Study On Highway Subgrade Filling Thickness Detection Of Rayleigh Wave Method And Theoretical

Along with the national highway and municipal road projects after the completion of construction, highway project is about to enter the peak acceptance work.. Road base course thickness meets the standard requirements of highway subgrade engineering quality detection is one of the important contents of evaluation, but now in the highway engineering quality inspection process to this key indicators are still used by most of core drilling method for pavement thickness.The damage detection method is difficult to fully, accurately grasp the actual situation of highway engineering construction quality.Rayleigh Mianbo detection method as a new model of highway engineering quality detection technology, and traditional damage detection methods, this method has the use of simple, nondestructive testing, the economic cost and can quickly reflect the layered structure of highway subgrade extracted dispersion curve etc. Then, for hundreds of kilometers of highway pavement thickness quality detection, Rayleigh Mianbo detection method shows great workload and not high detection precision. Therefore, it is necessary to establish and perfect a rapid detection and data analysis and evaluation system.. The Rayleigh Mianbo detection and artificial neural network nonlinear predictive theory has conducted in-depth discussion, study the formation of a set of road base course thickness quality detection and evaluation method.Combined with the term in the highway engineering quality detection in the field of practical work experience, the Rayleigh Mianbo detection method in highway pavement thickness detection of more thorough research and the discussion. According to a large number of highway subgrade Rayleigh Mianbo detection data acquisition, processing and the results of analysis and evaluation, satisfy the need of base course thickness detection was studied and the system; based on Rayleigh Mianbo (Rayleigh Wave) dispersion curve inflection point and highway subgrade engineering of the thickness of each layer corresponding relation, carry out BP neural network prediction and evaluation of Highway Engineering Research on road base thickness. The innovation of this thesis:(1) the Rayleigh Mianbo data acquisition technology parameters were optimized, after many experiments and discussed the optimization scheme of field detection;(2) according to the thickness of roadbed quality inspection of the scene of the actual situation, forward simulation model, and the results were analyzed;(3) based on the BP neural network comprehensive evaluation forecast system of highway subgrade engineering thickness quality index. The study shows that:in the evaluation of highway engineering quality, Rayleigh Mianbo detection technology is a kind of no damage, but the popularity of the detection method, to solve the deficiency of the traditional testing method. BP neural network system for road base course thickness prediction has better applicability, for Subgrade of highway engineering quality inspection application thickness has opened up a new way.