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.

翻译：沥青路面车辙是多种路面设计指南中的关键设计指标。良好的道路运输基础可为油气运输提供安全保障。本研究旨在开发稳健的人工智能模型，以温度、载荷轴数及不同沥青路面的车辙深度片段为主要特征进行预估。实验数据来源于北京通州2.038公里足尺加速路面试验环道（RIOHTrack，道路研究所）上19种不同原油来源的沥青路面。此外，本文还提出通过复杂网络方法与Louvain社区检测算法构建不同路面车辙深度的复杂网络，可筛选不同沥青路面车辙数据中的关键结构要素并发现相似结构要素。设计了一种基于残差校正的极限学习机（RELM）算法，并采用独立自适应粒子群算法进行优化。将所提方法的实验结果与多种经典机器学习算法对比，19种沥青路面的平均均方根误差、平均绝对误差和平均绝对百分比误差预测值分别达到1.742、1.363和1.94%。实验表明，RELM算法在处理道路工程非线性问题方面优于经典机器学习方法。值得注意的是，该方法通过对生产环境参数的认知分析，确保模拟环境能够适应不同抽象层次的需求。