A Data-Driven Rutting Depth Short-Time Prediction Model With Metaheuristic Optimization for Asphalt Pavements Based on RIOHTrack

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.

Artificial intelligence applications in finance:a survey

Finance is in our daily life.We invest,borrow,lend,budget,and save money.Finance also provides guidelines for corporation and government spending and revenue collection.Traditional statistical solutions such as regression,PCA,and CFA have been widely used in financial forecasting and analysis.With the increasing interest in artificial intelligence in recent years,this paper reviews the Artificial Intelligence(AI)techniques in the finance domain systematically and attempts to identify the current AI technologies used,major applications,challenges,and trends in Finance.It explores AI-related articles in Finance in IEEE Xplore and EI compendex databases.Findings suggest AI has been engaged in Finance in financial forecasting,financial protection,and financial analysis and decision-making areas.Financial forecasting is one of the main sub-fields of Finance affected by AI technology.The major AI technology used is supervised learning.Deep learning has gained popular in recent years.AI could be used to address some emerging topics.