The invention discloses a
silt biomineralization dust suppression method based on experiment and
machine learning, which comprises the following steps: S1, acquiring a soil sample, and testing and recording soil property parameters of the sample; s2, proportioning a plurality of parts of
biomineralization dust suppressants; s3, mixing the soil sample with a
biomineralization dust suppressant, carrying out
engineering characteristic experiment on the tested soil sample after the mixing reaction is completed, and obtaining and recording
engineering property parameters of the soil sample; s4, establishing a training subsystem, and constructing a prediction model; and S5, according to newly-collected
silt sample data, the prediction model is called for real-time prediction of the reinforcement effect, and a prediction result is interpreted, analyzed and visually displayed. The
system has the beneficial effects that
experimental research and
machine learning technologies are combined, and the
system can predict the biomineralization dust suppression effect by comprehensively considering the influence of factors such as a biomineralization dust suppressant formula, a
field soil condition and a field environment, so that a scientific basis is provided for biomineralization dust suppression of the
silt foundation in the yellow flood area.