Intelligent Prediction and Optimization Algorithm for Material Properties in Civil Engineering

In the rapidly developing field of civil engineering today, concrete serves as the cornerstone of the construction industry, and its quality directly affects the safety and durability of engineering projects. However, the uneven quality of concrete in the market not only damages public interests, but also poses a serious threat to the safety of people's lives and property. Among them, compressive strength, as a key indicator for measuring concrete quality, directly determines the structural stability and service life of buildings. Therefore, accurately predicting the compressive strength of concrete has immeasurable value for optimizing concrete mix design and improving engineering quality. In response to this urgent need, this article innovatively proposes an intelligent prediction and optimization method for civil engineering material properties based on the Random Forest (RF) algorithm. This method fully utilizes the advantages of RF in processing large-scale data, avoiding overfitting, and capturing complex nonlinear relationships. Through deep learning and analysis of multidimensional data such as concrete composition and preparation process, it achieves accurate prediction of concrete compressive strength. The experimental results show that the algorithm can significantly improve the accuracy of predicting the properties of civil engineering materials.