The invention relates to the technical field of
machine learning, and provides a
drill hole rock
mass structure quantitative prediction method and
system based on
machine learning, and the method comprises the steps: constructing an associated
data set with a
drill hole television image and / or an actual rock
mass structure disclosed by an exploration
adit as a benchmark, and taking an actual rock
mass structure quantitative value as a benchmark; and calculating an overall deviation correction coefficient of a rock mass integrity coefficient and a rock quality index based on the associated
data set, performing deviation correction on
original data by using the correction coefficient, and realizing automatic mapping from double-index input to rock mass structure quantitative predicted value output by a
machine learning model. The decision coefficient of the prediction model on a
test set reaches 0.9 or above, the average absolute error does not exceed 0.3, the prediction time of a
single group of data does not exceed 1.5 seconds, and the prediction precision and efficiency are far higher than those of a traditional experience method. The method supports local off-line deployment, adapts to a field network-free environment, constructs a data closed-loop mechanism to realize continuous iteration of the model, and provides a reliable basis for dam foundation safety assessment.