Enhancing concrete structures: Integrating machine learning and deep learning for optimizing material strength, fire resistance, and impact protection

Abstract

This paper presents an innovative approach to enhancing the performance of concrete structures by integrating advanced machine learning (ML) and deep learning (DL) techniques. Concrete, a ubiquitous material in construction, is known for its strength and durability, but its performance can be significantly improved through the optimization of material properties such as strength, fire resistance, and impact protection. Traditional methods of optimizing these properties rely heavily on empirical testing and expert intuition, which can be time-consuming and may not fully capture the complex interactions between different material components. In this study, we propose a framework that leverages ML and DL algorithms to analyse vast datasets of concrete compositions and their corresponding performance metrics [1]. By employing these computational techniques, we aim to identify patterns and relationships that can guide the design of more resilient concrete mixes. The integration of DL models, particularly neural networks, allows for the prediction of material behaviour under various conditions, leading to more accurate and reliable optimization of concrete properties. The proposed framework has the potential to revolutionize the construction industry by enabling the development of concrete with enhanced properties, reducing the need for extensive physical testing, and accelerating the innovation process. This paper discusses the methodology, implementation, and potential impact of using ML and DL in concrete optimization, providing a roadmap for future research and practical applications in improving the performance of concrete structures [2].