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 stu...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.展开更多
采用足尺加速加载试验模拟和研究路面长期服役性能,是世界公认的最为高效、对自然和荷载耦合作用仿真度最高的技术手段,是路面设计理论发展的重要支撑.面向新一代长寿命路面技术目标,充分发挥足尺加速加载试验的优势,深入揭示荷载与环...采用足尺加速加载试验模拟和研究路面长期服役性能,是世界公认的最为高效、对自然和荷载耦合作用仿真度最高的技术手段,是路面设计理论发展的重要支撑.面向新一代长寿命路面技术目标,充分发挥足尺加速加载试验的优势,深入揭示荷载与环境耦合作用下的复杂路面结构行为机理,是革新路面设计方法的有效途径之一.第S54次香山科学会议"中国长寿命路面关键科学问题及技术前沿"的议题之一"路面足尺试验的目标与科学问题",即立足国内外足尺加速加载试验研究的主要经验与贡献,对研究目标和科学问题进行了讨论,进一步明确了该领域的发展方向和研究重点.本文通过对我国首条足尺加速加载路面试验环道RIOHTrack(Research Institute of Highway MOT Track)设计理念及研究进展的介绍,论述了长期持续观测积累的科学数据对于认识、发现路用性能演化规律的重要性,以及由现象学层面特征和差异出发,遵循现象发现-理论解析-实践验证的路面工程研究方法论,在非线性力学分析体系、多元仿真模型构建等研究方面取得的主要进展.展开更多
To clarify the importance of various influencing factors on asphalt pavement rutting deformation and determine a screening method of model indicators,the data of the RIOHTrack full-scale track were examined using the ...To clarify the importance of various influencing factors on asphalt pavement rutting deformation and determine a screening method of model indicators,the data of the RIOHTrack full-scale track were examined using the factor analysis method(FAM).Taking the standard test pavement structure of RIOHTrack as an example,four rutting influencing factors from different aspects were determined through statistical analysis.Furthermore,the common influencing factors among the rutting influencing factors were studied based on FAM.Results show that the common factor can well characterize accumulative ESALs,center-point deflection,and temperature,besides humidity,which indicates that these three influencing factors can have an important impact on rutting.Moreover,an empirical rutting prediction model was established based on the selected influencing factors,which proved to exhibit high prediction accuracy.These analysis results demonstrate that the FAM is an effective screening method for rutting prediction model indicators,which provides a reference for the selection of independent model indicators in other rutting prediction model research when used in other areas and is of great significance for the prediction and control of rutting distress.展开更多
The use of non-destructive testing(NDT) equipment, such as the falling weight deflectometer(FWD), provides important estimates of road health and helps to optimize road management regimes. However, periodic road testi...The use of non-destructive testing(NDT) equipment, such as the falling weight deflectometer(FWD), provides important estimates of road health and helps to optimize road management regimes. However, periodic road testing and post-processing of the collected data are cumbersome and require much expertise, a considerable amount of time, money, and other resources. This study attempts to develop a reliable prediction method for estimating the deflection basin area of different asphalt pavements using road temperature, load time, and load pressure as main characteristics. The data are obtained from 19 kinds of asphalt pavements on a 2.038-km-long full-scale fleld accelerated pavement testing track named RIOHTrack(Research Institute of Highway Track) in Tongzhou, Beijing. In addition, a chaotic particle swarm algorithm(CPSO) and a segmented regression strategy are proposed in this paper to optimize the XGBoost model. The experiment results of the proposed method are compared with those of classical machine learning algorithms and achieve an average of mean square error and mean absolute error respectively by 5.80 and 1.59.The experiments demonstrate the superiority of the XGBoost algorithm over classical machine learning methods in dealing with nonlinear problems in road engineering. Signiflcantly, the method can reduce the frequency of deflection tests without affecting its estimation accuracy, which is a promising alternative way to facilitate the rapid assessment of pavement conditions.展开更多
基金supported by the Analytical Center for the Government of the Russian Federation (IGK 000000D730321P5Q0002) and Agreement Nos.(70-2021-00141)。
文摘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.
