Perceptual concrete mix proportion optimization method based on interpretable machine learning

Abstract

The invention provides a perceptual concrete mix proportion optimization method based on interpretable machine learning, which comprises the following steps: constructing an inductive concrete compressive strength database and performing preprocessing to determine model input characteristics, constructing an interpretable machine learning model, and performing performance comparison with other mainstream machine learning models; a feature circulation method is adopted to reduce the collinearity influence, model input features are trained based on an optimal feature function, and the contribution of the model input features to a prediction result is quantified from the overall aspect and the local aspect; preliminarily drawing up a mix proportion, substituting the mix proportion into the interpretable machine learning model after the characteristic contribution quantification, and guiding the optimal design of the mix proportion of the inductive concrete mixture according to an interpretable analysis framework. According to the method, the optimal design of the inductive concrete mixture mix proportion is guided according to an interpretability analysis framework, the contribution degree of each parameter in the mix proportion to the material performance can be quantified, and technical support is provided for rapid and intelligent design of the self-sensing concrete mix proportion based on the performance.

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