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Compressive Strength Prediction of High-Strength Concrete Using Long Short-Term Memory and Machine Learning Algorithms
| Content Provider | MDPI |
|---|---|
| Author | Chen, Honggen Li, Xin Wu, Yanqi Zuo, Le Lu, Mengjie Zhou, Yisong |
| Copyright Year | 2022 |
| Description | 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 $(R^{2}$). The results showed that the prediction accuracy and reliability of LSTM were higher with $R^{2}$ = 0.997, RMSE = 0.508, MAE = 0.08, and MAPE = 0.653 compared to the evaluation metrics $R^{2}$ = 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. |
| Starting Page | 302 |
| e-ISSN | 20755309 |
| DOI | 10.3390/buildings12030302 |
| Journal | Buildings |
| Issue Number | 3 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-03-04 |
| Access Restriction | Open |
| Subject Keyword | Buildings Characterization and Testing of Materials High-strength Concrete Lstm Svr Compressive Strength Shapley Additive Explanations |
| Content Type | Text |
| Resource Type | Article |
