Deep integrated forest regression modeling method for measuring concrete compressive strength

Abstract

The invention discloses a deep integrated forest regression-based modeling method for measuring the compressive strength of concrete. The method comprises the following steps of preprocessing originalhigh-dimensional features by adopting a dimension reduction strategy suitable for an industrial process to obtain a reduction feature vector; then, training a plurality of sub-forest models by takingthe reduction feature vector as an input, selecting predicted values of a plurality of sub-forests through a KNN neighbor method to combine to obtain a layer regression vector, and combining the layer regression vector with the reduction feature vector to obtain an enhancement layer regression vector, thereby obtaining an output of the layer; secondly, inputting the enhancement layer regression vector of the input layer to obtain the output of the second-layer forest model, and repeating the steps in sequence until the output of the (K-1)th-layer forest model is completed; and finally, training a plurality of sub-forest models by taking the output of the (K-1)th layer of middle-layer forest model as the input of an output-layer forest model module, and performing arithmetic average on theprediction output of the sub-forest model to obtain a final prediction result.

Description

technical field [0001] The invention relates to a deep integrated forest regression modeling method for measuring the compressive strength of concrete. Background technique [0002] Limited to the comprehensive and complex characteristics of complex physical / chemical production processes such as unclear mechanism, nonlinearity, and strong coupling, the key process parameters that characterize the product quality and environmental protection indicators of this type of process are usually called difficult to measure parameters [1]. Such parameters are manually sampled at regular intervals, and then analyzed offline in the laboratory (such as concrete compressive strength, dioxin concentration emitted from urban solid waste incineration pollution, and grinding particle size that characterizes the quality of grinding) or rely on excellent fields It is estimated by experts at the production site based on experience (such as the mill load that characterizes the grinding efficiency...

AI Technical Summary

Problems solved by technology

However, the structure of the concrete compressive strength soft sensor model in the above research literature is complex, the representation learning of features is not considered between the modules, and there are problems such as low prediction accuracy of the concrete compressive strength soft sensor value

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