International Roughness Index Prediction for Jointed Plain Concrete Pavements Using Regression and Machine Learning Techniques
Rigid pavements are recently gaining vast interest for their durability and large traffic volume endurance. This raises the need for a robust method to predict their long-term performance to support decisions on their future maintenance and rehabilitation requirements. One of the most descriptive parameters of pavement ride quality is the International Roughness Index (IRI). IRI can be used as a performance indicator for the level of serviceability of rigid pavement. This study presents the evaluation of 21 variables covering climate, traffic, and structural parameters in predicting IRI for jointed plain concrete pavement (JPCP) sections using 1,414 data points acquired from the Long-Term Pavement Performance (LTPP) database. It was found that only 10 variables are significant in the IRI prediction for JPCP. Moreover, four modeling techniques were applied, namely, linear regression, multivariate adaptive regression splines, Gaussian process regression, and artificial neural network. It was found that a deep ANN model with a structure of one input layer, three hidden layers, and one output layer [10-36-18-9-1] gives the highest IRI prediction accuracy with root mean square error and coefficient of determination (R2) of 0.117 and 0.92, respectively. Finally, a sensitivity analysis was performed to evaluate the impact of each input variable on the accuracy of IRI prediction for JPCP sections, and it was found that the most influential variables are initial IRI, pavement age, mean and standard deviation of humidity, standard deviation of evaporation, mean and standard deviation of freezing index, pavement compressive strength, ratio between pavement and base layers thickness, and finally an estimated parameter called site factor which is a function of (age, freezing index and soil percentage passing sieve no. 200).
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
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Supplemental Notes:
- Amany M. Suliman https://orcid.org/0009-0008-5155-8553© National Academy of Sciences: Transportation Research Board 2023.
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Authors:
- Suliman, Amany M
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0009-0008-5155-8553
- Awed, Ahmed M
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0000-0001-7951-1636
- Abd El-Hakim, Ragaa T
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0000-0003-4148-6855
- El-Badawy, Sherif M
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0000-0001-8348-1580
- Publication Date: 2024-2
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 235-250
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Serial:
- Transportation Research Record: Journal of the Transportation Research Board
- Volume: 2678
- Issue Number: 2
- Publisher: Sage Publications, Incorporated
- ISSN: 0361-1981
- EISSN: 2169-4052
- Serial URL: http://journals.sagepub.com/home/trr
Subject/Index Terms
- TRT Terms: Machine learning; Predictive models; Ride quality; Rigid pavements; Roughness; Service life
- Subject Areas: Data and Information Technology; Highways; Pavements;
Filing Info
- Accession Number: 01885857
- Record Type: Publication
- Files: TRIS, TRB
- Created Date: Jun 22 2023 3:44PM