Research on Driving Factors of the RIOHTrack Rutting Prediction Model Based on Interpretable Ensemble Learning

LI Min; LI Zhuoxuan; SHI Xinli; CAO Jinde

doi:10.21656/1000-0887.450066

Volume 46 Issue 1

Jan. 2025

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Article Navigation > Applied Mathematics and Mechanics > 2025 > 46(1): 92-104

LI Min, LI Zhuoxuan, SHI Xinli, CAO Jinde. Research on Driving Factors of the RIOHTrack Rutting Prediction Model Based on Interpretable Ensemble Learning[J]. Applied Mathematics and Mechanics, 2025, 46(1): 92-104. doi: 10.21656/1000-0887.450066

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Research on Driving Factors of the RIOHTrack Rutting Prediction Model Based on Interpretable Ensemble Learning

doi: 10.21656/1000-0887.450066

LI Min¹,
LI Zhuoxuan^2,3,
SHI Xinli^{1,2
,
,},
CAO Jinde^2,3

1. School of Cyber Science and Engineering, Southeast University, Nanjing 210096, P.R.China;
2. Key Laboratory of Transport Industry of Comprehensive Transportation Theory (Nanjing Modern Multimodal Transportation Laboratory), Nanjing 211100, P.R.China;
3. School of Mathematics, Southeast University, Nanjing 211189, P.R.China

Received Date: 2024-03-12
Rev Recd Date: 2024-11-28

Abstract

Abstract

The transport infrastructure is the foundation of modern social and economic development, where the asphalt pavement plays an important role as a key component. Accurate prediction of asphalt pavement conditions is of great significance to guide pavement maintenance work. Rutting is an important indicator for evaluating the health condition of asphalt pavement. Existing asphalt pavement condition prediction models are mainly based on mechanical experience models or machine learning technologies. However, these methods lack interpretability and cannot provide relevant information on the extent to which the input features affect rutting. Herein, an interpretable integrated learning framework (FI-EL-SHAP) was established, in which the FI module filters features with the entropy weight method and the Pareto analysis, the EL module evaluates the performances of different models and selects the optimal model, and the SHAP module performs visual analysis on the relationship between input features and model outputs to reveal the impacts of different features on model prediction results. This study realizes a quantitative analysis of the rut formation mechanism while ensuring the model accuracy.
- asphalt pavement,
- rutting,
- ensemble learning,
- interpretability

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References(43)

References

[2]LI Z, ZHANG J, LIU T, et al. Using PSO-SVR algorithm to predict asphalt pavement performance[J]. Journal of Performance of Constructed Facilities,2021,35(6): 04021094.

ZHANG N, ALIPOUR A. A two-level mixed-integer programming model for bridge replacement prioritization[J]. Computer-Aided Civil and Infrastructure Engineering,2020,35(2): 116-133.

[3]CHOI S, DO M. Development of the road pavement deterioration model based on the deep learning method[J]. Electronics,2019,9(1): 3.

[4]DAMIRCHILO F, HOSSEINI A, PARAST M M, et al. Machine learning approach to predict international roughness index using long-term pavement performance data[J]. Journal of Transportation Engineering (Part B): Pavements,2021,147(4): 04021058.

[5]HOSSEINI S A, SMADI O. How prediction accuracy can affect the decision-making process in pavement management system[J]. Infrastructures,2021,6(2): 28.

[6]NASERI H, SHOKOOHI M, JAHANBAKHSH H, et al. Evolutionary and swarm intelligence algorithms on pavement maintenance and rehabilitation planning[J]. International Journal of Pavement Engineering,2022,23(13): 4649-4663.

[7]HOSSAIN E I N, SINGH D, ZAMAN P E M. Dynamic modulus-based field rut prediction model from an instrumented pavement section[J]. Procedia-Social and Behavioral Sciences,2013,104: 129-138.

[8]LI Y, LIU L, XIAO F, et al. Effective temperature for predicting permanent deformation of asphalt pavement[J]. Construction and Building Materials,2017,156: 871-879.

[9]张承烨, 李卓轩, 曹进德. 基于随机k-近邻集成算法的网络流量入侵检测[J]. 南通大学学报(自然科学版), 2023,22(3): 26-32. (ZHANG Chengye, LI Zhuoxuan, CAO Jinde. Network intrusion detection based on random k-nearest neighbor ensemble algorithm[J]. Journal of Nantong University (Natural Science Edition), 2023,22(3): 26-32. (in Chinese))

[10]MIRABDOLAZIMI S M, SHAFABAKHSH G. Rutting depth prediction of hot mix asphalts modified withforta fiber using artificial neural networks and genetic programming technique[J]. Construction and Building Materials,2017,148: 666-674.

[11]ZIARI H, AMINI A, GOLI A, et al. Predicting rutting performance of carbon nano tube (CNT) asphalt binders using regression models and neural networks[J]. Construction and Building Materials,2018,160: 415-426.

[12]SHAN A, HAFEEZ I, HUSSAN S, et al. Predicting the laboratory rutting response of asphalt mixtures using different neural network algorithms[J]. International Journal of Pavement Engineering,2022,23(6): 1948-1956.

[13]QADIR A, GAZDER U, CHOUDHARY K U N. Artificial neural network models for performance design of asphalt pavements reinforced with geosynthetics[J]. Transportation Research Record: Journal of the Transportation Research Board,2020,2674(8): 319-326.

[14]WANG S C. Interdisciplinary Computing in Java Programming[M]. Boston, MA: Springer, 2003.

[15]MISHRA M, SRIVASTAVA M. A view of artificial neural network[C]//2014 International Conference on Advances in Engineering & Technology Research (ICAETR-2014). Unnao, India: IEEE, 2014: 1-3.

