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Generalization ability of rutting prediction model for asphalt pavement based on RIOHTrack full-scale track

Published: 09 July 2024 Publication History

Abstract

The effective prediction of rutting on asphalt pavement is a difficult problem in industrial field. How to accurately characterize the rutting evolution law is an important research topic in the industry. This paper obtains the characteristic factors related to asphalt road rutting through machine learning, and uses the RIOHTrack full-scale track data to carry out the simulation research on the characteristic factors to the unknown domain data through machine learning, and obtains the corresponding prediction results. In this paper, by comparing the mechanical model and the machine learning model, combined with different division of data set, the influence of eigenvalues on generalization ability of asphalt rutting prediction model is revealed. In terms of predicting unknown structural data, the R2 score is increased by 0.15 at the highest.

References

[1]
[1]X.D. Wang, G.l. Zhou, H.y. Liu, Q. Xiao, “Key points of RIOHTRACK testing road design and construction Journal of Highway and Transportation Research and Development (English Edition)," 14 (4) (2020), pp. 1-16
[2]
[2]Z. Dong, F. Ni, “Dynamic model and criteria indices of semi-rigid base asphalt pavement[J]," The internati onal journal of pavement engineering, 2014, 15(9-10): 854-866.
[3]
[3]J. Cai, J. Luo, S. Wang, “Feature selection in machine learning: A new perspective[J]," NEURO COMPUTING, 2018, 300(jul.26): 70-79.
[4]
[4]D. Sculley, Carla E. Brodley {Dsculley, “Compression and Machine Learning: A New Perspective on Feature Space Vectors," dcc, 2006.
[5]
[5]M.N. Ghimire, “A Statistical Framework for Discrete Visual Features Modeling and Classification[J]," electrical & computer engineering, 2011.
[6]
[6]L. Yoonkyung, K. Yuwon, L. Sangjun, K. Ja-Yong, “Structured multicategory support vector machines with analysis of variance decomposition," Biometrika 93. 3(2006): 555-571.
[7]
[7]L. Wenchao, Z. Yong, X. Shixiong, “A Novel Clustering Algorithm Based on Hierarchical and K-means Clustering. Control Conference," 2007. CCC 2007. Chinese. IEEE.
[8]
[8]F. Camastra, A. Verri, “A novel kernel method for clustering," IEEE Trans Pattern Anal Mach Intell, 2005, 27(5), 801-805.
[9]
[9]N.E. Huang, “The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis," Proceedings of Royal Society of London, 454(1998).
[10]
[10]Y. Yang, S. Li, C. Li, “Research on ultrasonic signal processing algorithm based on CEEMDAN joint wavelet packet thresholding[J]," Measurement, 2022.
[11]
[11]B.D. Loomis, S.B. Luthcke, “Optimized signal denoising and adaptive estimation of seasonal timing and mass balance from simulated grace-like regional mass variations," Advances in Adaptive Data Analysis, 2014.
[12]
[12]E. Magli, L.L. Presti, G. Olmo, “A pattern detection and compression algorithm based on the joint wavelet and Radon transform[C]," International Conference on Digital Signal Processing.IEEE, 1997.
[13]
[13]M. Abambres, A. Ferreira, “Application of ANN in Pavement Engineering: State-of-Art[J]," TechRxiv, 2020.
[14]
[14]J. Yang, “Overall pavement condition forecasting using neural networks Can application to Florida highway network[J]."
[15]
[15]H. Gong, et al., “Improving accuracy of rutting prediction for mechanistic-empirical pavement design guide with deep neural networks," Construction and Building Materials, 2018, 190, 710-718.
[16]
[16]S. Choi, M. Do, “Development of the road pavement deterioration model based on the Deep Learning method," Electronics,2020,9,3.
[17]
[17]B. Kou, J.D. Cao, W. Huang, T. Ma, “The rutting model of semi-rigid asphalt pavement based on RIOHTRACK full-scale track [J]," Mathematical Biosciences and Engineering, 2023, 20(5): 8124-8145.
[18]
[18]S. Li, M. Fan, L. Xu L, “Rutting Performance of Semi-Rigid Base Pavement in RIOHTrack and Laboratory Evaluation[J]," Frontiers in Materials, 2021, 7(395).
[19]
[19]C. Qi, Q. Chen, “Evolutionary Random Forest Algorithms for Predicting the Maximum Failure Depth of Open Stope Hangingwalls," IEEE Access 6 (2018) 72808-72813
[20]
[20]C. Qi, A. Fourie, X. Du, X. Tang, “Prediction of open stope hangingwall stability using random forests. Natural Hazards," 2018.
[21]
[21]L. Yi-Hui, “Evolutionary neural network modeling for forecasting the field failure data of repairable systems," Expert Systems with Applications 33.4(2007):1090-1096.
[22]
[22]L. Tian, A. Noore, “Evolutionary neural network modeling for software cumulative failure time prediction," Reliability Engineering & System Safety,2005, 87(1), 45-51.
[23]
[23]H.E. Bin, B. Yan-Ping, S.O. Science, “MEMS Hydrophone Signal Denoising Based on Wavelet Packet and CEEMDAN," Mathematics in Practice and Theory, 2016.
[24]
[24]H. Hu, Y. Ao, H. Yan, Y. Bai, N. Shi, “Signal denoising based on wavelet threshold denoising and optimized variational mode decomposition," Hindawi, 2021.
[25]
[25]J.Wang, J. Wang, “A New Hybrid Forecasting Model Based on SW-LSTM and Wavelet Packet Decomposition: A Case Study of Oil Futures Prices[J]," Computational intelligence and neuroscience, 2021: 7653091.
[26]
[26]Z. Jianwen, L. Yang, Z. Dapeng, Z. Huanyu, “A new method of combined denoising based on ceemdan and wavelet adaptive thresholding," Electrical Measurement & Instrumentation, 2018.
[27]
[27]A.J. Haddad, G.R. Chehab, G.A. Saad, “The use of deep neural networks for developing generic pavement rutting predictive models[J]," International Journal of Pavement Engineering.
[28]
[28]M. Samir, M. El-Ramly, A. Kamel, “Investigating the Use of Deep Neural Networks for Software Defect Prediction," 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA). IEEE, 2019.
[29]
[29]X.D. Wang, et al, “Review of Researches of RIOHTRACK in 2017[J]," Journal of Highway and Transportation Research and Development, 2018.
[30]
[30]X.D. Wang, “Design of Pavement Structure and Material for Full-scale Test Track."

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  1. Generalization ability of rutting prediction model for asphalt pavement based on RIOHTrack full-scale track

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    ICIEI '24: Proceedings of the 2024 9th International Conference on Information and Education Innovations
    April 2024
    133 pages
    ISBN:9798400716409
    DOI:10.1145/3664934
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 09 July 2024

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    Author Tags

    1. RIOHTrack track
    2. Ruting
    3. asphalt pavement
    4. generalization ability
    5. machine leaning

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