Explainable highway performance degradation prediction model based on LSTM

•Organizing a data matrix of PCI, RDI, RQI, and SRI from highway network in Guizhou.•Forming the method of establishing a multi-output neural network prediction model.•Establishing a LSTM model of network-level semi-rigid asphalt pavement performance.•Combining the Bayesian optimization algorithm to...

Full description

Saved in:

Bibliographic Details
Published in:	Advanced engineering informatics Vol. 61; p. 102539
Main Authors:	Sun, Xin, Wang, Honglei, Mei, Shilong
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.08.2024
Subjects:	Bayesian optimization algorithm Explainable prediction model Highway performance degradation Long Short-Term Memory (LSTM) SHapley Additive exPlanations (SHAP) SHapley Additive exPlanations (SHAP) Long Short-Term Memory (LSTM) Bayesian optimization algorithm Highway performance degradation Explainable prediction model
ISSN:	1474-0346
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	•Organizing a data matrix of PCI, RDI, RQI, and SRI from highway network in Guizhou.•Forming the method of establishing a multi-output neural network prediction model.•Establishing a LSTM model of network-level semi-rigid asphalt pavement performance.•Combining the Bayesian optimization algorithm to optimize the hyperparameters.•Adopting SHAP to conduct post-hoc causal analysis through set-valued mapping. With the dense and huge highway network in China, the highway maintenance management system has become the most concerned issue for Chinese highway managers in recent years. Therefore, based on the highway network in Guizhou Province of China, this paper established the semi-rigid asphalt pavement performance multi-output Long Short-Term Memory (LSTM) prediction model with regional applicability, including Pavement Surface Condition Index (PCI), Pavement Rutting Depth Index (RDI), Pavement Riding Quality Index (RQI) and Pavement Skidding Resistance Index (SRI) and the hyperparameters of the model were optimized by Bayesian optimization algorithm and the casual analysis of the model was conducted based on SHapley Additive exPlanations (SHAP), providing a reference for China's highway network-level preventive maintenance decision-making. The results prove that the established model can effectively predict the performance of the following year through the data of the previous two years. The established model has a better comprehensive prediction effect in comparison to similar multi-output models. The average coefficient of determination (R2) of the four prediction indexes can reach 0.823. Besides, the prediction effect among the indexes is stable and multiple pavement performance indexes can be effectively and reliably predicted at the same time. In addition, the contribution of input feature variables to output is quantified based on SHAP. According to quantified the contribution, the association relationship of the neural network model is set-valued mapped to the causality relationship in the post hoc. The input feature variables with the main contribution are extracted for causal analysis. The causal analysis shows that the degradation of the four pavement performances all have obvious time memory characteristics and have significant differences, but their pavement performance degradation is the result of nonlinear changes caused by the coupling effects of traffic load, road age, climate, and pavement structure.
ISSN:	1474-0346
DOI:	10.1016/j.aei.2024.102539