A data-driven rutting depth short-time prediction model with
  metaheuristic optimization for asphalt pavements based on RIOHTrack
Zhuoxuan Li Iakov Korovin Xinli Shi Sergey Gorbachev Nadezhda Gorbacheva Wei Huang Jinde Cao
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 from 19 asphalt
pavements with different crude oil sources on a 2.038 km long full-scale field
accelerated pavement test track (RIOHTrack, Road Track Institute) 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, Average Mean Absolute Error,
and Average Mean Absolute Percentage Error 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.