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Predicting IRI Using Machine Learning Techniques.

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Title: Predicting IRI Using Machine Learning Techniques.
Authors: Sharma, Ankit1 ankitsharma336@gmail.com, Sachdeva, S. N.1,2, Aggarwal, Praveen1
Source: International Journal of Pavement Research & Technology. Jan2023, Vol. 16 Issue 1, p128-137. 10p.
Subjects: Deep learning, Machine learning, Standard deviations, Random forest algorithms
Abstract: The behaviour of pavement structure to varying degrees of loads, climate conditions, traffic, drainage conditions and dimensions of road cause difficulty in deciding the maintenance/rehabilitation task on the pavement. International Roughness Index (IRI) is the most commonly used criteria for evaluating pavement performance and determining maintenance/rehabilitation requirements of the pavements. In a road network comprising hundreds of km of the road, it becomes difficult to accurately predict the road's IRI. The data have been taken from a public database of roads, i.e. long-term pavement performance. In this study, machine learning models have been studied to understand/analyze the IRI of roads. The evaluation/performance of regression models has been done on the basis of commonly used statistical measures. Gradient boosting machine (GBM) model performed best on the test as well as train data set out of five used models, namely GBM, deep learning, extremely random forest, distributed random forest, and generalized linear model. Performance of GBM in the testing dataset had root mean square error (RMSE = 0.176003), root mean square log error (RMSLE = 0.074924), mean average error (MAE = 0.126345), mean square error (MSE = 0.030977), which was minimum of five models, and R2 (0.86572) which was maximum. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Pavement Research & Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Predicting IRI Using Machine Learning Techniques.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Pavement+Research+%26+Technology%22">International Journal of Pavement Research & Technology</searchLink>. Jan2023, Vol. 16 Issue 1, p128-137. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink>
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  Label: Abstract
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  Data: The behaviour of pavement structure to varying degrees of loads, climate conditions, traffic, drainage conditions and dimensions of road cause difficulty in deciding the maintenance/rehabilitation task on the pavement. International Roughness Index (IRI) is the most commonly used criteria for evaluating pavement performance and determining maintenance/rehabilitation requirements of the pavements. In a road network comprising hundreds of km of the road, it becomes difficult to accurately predict the road's IRI. The data have been taken from a public database of roads, i.e. long-term pavement performance. In this study, machine learning models have been studied to understand/analyze the IRI of roads. The evaluation/performance of regression models has been done on the basis of commonly used statistical measures. Gradient boosting machine (GBM) model performed best on the test as well as train data set out of five used models, namely GBM, deep learning, extremely random forest, distributed random forest, and generalized linear model. Performance of GBM in the testing dataset had root mean square error (RMSE = 0.176003), root mean square log error (RMSLE = 0.074924), mean average error (MAE = 0.126345), mean square error (MSE = 0.030977), which was minimum of five models, and R2 (0.86572) which was maximum. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Pavement Research & Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s42947-021-00119-w
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      – Code: eng
        Text: English
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        StartPage: 128
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      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Machine learning
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      – SubjectFull: Standard deviations
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      – SubjectFull: Random forest algorithms
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              Text: Jan2023
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              Y: 2023
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