Prediction of high-performance concrete compressive strength using deep learning techniques

Concrete compressive strength (CCS) is the most crucial structural engineering designing conventional concrete and high-performance concrete (HPC) structures. Accurately predicting high-performance concrete (HPC) compressive strength is crucial when considering this parameter for cost–benefit analysis and time-saving. Recent studies have found that the deep learning (DL) model is more popular, since it has a higher prediction accuracy than conventional machine learning (ML) techniques. This study proposes four deep learning approaches: BiLSTM, CNN, GRU, and LSTM models, which is rarely seen in the literature. The model is developed using a large database, including details about cement, fly ash, coarse aggregate, sand, water, age, and blast furnace slag as input variables and compressive strength as an output variable. In this research, 80% of the dataset is used for training, while the remaining 20% is used as a testing dataset. Deep learning models result showed an R-square value of the above around 0.960 at the training phase and almost overhead 0.940 at the testing phase. But GRU model performs better than other models, where the R-square value was the significant level of the overhead of 0.990 at the training phase and also the above almost 0.961 at the testing phase, which is a high-accuracy result for both phases. Thus, this research provides a novel and effective method for predicting HPC's compressive strength, which can help develop sustainable infrastructures without requiring time-consuming and costly experiments. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.