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

Notes/Preview

The contents of this course include 4 recorded presentations from the ACI 2024 Fall Convention:

1. High-Fidelity Machine Learning-Based Prediction of the Thermal Behavior of Concrete Mixture Designs and Massive Structures; Luna Al Hasani, Kiewit Engineering

2. An AI-Framework to Control and Optimize Material and Process in 3D Concrete Printing; Liberato Ferrara, Politecnico Di Milano

3. On the Use of Machine Learning and Data-Transformation Methods to Predict Hydration Kinetics and Strength of Alkali-Activated Mine Tailings-Based Binders; Sahil Surehali, Arizona State University

4. Explainable Prediction Model for Punching Shear Strength of FRP-RC Slabs Based on Kernel Density Estimation and XGBoost; Nima Khodadadi, University of Miami

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: Hasani, Ferrara, Surehali

Publication Year: 2025

ISBN:

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

Table of Contents

Learning Objectives:

1. Identify the role of machine learning in concrete design and analysis.

2. Apply AI models to predict material and structural behavior.

3. Integrate AI tools into construction and production workflows.

4. Interpret model outputs using explainable AI techniques.

ERRATA INFO

Any applicable errata are included with individual documents at the time of purchase. Errata are not included for collections or sets of documents such as the ACI Collection. For a listing of and access to all product errata, visit the Errata page.

Return/Exchange Policy

Printed / Hard Copy Products: The full and complete returned product will be accepted if returned within 60 days of receipt and in salable condition. A 20% service charge applies. Return shipping fees are the customer’s responsibility.

Electronic /Downloaded Products & Online Learning Courses: These items are not eligible for return.

Subscriptions These items are not eligible for return.

Exchanges: Contact ACI’s Customer Services Department for options (+1.248.848.3800 – ACICustomerService@concrete.org).

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