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A Data-Driven rutting depth Short-Time Prediction Model With Metaheuristic Optimization for Asphalt Pavements Based on riohtrack

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自动化学报:英文版 2023年第10期10卷 1918-1932页

作者： Zhuoxuan Li Iakov Korovin Xinli Shi Sergey Gorbachev Nadezhda Gorbacheva Wei Huang Jinde Cao School of Mathematics Southeast UniversityNanjing 210096China Scientific Research Institute of Multiprocessor Computer Systems Southern Federal UniversityTaganrog 347928Russia School of Cyber Science and Engineering Southeast UniversityNanjing 210096China Russian Academy of Engineering Moscow 125009Russia Intelligent Transportation System Research Center Southeast UniversityNanjing 210096China Nanjing Modern Multimodal Transportation Laboratory Nanjing 211100China Yonsei Frontier Laboratory Yonsei UniversitySeoulKorea(South)

rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements’ rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from19 asphalt pavements with different crude oil sources on a 2.038km long full-scale field accelerated pavement test track(Road Track Institute, riohtrack) in Tongzhou, Beijing. In addition,this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction(RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of average root mean squared error(MSE), average mean absolute error(MAE), and a verage mean absolute percentage error(MAPE) for 19 asphalt pavements reaching 1.742, 1.363, and 1.94% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. It is a promising alternative method that facilitates the rapid assessment of pavement conditions and could be applied in the future to production processes in the oil and gas industry.

关键词： Extreme learning machine algorithm with residual correction(RELM) metaheuristic optimization oil-gas transportation riohtrack,rutting depth

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