[16]MAIND S B, WANKAR P. Research paper on basic of artificial neural network[J]. International Journal on Recent and Innovation Trends in Computing and Communication,2014,2(1): 96-100.

[17]SHANMUGANATHAN S A S. Artificial Neural Network Modelling[M]. Cham: Springer, 2016.

[18]SIMPSON A L, DALEIDEN J F, HADLEY W O. Rutting analysis from a different perspective[J]. Transportation Research Record,1995,1473: 9-16.

[19]SHAFABAKHSH G H, ANI O J, TALEBSAFA M. Artificial neural network modeling (ANN) for predicting rutting performance of nano-modified hot-mix asphalt mixtures containing steel slag aggregates[J]. Construction and Building Materials,2015,85: 136-143.

[20]ABDELAZIZ N, ABD EL-HAKIM R T, EL-BADAWY S M, et al. International roughness index prediction model for flexible pavements[J]. International Journal of Pavement Engineering,2020,21(1): 88-99.

[21]BARUA L, ZOU B, NORUZOLIAEE M, et al. A gradient boosting approach to understanding airport runway and taxiway pavement deterioration[J]. International Journal of Pavement Engineering,2021,22(13): 1673-1687.

[22]王旭东. 从试验环道看长寿命路面的“中国制造”[J]. 中国公路, 2020(14): 30-32. (WANG Xudong. Viewing the “Made in China” of long life road surface from the experimental ring road [J]. China Highway,2020(14): 30-32. (in Chinese))

[23]张蕾, 周兴业, 王旭东. 基于RIOHTrack足尺加速加载试验的长寿命沥青路面行为研究进展[J]. 科学通报, 2020,65(30): 3247-3258. (ZHANG Lei, ZHOU Xingye, WANG Xudong. Research progress of long-life asphalt pavement behavior based on the RIOHTrack full-scale accelerated loading test[J]. Chinese Science Bulletin,2020,65(30): 3247-3258. (in Chinese))

[24]LI Z, SHI X, CAO J, et al. CPSO-XGBoost segmented regression model for asphalt pavement deflection basin area prediction[J]. Science China Technological Sciences,2022,65(7): 1470-1481.

[25]ZEIADA W, HAMAD K, OMAR M, et al. Investigation and modelling of asphalt pavement performance in cold regions[J]. International Journal of Pavement Engineering,2019,20(8): 986-997.

[26]BOMMERT A, SUN X, BISCHL B, et al. Benchmark for filter methods for feature selection in high-dimensional classification data[J]. Computational Statistics & Data Analysis,2020,143: 106839.

[27]NASERI H, WAYGOOD E O D, WANG B B, et al. How to predict climate change stage of change accurately: proposing a new feature selection technique[C］//Transportation Research Board 101st Annual Meeting. Washington DC, 2022.

[28]LIU L, ZHOU J, AN X, et al. Using fuzzy theory and information entropy for water quality assessment in Three Gorges region, China[J]. Expert Systems With Applications,2010,37(3): 2517-2521.

[29]ZOU Z H, YUN Y, SUN J N. Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment[J]. Journal of Environmental Sciences,2006,18(5): 1020-1023.

[30]KIREMIRE A R. The application of the Pareto principle in software engineering[Z/OL]. 2021[2024-11-28]. https://studylib.net/doc/8372157/the-application-of-the-pareto-principle-in-software.

[31]REFAEILZADEH P, TANG L, LIU H. Cross-validation[C]//Encyclopedia of Database Systems. Boston, MA: Springer, 2009: 532-538.

[32]NGARAMBE J, IRAKOZE A, YUN G Y, et al. Comparative performance of machine learning algorithms in the prediction of indoor daylight illuminances[J]. Sustainability,2020,12(11): 4471.

[33]LUNDBERG S M, LEE S I. A unified approach to interpreting model predictions[C］//31st Conference on Neural Information Processing Systems. Long Beach, CA, 2017.

[34]BREIMAN L. Random forests[J]. Machine Learning,2001,45: 5-32.

[35]CHEN T, GUESTRIN C. XGBoost: a scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: ACM, 2016: 785-794.

[36]PROKHORENKOVA L, GUSEV G, VOROBEV A, et al. CatBoost: unbiased boosting with categorical features[C］//Advances in Neural Information Processing Systems 31. 2018: 6138-6148.

[37]KE G L, MENG Q, FINLEY T, et al. LightGBM: a highly efficient gradient boosting decision tree[C］//Advances in Neural Information Processing Systems 30. 2017: 3147-3155.

[38]ZHOU Z H, FENG J. Deep forest[J]. National Science Review,2019,6(1): 74-86.

[39]PETERSON L. K-nearest neighbor[J]. Scholarpedia,2009,4(2): 1883.

[40]HEARST M A, DUMAIS S T, OSUNA E, et al. Support vector machines[J]. IEEE Intelligent Systems and Their Applications,1998,13(4): 18-28.

[41]MYLES A J, FEUDALE R N, LIU Y, et al. An introduction to decision tree modeling[J]. Journal of Chemometrics,2004,18(6): 275-285.

[42]GARDNER M W, DORLING S R. Artificial neural networks (the multilayer perceptron): a review of applications in the atmospheric sciences[J]. Atmospheric Environment,1998,32(14/15): 2627-2636.

[43]李卓轩, 林凯迪, 郭建华, 等. 基于车联网数据的运输车辆安全评价模型[J]. 南通大学学报(自然科学版), 2020,19(1): 26-32. (LI Zhuoxuan, LIN Kaidi, GUO Jianhua, et al. Transportation vehicle safety evaluation model based on vehicle network data[J]. Journal of Nantong University (Natural Science Edition), 2020,19(1): 26-32. (in Chinese))

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