Estimation of the compressive strength of ultrahigh performance concrete using machine learning models

Abstract

The compressive strength of ultrahigh performance concrete (UHPC) is influenced by the composition, quality, and quantity of its constituent elements. Using traditional statistical methods, it is difficult for us to quantify the relationships between the technical properties of UHPC and the composition of the mixture because of their complexity and nonlinearity. This work aims to develop advanced prediction models for estimating UHPC compressive strength over a large spectrum of supplementary cementitious material combinations and aggregate sizes. The models trained on the UHPC mixture dataset with 15 input variables included the group method of data handling, recurrent neural networks, long short-term memory, and bidirectional long short-term memory (Bi-LSTM). These models routinely forecast UHPC compressive strength according to sensitivity analysis, external validation, and statistical performance measures. During testing, the Bi-LSTM model outperformed the other models, with an RMSE of 0.0482 and an R² value of 0.9464. These results maximize component selection by showing how effectively the Bi-LSTM model might reduce UHPC formulation development and lower the cost and testing time span.