Abstract
Because of the deficiency of the index of cement pavement performance evaluation and the defect of the evaluation method in the specification, the performance of the pavement is comprehensively evaluated by seven optimized indexes and grading standards that reflect functional performance and structure of the pavement. Because the discrete Hopfield neural network is available with simple construction procedure, less training samples, and strong objectivity.The DHNN is constructed by MATLAB to evaluate the performance of test pavement. The ideal cement pavement performance grading evaluation index matrix and 6 places unclassified of test pavement performance evaluation index matrix are input to the neural network then the evaluation result is obtained after simulating and learning. Finally, comparing the result of the DHNN with the fuzzy complex matter element method and the nonlinear fuzzy method, it is proved that the discrete Hopfield neural network evaluation method is reliable.
Highlights
Pavement performance evaluation plays an important role in the road maintenance and maintenance process
The discrete Hopfield neural network constructed by MATLAB is used to evaluate the performance of the cement pavement on the test section
1, This paper enriches the indicators in the Code in the selection of cement pavement performance evaluation indicators
Summary
Pavement performance evaluation plays an important role in the road maintenance and maintenance process. The evaluation index of cement pavement performance in "Standard" is insufficient and the determination of index weight is subjective, which is easy to produce a phenomenon that the evaluation result is inconsistent with the actual condition of the road surface. The second category is the comprehensive evaluation of the performance of cement pavement using mathematical methods, which is based on the optimization index and grading standard of cement pavement performance evaluation. This type of method largely compensates for the shortcomings of the standard evaluation method. Based on the test data of a test section, the discrete Hopfield neural network method was applied to evaluate the performance of cement pavement. Compared with the fuzzy complex element method and the nonlinear fuzzy method, the discrete Hopfield neural network method is proved to be reliable
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