Concrete structure durability prediction method based on random forest and intelligent algorithm
A technology of concrete structure and random forest, applied in the direction of neural learning methods, calculations, calculation models, etc., can solve the problems of large dispersion of experimental observation data, long experimental period, unreliable prediction results, etc., to solve the problem of unstable prediction results, The effect of solving computational complexity and good anti-interference ability
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[0076] Example 1
[0077] The method for predicting the impermeability of concrete structures based on random forest-based least squares support vector machines proposed in the present invention mainly includes the following steps:
[0078] (1) Sample data collection of influencing factor index system
[0079] Based on cement strength, cement dosage, fly ash dosage, water reducing agent dosage, fine aggregate dosage, coarse aggregate dosage, concrete strength, sand ratio, water-binder ratio, water dosage, alkali content, mud content, needles, flakes There are a total of 14 factors, including the total content and average particle size of particles, as input variables, and the chloride ion diffusion coefficient of concrete as output variables. 33 sets of monitored data are selected as the original training number set. The data are shown in Table 1:
[0080] Table 1 sample data
[0081]
[0082] (2) Random forest feature selection
[0083] Divide all data samples into two parts: a trainin...
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