Asphalt pavement performance and service life prediction method

By constructing a comprehensive quantitative model of influencing factors and a deep learning prediction model, combined with curve fitting technology, the lack of comprehensiveness and accuracy of asphalt pavement performance prediction methods in the existing technology is solved, and more accurate pavement performance and life prediction are achieved.

CN119691379APending Publication Date: 2025-03-25CRRC P & D INSTITUTE CO LTD +1
0 Cites 1 Cited by

Patent Information

Application Number
CN202411605937.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-12
Publication Date
2025-03-25

AI Technical Summary

Technical Problem

The existing asphalt pavement performance prediction methods lack comprehensiveness and accuracy, and the model is difficult to adapt to complex actual conditions under different environments and conditions.

Method used

A method combining multi-source data preprocessing and deep learning prediction model is adopted to build a comprehensive quantitative model of influencing factors, trained through long-term memory networks and full-connection layer prediction models, and predicted pavement performance indicators and life calculations are carried out in combination with curve fitting technology.

Benefits of technology

A more comprehensive and accurate prediction of asphalt pavement performance and life is achieved, breaking through the limitations of handling performance changes in complex pavement, and the prediction results are closer to actual engineering needs.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses an asphalt pavement performance and service life prediction method, and relates to the technical field of road engineering, and the method comprises the steps: carrying out the preprocessing of multi-source data, and constructing an influence factor comprehensive quantification model based on the preprocessed multi-source data; using the preprocessed multi-source data to train a prediction model; inputting real-time multi-source data of a to-be-predicted asphalt pavement into the trained prediction model to obtain pavement performance indexes, and using a curve fitting technology to obtain a function expression of the pavement performance indexes changing along with time; and calculating the time required by the pavement performance index to reach the corresponding critical value, and selecting the minimum time value as the pavement residual life. According to the method, by comprehensively considering the influence factors of multiple dimensions such as traffic load, environmental factors and pavement structure characteristics, the specific influence of the actual situation on the use performance and the residual life of the asphalt pavement can be disclosed more comprehensively and accurately.
Need to check novelty before this filing date? Find Prior Art