Research on the Optimization Design of HMM-13 Grading for High Modulus Mixtures Based on BP Neural Network Algorithm
Voids Content is an important indicator in the mix design of high modulus mixture HMM-13.This article tests the porosity of high modulus mixtures by forming rotating compaction specimens under differ-ent mix ratio conditions.The key mesh sizes of 13.2 mm(K1),4.75 mm(K2),2.36 mm(K3),0.075 mm(K4)and the comprehensive oil stone ratio(O/S)are used as input parameters,and the voids(V)is used as output parameter.The double hidden layer BP neural network algorithm is used.A volume index prediction model for high modulus mixture HMM-13 was established,and the sensitivity of key sieve pass rate to the po-rosity of rotary compacted specimens was studied.The research results indicate that the established model has an error of less than 1％and strong generalization ability,which can be used for predicting the volume index of high modulus mixtures;When 90％＜K1＜93％,45％＜K2＜50％,25％＜K3＜34％,6.6％＜K4＜6.9％,and O/S ≥5.2％,the requirement of mixture porosity of 1.5％～2.5％can be met.
high modulus asphalt mixtureVoids contentkey sieveartificial neural network
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