Based On The Grey Correlation Entropy Method And BP Neural Network Of SBS Modified Asphalt Concrete Road Ultraviolet Aging Research

In this paper, asphalt mixture pavement by the external environment of light, heat,oxygen, water, load, management, maintenance and internal of aggregate gradation,asphalt content, void ratio, multiple factors, such as surface structure at the grass-rootslevel concerns the performance of the road pavement life. Asphalt changessignificantly affected by ultraviolet after three indicators, and asphalt mixture byultraviolet (uv) posterior with performance, and the influence factors on the agingeffects of posterior with performance and influence of primary and secondary order,less to this research at home and abroad. Factors, this paper will consider strongultraviolet ray through the grey correlation entropy method and BP neural networkmodel analysis and research the SBS modified asphalt concrete high temperaturestability, low temperature crack resistance, fatigue resistance and anti ultravioletageing performance and so on correlation between various influencing factors, andprovide the basis for design and construction.Based on2.36mm,4.75mm and9.5mm for key screen hole, by limiting theparticle diameter increasing of medium diameter ratio, put forward several kinds ofAC-13and improvement of the AC-16S grading, through Marshall test method andthe entropy theory to analyze the best context determines the oil-stone ratio, high andlow temperature performance. Build uv ultraviolet light box simulation environment.Using rutting test and grey entropy theory analysis and evaluation before and afterultraviolet aging at various levels with high temperature stability of asphalt mixtureand the effects of various factors, with low temperature splitting experiment and greyentropy theory analysis and evaluation before and after ultraviolet aging at all levelswith asphalt mixture at low temperature crack resistance and the effects of various factors. By fatigue test and the grey correlation entropy method combined withmultivariate linear regression OriginPro8.0data analysis software on the fatigue testperformance of the asphalt mixture is analyzed. Analysis the influence degree of theright performance of the various factors, find out main influence factors, according tothe entropy correlation value is sorted to analyze its rationality, reasonable selectionfor design and construction personnel dosage of materials and provide guidance.Using grey prediction model based on a small experimental information toestablish a GM(1,1), GM(1,N) model to predict the road surface resistance toultraviolet aging degree of intensity change over time, finally, aiming at theshortcomings of the traditional road use performance prediction method using BPneural network is adopted to establish the pavement performance prediction model forpavement management system to provide guidance.