Predictive modeling for self-compacting concrete: evaluating machine learning approaches in real-world construction scenarios

Aldawish, Abdulaziz and Kulasegaram, Sivakumar 2024. Predictive modeling for self-compacting concrete: evaluating machine learning approaches in real-world construction scenarios. Presented at: 4th fib International Conference on Concrete Sustainability (ICCS2024), Guimarães, Portugal, 11–13 September 2024. Published in: Barros, Joaquim A. O., Cunha, Vítor M. C. F., Sousa, Hélder S., Matos, José C. and Sena-Cruz, José M. eds. 4th fib International Conference on Concrete Sustainability (ICCS2024). Lecture Notes in Civil Engineering. Lecture Notes in Civil Engineering Springer Nature Switzerland, pp. 236-242. 10.1007/978-3-031-80672-8_29

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Abstract

This study provides a comprehensive review and analysis of the applications of various machine learning techniques in predicting properties of self-compacting concrete (SCC). This study also integrated experimental data from existing literature to build and evaluate ML models. We critically assess methodologies, strengths and limitations of Artificial Neural Networks (ANN), Support Vector Machines (SVM), Decision Tree Regression s(DTR), and other machine learning models, emphasizing their predictive accuracy in real-world scenarios. We found that ANN showed significant promise for handling complex data structures and adaptability, but was dependent on extensive and high-quality datasets. SVM exceled in generalisability and effectiveness, even with limited data, while DTR and its advanced forms, such as XGBoost, offered a balance of accuracy and efficiency. The objective of this study was to identify the most effective model based on predictive accuracy and efficiency in real-world construction scenarios. Furthermore, this study explored challenges such as data diversity, model generalizability and real-world applicability. Future research should focus on hybrid models, expanding datasets, and applying these models to diverse concrete mixtures and conditions, offering significant implications for efficient and sustainable SCC use in construction.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	Springer Nature Switzerland
ISBN:	9783031806711
ISSN:	2366-2557
Last Modified:	08 Dec 2025 14:46
URI:	https://orca.cardiff.ac.uk/id/eprint/182985

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