Multi-Layer Perceptron Neural Networks for Concrete Strength Prediction: Balancing Performance and Optimizing Mix Designs

Optimizing concrete production requires balancing ingredient ratios and using local resources to produce an economical material with the desired consistency, strength, and durability. Compressive strength is crucial for structural design, yet predicting it accurately is challenging due to the complex interplay of various factors, including component types, water–cement ratio, and curing time. This study employs a Multi-layer Perceptron Neural Network (ANN_MLP) to model the relationship between input variables and the compressive strength of normal and high-performance concrete. A dataset of 1030 samples from the literature was used for training and evaluation. The optimized ANN_MLP configuration included 16 neurons in a single hidden layer, with the ‘tanh’ activation function and ‘sgd’ solver. It achieved an R2 of 0.892, an MAE of 3.648 MPa, and an RMSE of 5.13 MPa. The model was optimized using a univariate sensitivity analysis to measure the impact of each hyperparameter on performance and select optimal values to maximize the accuracy and robustness.