The application discloses a concrete proportioning multi-objective
parallel design method based on sparse
Gaussian process
Bayesian optimization, and the method comprises the following steps: taking a regression model represented by a preset
machine learning
algorithm as a meta-learner, respectively constructing meta-prediction models of data-driven concrete mechanical properties, and finally integrating the meta-learner to form a high-precision concrete
mechanical property prediction model; judging whether the current concrete
mechanical property pre-training prediction model meets
engineering requirement information, if yes, the step can be skipped, the optimization target of the concrete proportioning is determined, that is, the weights of the
mechanical property parameters and the cost of
unit volume of concrete are set; entering the sparse
Gaussian process Bayesian
parallel optimization process, predicting the mechanical property parameters according to the generated initial proportioning, calculating the cost of
unit volume of concrete under the proportioning, performing multi-round optimization, and finally obtaining the optimal proportioning; the method constructs a high-precision concrete mechanical property prediction model through pre-training or a newly-built
data set, adopts a
residual neural network, integrated learning and other models to integrate the advantages of multiple algorithms, and improves the accuracy of the concrete mechanical property prediction such as
compressive strength, tensile strength and
elastic modulus.