A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm

By Q. Han, Changqing Gui, Jie Xu, Giuseppe Lacidogna. Construction and Building Materials, 2019

378 citations recorded by the source corpus.

Abstract The prediction results of high-performance concrete compressive strength (HPCCS) based on machine learning methods are seriously influenced by input variables and model parameters. This study proposes a method with two stages to select proper variables, simplify parameter settings, and predict HPCCS. The appropriate variables are selected in the first stage by measuring their importance based on random forest, and then are optimized to predict HPCCS in the second stage. The results show that the proposed method was effective for input variable optimization, and could return better predictions than that without variable optimization, provided that the parameters are set within a reasonable range. Compared with previous models, the proposed method shows a strong generalization capacity for HPCCS prediction. We find that the prediction performance of the model is better when the input variables are expressed as absolute mass, and the model performers well when the actual compressive strength of HPC is high.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/7f6453729d26d193efef06e03eef6fde67ec32f1

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In Civil and Structural Engineering

Citations in the corpus

Classification-Based Regression Models for Prediction of the Mechanical Properties of Roller-Compacted Concrete Pavement (2020, methodology): “proposed improved RF model to simulate CS of high-performance concrete.[27].”