High-performance concrete strength prediction based on ensemble learning

The compressive strength and tensile strength of high-performance concrete (HPC) are important mechanical property indexes. However, the related mechanical tests are time-consuming; therefore, predicting the strength of HPC using available test data is important. In this study, compressive strength and tensile strength tests were conducted on HPC with fly ash and silica fume separately, with fly ash and silica fume together, and with fly ash, silica fume, and polypropylene fiber in triple-blending. Based on the analysis of the test data, the contribution of silica fume to the increase in compressive strength and tensile strength occurred in the early stage of maintenance, whereas the contribution of fly ash to the increase in compressive strength and tensile strength occurred in the late stage of maintenance. Four ensemble learning models, AdaBoost, GBDT, XGBoost and random forest, were used in this study. The optimal data set division ratio was tested to be 8:2. The sensitivity of the input variables was obtained through the model. The best prediction model among the four ensemble learning models established was GBDT, and the GBDT model showed a good performance with other machine learning models.

Automated summary

In this study, compressive strength and tensile strength tests were conducted on HPC, and the impact of different additives on strength was analyzed. Four ensemble learning models were used to predict the strength of HPC, with the GBDT model performing the best.