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Compressive strength is an important mechanical property of high-strength concrete (HSC), but testing methods are usually uneconomical, time-consuming, and labor-intensive. To this end, in this paper, a long short-term memory (LSTM) model was proposed to predict the HSC compressive strength using 324 data sets with five input independent variables, namely water, cement, fine aggregate, coarse aggregate, and superplasticizer. The prediction results were compared with those of the conventional support vector regression (SVR) model using four metrics, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient (R2). The results showed that the prediction accuracy and reliability of LSTM were higher with R2 = 0.997, RMSE = 0.508, MAE = 0.08, and MAPE = 0.653 compared to the evaluation metrics R2 = 0.973, RMSE = 1.595, MAE = 0.312, MAPE = 2.469 of the SVR model. The LSTM model is recommended for the pre-estimation of HSC compressive strength under a given mix ratio before the laboratory compression test. Additionally, the Shapley additive explanations (SHAP)-based approach was performed to analyze the relative importance and contribution of the input variables to the output compressive strength.

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It is time-consuming and uneconomical to estimate the strength properties of fly ash concrete using conventional compression experiments. For this reason, four machine learning models—extreme learning machine, random forest, original support vector regression (SVR), and the SVR model optimized by a grid search algorithm—were proposed to predict the compressive strength of fly ash concrete on 270 group datasets. The prediction results of the proposed model were compared using five evaluation indices, and the relative importance and effect of each input variable on the output compressive strength were analyzed. The results showed that the optimized hybrid model showed the best predictive behavior compared to the other three models, and can be used to forecast the compressive strength of fly ash concrete at a specific mix design ratio before conducting laboratory compression tests, which will save costs on the specimens and laboratory tests. Among the eight input variables listed, age and water were the two relatively most important features with superplasticizer and fly ash being of weaker relative importance.