Open Access

ARTICLE

Sensitive Analysis on the Compressive and Flexural Strength of Carbon Nanotube-Reinforced Cement Composites Using Machine Learning

Ahed Habib^1,*, Mohamed Maalej², Samir Dirar³, M. Talha Junaid², Salah Altoubat²

1 Research Institute of Sciences and Engineering, University of Sharjah, Sharjah, P.O. Box 27272, United Arab Emirates
2 Department of Civil and Environmental Engineering, University of Sharjah, Sharjah, P.O. Box 27272, United Arab Emirates
3 Department of Architectural Engineering, University of Sharjah, Sharjah, P.O. Box 27272, United Arab Emirates

* Corresponding Author: Ahed Habib. Email:

(This article belongs to the Special Issue: Innovative and Sustainable Materials for Reinforced Concrete Structures)

Structural Durability & Health Monitoring 2025, 19(4), 789-817. https://doi.org/10.32604/sdhm.2025.064882

Received 26 February 2025; Accepted 20 May 2025; Issue published 30 June 2025

Abstract

Carbon nanotube-reinforced cement composites have gained significant attention due to their enhanced mechanical properties, particularly in compressive and flexural strength. Despite extensive research, the influence of various parameters on these properties remains inadequately understood, primarily due to the complex interactions within the composites. This study addresses this gap by employing machine learning techniques to conduct a sensitivity analysis on the compressive and flexural strength of carbon nanotube-reinforced cement composites. It systematically evaluates nine data-preprocessing techniques and benchmarks eleven machine-learning algorithms to reveal trade-offs between predictive accuracy and computational complexity, which has not previously been explored in carbon nanotube-reinforced cement composite research. In this regard, four main factors are considered in the sensitivity analysis, which are the machine learning model type, the data pre-processing technique, and the effect of the concrete constituent materials on the compressive and flexural strength both globally through feature importance assessment and locally through partial dependence analysis. Accordingly, this research optimizes ninety-nine models representing combinations of eleven machine learning algorithms and nine data preprocessing techniques to accurately predict the mechanical properties of carbon nanotube-reinforced cement composites. Moreover, the study aims to unravel the relationships between different parameters and their impact on the composite’s strength by utilizing feature importance and partial dependence analyses. This research is crucial as it provides a comprehensive understanding of the factors influencing the performance of carbon nanotube-reinforced cement composites, which is vital for their efficient design and application in construction. The use of machine learning in this context not only enhances predictive accuracy but also offers insights that are often challenging to obtain through traditional experimental methods. The findings contribute to the field by highlighting the potential of advanced data-driven approaches in optimizing and understanding advanced composite materials, paving the way for more durable and resilient construction materials.

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