Intelligent decision-making model in preventive maintenance of asphalt pavement based on PSO-GRU neural network. (January 2022)

• Record Type: Journal Article
• Authors: Li, Jiale; Zhang, Zhishuai; Wang, Xuefei; Yan, Weixi
• Is Part Of: Advanced engineering informatics. Volume 51(2022)
• Publication Date: 2022-01
• Subjects: Highway preventive maintenance (PM) -- Rehabilitation and maintenance (R&M) strategies -- Pavement surface distress -- Artificial neural networks (ANN); Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2022.101525
• Languages: English
• ISSNs: 1474-0346

Abstract

Graphical abstract: Abstract: The milage of asphalt pavement growth explosively around the world in the past decades resulted in a tremendous maintenance workload. Preventive maintenance (PM) is an effective strategy in saving budget, keeping the pavement in good condition, and extending pavement life. A particle swarm optimization (PSO) algorithm enhanced gated recurrent unit (GRU) neural network is developed in this research to predict five pavement performance parameters. The model is trained based on a dataset containing seven-year distress measurement data in 100-m intervals, traffic load data, climatic records, and maintenance records of a chosen highway in China. The random forest (RF) algorithm is used to analyze the influence of the factors on pavement performances for different lanes. The result shows the PSO-GRU model could increase the prediction accuracy by 21% on average compared with traditional ANN and 17% on average compared with the AdaBoost model. The validation case study shows a significant consistency between the predicted pavement quality index and the whole-year measurement data with a 0.67 coefficient of determination. This study demonstrates the potential of using the PSO-GRU neural network to provide the most effective treatment at a given location on a highway.