Prediction and Optimization of Cementitious Composites Using Machine Learning Algorithms

DOI

10.2991/978-94-6463-662-8_31How to use a DOI?

Keywords

Machine Learning; Genetic Algorithm; Optimization

Abstract

Advanced machine learning (ML) techniques are employed in this study to enhance the design and performance of cementitious composites, with a focus on optimizing concrete’s compressive strength—a key attribute for resilient structures. By leveraging algorithms such as Genetic Algorithms (GA), Artificial Neural Networks (ANN), and Support Vector Machines (SVM), the research refines concrete mix compositions, carefully adjusting parameters like water-cement ratio, aggregate proportions, and admixture levels. Rigorous data preprocessing ensures high-quality input, allowing the ML models to capture intricate, nonlinear relationships between these variables and compressive strength, significantly reducing the need for traditional, time-intensive testing. Through GA-based optimization, the study iteratively improves mix designs to maximize both durability and cost-efficiency, achieving outcomes superior to conventional empirical methods. This integration of ML and optimization techniques provides a scalable, adaptive framework for concrete design, promoting material efficiency and structural resilience. By setting a new standard for performance-based mix design, the study highlights ML’s transformative potential in civil engineering, advancing sustainable practices while meeting rigorous performance requirements.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - B. J. S. Varaprasad
AU  - V. Rangaveni
AU  - B. Yashwanth
PY  - 2025
DA  - 2025/03/17
TI  - Prediction and Optimization of Cementitious Composites Using Machine Learning Algorithms
BT  - Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)
PB  - Atlantis Press
SP  - 377
EP  - 389
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-662-8_31
DO  - 10.2991/978-94-6463-662-8_31
ID  - Varaprasad2025
ER  -