文摘采用足尺加速加载试验模拟和研究路面长期服役性能,是世界公认的最为高效、对自然和荷载耦合作用仿真度最高的技术手段,是路面设计理论发展的重要支撑.面向新一代长寿命路面技术目标,充分发挥足尺加速加载试验的优势,深入揭示荷载与环境耦合作用下的复杂路面结构行为机理,是革新路面设计方法的有效途径之一.第S54次香山科学会议"中国长寿命路面关键科学问题及技术前沿"的议题之一"路面足尺试验的目标与科学问题",即立足国内外足尺加速加载试验研究的主要经验与贡献,对研究目标和科学问题进行了讨论,进一步明确了该领域的发展方向和研究重点.本文通过对我国首条足尺加速加载路面试验环道RIOHTrack(Research Institute of Highway MOT Track)设计理念及研究进展的介绍,论述了长期持续观测积累的科学数据对于认识、发现路用性能演化规律的重要性,以及由现象学层面特征和差异出发,遵循现象发现-理论解析-实践验证的路面工程研究方法论,在非线性力学分析体系、多元仿真模型构建等研究方面取得的主要进展.
基金The National Key Research and Development Program of China(No.2018YFB1600300,2018YFB1600304,2018YFB1600305)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX21_0133)the Scientific Research Foundation of Graduate School of Southeast University.
文摘To clarify the importance of various influencing factors on asphalt pavement rutting deformation and determine a screening method of model indicators,the data of the RIOHTrack full-scale track were examined using the factor analysis method(FAM).Taking the standard test pavement structure of RIOHTrack as an example,four rutting influencing factors from different aspects were determined through statistical analysis.Furthermore,the common influencing factors among the rutting influencing factors were studied based on FAM.Results show that the common factor can well characterize accumulative ESALs,center-point deflection,and temperature,besides humidity,which indicates that these three influencing factors can have an important impact on rutting.Moreover,an empirical rutting prediction model was established based on the selected influencing factors,which proved to exhibit high prediction accuracy.These analysis results demonstrate that the FAM is an effective screening method for rutting prediction model indicators,which provides a reference for the selection of independent model indicators in other rutting prediction model research when used in other areas and is of great significance for the prediction and control of rutting distress.
基金supported by the National Key Research and Development Program of China (Grant No. 2020YFA0714300)the National Natural Science Foundation of China (Grant Nos. 61833005 and 62003084)the Natural Science Foundation of Jiangsu Province of China (Grant No.BK20200355)。
文摘The use of non-destructive testing(NDT) equipment, such as the falling weight deflectometer(FWD), provides important estimates of road health and helps to optimize road management regimes. However, periodic road testing and post-processing of the collected data are cumbersome and require much expertise, a considerable amount of time, money, and other resources. This study attempts to develop a reliable prediction method for estimating the deflection basin area of different asphalt pavements using road temperature, load time, and load pressure as main characteristics. The data are obtained from 19 kinds of asphalt pavements on a 2.038-km-long full-scale fleld accelerated pavement testing track named RIOHTrack(Research Institute of Highway Track) in Tongzhou, Beijing. In addition, a chaotic particle swarm algorithm(CPSO) and a segmented regression strategy are proposed in this paper to optimize the XGBoost model. The experiment results of the proposed method are compared with those of classical machine learning algorithms and achieve an average of mean square error and mean absolute error respectively by 5.80 and 1.59.The experiments demonstrate the superiority of the XGBoost algorithm over classical machine learning methods in dealing with nonlinear problems in road engineering. Signiflcantly, the method can reduce the frequency of deflection tests without affecting its estimation accuracy, which is a promising alternative way to facilitate the rapid assessment of pavement conditions.
