovation Day: AI Odyssey Session - Building Smarter: Leveraging AI to Engineer Concrete Innovations, Part 1 of 2

Price: $ 39.00 USD

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Notes/Preview

The contents of this course include 5 recorded presentations from the ACI 2024 Fall Convention:

1. How does AI/ML fit within our concrete construction world? Nathan Tregger, Texas A&M

2. Database and Model Development to Support Modernization of Army Materials and Design Specifications involving Type IL Cements; Newell Washburn, Georgia Institute of Technology

3. On the Prediction of the Mechanical Properties of Limestone Calcined Clay Cement: A Random Forest Approach Tailored to Cement Chemistry; Taihao Han, Missouri University of Science and Technology

4. Real-Time Refinement of Concrete Mix Designs by Active Learning; Yu Song, Concrete.Ai

5. Bayesian Optimization and Machine Learning for Accelerated Sustainable Concrete Design; Thomas T. Baah, University of Arizona

INSTRUCTIONS: Study the materials included in this module. Then, complete and pass the corresponding 10-question quiz with a score of 80% or higher to receive a certificate for 0.1 CEU (equivalent to 1.0 PDH).

Continuing Education Credit: 0.1 CEU (1 PDH)

Approved AIA and ICC

Access Period: 30 days

Description

State-of-the art machine learning applications in modeling cement and concrete properties will be explored in this session. Industry professionals, and researchers will demonstrate AI`s game-changing role in concrete science. Attendees will gain insight into AI applications in 3D concrete printing, concrete mixture optimization, crack detection and understanding composition-property linkages. Industry professionals, civil engineers, material scientists and researchers should attend. Potential outcomes for attendees include learning how various ML techniques can be implemented towards efficient concrete design.

 

Document Details

Author: Tregger, Washburn, Han

Publication Year: 2025

ISBN:

Categories: Cementitious Materials, Concrete Technology, Mixture Proportioning, Sustainability

Table of Contents

Learning Objectives:

1. Evaluate the transformative impact of generative AI on concrete material science.

2. Analyze machine learning's capability in linking cement composition to properties.

3. Review predictive analytics in optimizing the mechanical properties of alternative binders.

4. Assess active learning's role in real-time concrete mix design refinement.

ERRATA INFO

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