Method for predicting concrete compressive strength based on random forest and intelligent algorithm

Abstract

The invention belongs to the field of concrete compressive strength prediction, and particularly discloses a method for predicting concrete compressive strength based on a random forest and an intelligent algorithm. The method comprises the following steps: establishing an original sample set of a concrete compressive strength index system, taking a training number set as the input of a random forest regression model to carry out importance evaluation on influence factors forming the concrete compressive strength index system, carrying out feature selection, and selecting an influence factor set with the minimum error as an optimal feature variable set; training the least squares support vector machine model by taking the test data set as the input of the least squares support vector machine model and taking the concrete 28d compressive strength value as the output, and verifying the prediction result of the trained least squares support vector machine model by adopting the test data set; and performing error analysis on a prediction result. The invention improves the precision of the prediction model, enables the prediction result to be more accurate and stable, and can serve as an effective tool for quickly predicting the concrete compressive strength.

Description

technical field [0001] The invention belongs to the field of concrete compressive strength prediction, and more specifically relates to a method for predicting concrete compressive strength based on random forest and intelligent algorithm. Background technique [0002] In recent years, engineering accidents caused by insufficient durability design of concrete structures have occurred frequently, and higher requirements have been put forward for the durability of concrete in engineering. Concrete structures in Northeast, Northwest, and North China have long served in humid and cold environments, and freeze-thaw cycle damage, as the main problem of concrete in cold areas, has become the focus of research at home and abroad. The compressive strength is one of the important indexes to evaluate the durability of concrete, and it has important practical value to predict the compressive strength of concrete. [0003] At present, the concrete durability prediction methods studied b...

AI Technical Summary

Problems solved by technology

However, the derivation process of the prediction model established through theoretical research is complicated, and there is a certain degree of departure from the actual situation, and the applicability is not strong. The traditional concrete freeze-thaw test method can obtain high-precision freeze-thaw durability prediction results, but there are still some problems in the experimental research. There are many disadvantages such as relatively long test period, relatively large workload, and experimental errors.

Moreover, the freezing and thawing process of concrete is accompanied by many uncertainties, resulting in uncertainties in the expression of concrete freezing and thawing laws. These uncertainties mainly come from the randomness of measurement data, systematic errors, and certain unknown uncertainty

Using the general statistical method, due to the large dispersion of experimental observation data, the analysis results will often be distorted.

In addition, with the continuous development of intelligent algorithms and machine learning, many studies have applied intelligent algorithms to the field of concrete compressive strength prediction. At present, common algorithms mainly focus on BP neural network, artificial neural network, RBF neural network, etc. However, these neural network intelligent models have the disadvantages of slow learning speed, high possibility of network training failure and easy to fall into local extremum. There are many influencing factors in the prediction process of concrete compressive strength, and the data used for learning has complex Noise interference, these algorithms cannot meet the requirements of prediction accuracy to a certain extent

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