Estimation of concrete compressive strength from non-destructive tests using a customized neural network and genetic algorithm.

This paper introduces a novel approach for deriving an equation aimed at predicting concrete compressive strength, leveraging two non-destructive test results: ultrasonic pulse velocity and rebound hammer value. Traditional regression methods often fail to capture the complex non-linear relationships crucial for accurate concrete CS estimation, leading to limited effectiveness. In contrast, while machine learning models excel at mapping these non-linear relationships from non-destructive test results, their intricate internal mechanisms can be opaque, posing challenges for practical application and comprehension by engineers. This study aims to bridge these gaps by developing an estimation equation derived from the decoded weights of a neural network specifically designed to capture the intricate nonlinear mapping relationship. Initially, the equation is intricate and lengthy, comprising multiple terms. To streamline and refine it, a genetic algorithm is employed, focusing solely on the most pivotal terms. Consequently, the refined prediction equation demonstrates superior estimation performance with an RMSE of 3.40 MPa, an MAE of 2.70 MPa, an R² of 0.92, and an R of 0.97 compared to several existing formulations. Furthermore, a comparative analysis is undertaken to assess the impact of the degree of equation simplification on its predictive accuracy. The findings offer insights into the pivotal roles of exponential function terms in enhancing prediction performance, as well as elucidating trigonometric function terms contribute the model's complex non-linear mapping capability. • A novel scheme for derivation of equations to predict concrete compressive strength is proposed. • Ultrasonic pulse velocity and rebound number are utilized for assessing concrete compressive strength. • A neural network learns the non-linear relationship between non-destructive test results and concrete compressive strength. • To translate the learned non-linear mapping relationship into the mathematical equation, the neural network was customized. • A genetic algorithm refines the neural network-derived estimation equation by selectively preserving key terms. [ABSTRACT FROM AUTHOR]