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Prediction of International Roughness Index Using CatBooster and Shap Values

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Abstract

International Roughness Index (IRI) is the performance index of pavements that exhibits the efficiency of pavement smoothness. Road roughness is a fundamental element used for determining the performance of pavements and the ride quality of road users, therefore, this research aims to develop the precise IRI prediction model for flexible pavements using advanced machine learning algorithms including supervised methods. This research is directed toward accessing the functional performance of the pavements through long-term pavement performance (LTPP) databases. For developing the model, the incorporated dataset includes a set of functional attributes from general pavement studies (GPS-1, GPS-2 and GPS-6) and specific pavement studies (SPS-1, SPS-3 and SPS-5). The developed algorithms showed that the machine learning algorithms are more precise and accurate in predicting the IRI than the traditional regression approaches. The machine learning algorithms use the shapely additive explanation (SHAP) values to access the feature significance for each independent element on the predictive performance. The analysis showed that CatBooster Regression outperformed the random forest regression, artificial neural network (ANN), and the simple regression models in terms of mean square error and prediction quality with a coefficient of determination up to 0.99. The study depicted that it is possible to correlate the roughness index with pavement and structural, climatic and distress parameters that can be utilized for pavement maintenance.

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Availability of Data and Materials

All the datasets implemented in the research are available from the Department of Transportation United States Government website at https://infopave.fhwa.dot.gov.

Abbreviations

LTTP:

Long term pavement performance

MLR:

Multiple linear regression

RF:

Random forest

ANN:

Artificial neural network

GP:

Genetic programming

SHAP:

Shapely additive explanations

GMDH:

Group method of data handling

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Acknowledgements

The authors are thankful to the Long Term Pavement Performance-Federal Highway Administration, United States of America, for providing the source data collected through online database for the project “Prediction of International Roughness Index Using Machine Learning Algorithms” as a part of research study.

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Authors and Affiliations

  1. Department of Civil Engineering, Thapar Institute of Engineering and Technology, Thapar University, Patiala, Punjab, 147004, India

    Saket Bral & Tanuj Chopra

  2. Department of Civil Engineering, University of Manitoba, Winnipeg, MB, R3T 2H4, Canada

    Patnala Phani Kumar

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  1. Saket Bral
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Contributions

SB: conceptualization, methodology, designed and tested the algorithms in the research, writing-original draft preparation. PPK: investigation, resources, data curation, review and editing coordinated this research. TC: writing- review & editing, visualization, supervision.

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Correspondence to Saket Bral.

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Bral, S., Kumar, P.P. & Chopra, T. Prediction of International Roughness Index Using CatBooster and Shap Values. Int. J. Pavement Res. Technol. 17, 518–533 (2024). https://doi.org/10.1007/s42947-022-00253-z

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