Prediction of time-dependent concrete mechanical properties based on advanced deep learning models considering complex variables

Abstract

Evaluating the mechanical properties such as compressive strength, tensile strength, and modulus of elasticity (MOE) of concrete is crucial for the design, construction, and quality control of engineering projects. Developing a comprehensive theoretical-based numerical model to predict the concrete mechanical properties is considered challenging, owing to the diversity of admixtures and curing environment affecting the performance of modern concrete. The deep learning (DL) model gives a feasible approach as data-driven methods for accurate prediction of various material properties these years. However, predicting time-dependent mechanical properties of modern concrete with complex mix constituents by DL algorithms is still confined so far. In this article, artificial neuron network (ANN) and long short-term memory (LSTM) based DL models were established to predict concrete compressive strength, tensile strength, and MOE at different ages, considering the mix design, curing condition, curing time and tension testing method as input variables. LSTM outperformed ANN in terms of the statistical indicators for accuracy evaluation with high training and testing time costs. Bidirectional modification and multi-head attention mechanism were implemented to further improve the prediction accuracy of LSTM. It is shown that the multi-head attention bidirectional LSTM had more precise predicting results (R2=0.9355 for compressive strength, R2=0.8930 for tensile strength, R2=0.9584 for MOE) and fitting of the mechanical property-age curves. The LSTM-based models provided a promising prediction tool for time-dependent concrete mechanical properties with complex input parameters.