Pavement long-term performance prediction model based on deep learning and construction method thereof

Abstract

The invention discloses a pavement long-term performance prediction model based on deep learning and a construction method thereof. The model is an LSTM-BPNN feature fusion model for fusing an LSTM long-term and short-term memory network and a BPNN back propagation neural network through an attention method. The LSTM-BPNN neural network long-term performance prediction model is constructed by analyzing the prediction model of domestic and overseas scholars in road performance based on deep learning, dividing data features into basic features and time sequence features according to data features of a road surface long-term performance database and combining different feature data, so that the LSTM-BPNN neural network long-term performance prediction model can be used for predicting the road performance, gives play to the advantages of the network, performs respective functions, achieves the correlation coupling, and fully captures the potential correlation between different types of climatic changes and road performance change trends, thereby achieving the more accurate prediction of the long-term performance of a pavement in the future.

Description

technical field [0001] The invention belongs to the technical field of road maintenance industry, and in particular relates to a long-term road surface performance prediction model based on deep learning and a construction method thereof. Background technique [0002] With the geometric growth of the road network scale, as much as 400 billion yuan is invested in road network maintenance each year. The maintenance decision and fund division of the road network mostly rely on empirical judgment. How to grasp the accurate service status of the road network and determine the timing of maintenance is related to the health of the national economy It is extremely urgent and necessary. The accuracy of pavement performance prediction not only determines the reliability of maintenance and repair decisions, but also determines the economic benefits of maintenance investment, the comfort and safety of road users and other social benefits, so it is of great significance to the research o...

AI Technical Summary

Problems solved by technology

For climate characteristics, they only model a few simple statistics (e.g., mean, variance), do not combine time-series characteristics with basic characteristics for pavement performance prediction, and cannot capture deep patterns of climate change

In summary, existing machine learning models are difficult to model complex time series features and deep potential correlations contained in data

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