TY - JOUR
T1 - Machine Learning Approach to Predict International Roughness Index Using Long-Term Pavement Performance Data
AU - Damirchilo, Farshid
AU - Hosseini, Arash
AU - Mellat Parast, Mahour
AU - Fini, Elham H.
N1 - Publisher Copyright:
© 2021 American Society of Civil Engineers.
PY - 2021/12/1
Y1 - 2021/12/1
N2 - On-time pavement maintenance and rehabilitation are required to maintain or improve pavement roughness, which is an indicator of pavement performance and road safety. Better prediction of maintenance time can help in budget planning and allocation for highways as well as a better and safer driving experience for drivers. In this research, the International Roughness Index (IRI) for asphalt concrete pavement is predicted based on the 12,637 observations in the Long-Term Pavement Performance (LTPP) data set for 1,390 roads and highways in 50 states of the US and the District of Columbia from 1989 to 2018. To identify the research gaps and to better understand the state-of-the-art research in IRI prediction, a systematic literature review (SLR) has been performed to develop a comprehensive view of machine learning techniques used for IRI prediction. We used a machine learning algorithm that can handle missing data in the LTPP data set. Extreme gradient boosting (XGBoost) was used to predict the IRI. Also, the support vector regression (SVR) and random forest (RF) models were used to compare the results. Our results show that XGBoost provides a better model fit in terms of mean absolute error and coefficient of determination. Moreover, our results show that No.-200-passing, hydraulic conductivity, and equivalent single-axle loads in thousands (KESAL) are the most important factors in predicting the IRI.
AB - On-time pavement maintenance and rehabilitation are required to maintain or improve pavement roughness, which is an indicator of pavement performance and road safety. Better prediction of maintenance time can help in budget planning and allocation for highways as well as a better and safer driving experience for drivers. In this research, the International Roughness Index (IRI) for asphalt concrete pavement is predicted based on the 12,637 observations in the Long-Term Pavement Performance (LTPP) data set for 1,390 roads and highways in 50 states of the US and the District of Columbia from 1989 to 2018. To identify the research gaps and to better understand the state-of-the-art research in IRI prediction, a systematic literature review (SLR) has been performed to develop a comprehensive view of machine learning techniques used for IRI prediction. We used a machine learning algorithm that can handle missing data in the LTPP data set. Extreme gradient boosting (XGBoost) was used to predict the IRI. Also, the support vector regression (SVR) and random forest (RF) models were used to compare the results. Our results show that XGBoost provides a better model fit in terms of mean absolute error and coefficient of determination. Moreover, our results show that No.-200-passing, hydraulic conductivity, and equivalent single-axle loads in thousands (KESAL) are the most important factors in predicting the IRI.
KW - Data science
KW - International Roughness Index (IRI)
KW - Long-term pavement performance (LTPP)
KW - Machine learning
KW - Pavement performance
KW - Systematic literature review
UR - http://www.scopus.com/inward/record.url?scp=85113873533&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85113873533&partnerID=8YFLogxK
U2 - 10.1061/JPEODX.0000312
DO - 10.1061/JPEODX.0000312
M3 - Article
AN - SCOPUS:85113873533
SN - 2573-5438
VL - 147
JO - Journal of Transportation Engineering Part B: Pavements
JF - Journal of Transportation Engineering Part B: Pavements
IS - 4
M1 - 04021058
ER -