Predicting IRI Using Machine Learning Techniques

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 evalua...

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Bibliographic Details
Published in:International journal of pavement research & technology 2023-01, Vol.16 (1), p.128-137
Main Authors: Sharma, Ankit, Sachdeva, S. N., Aggarwal, Praveen
Format: Article
Language:English
Subjects:
Building Construction and Design
Civil Engineering
Deep learning
Engineering
Generalized linear models
Machine learning
Mean square errors
Mean square values
Original Research Paper
Pavements
Performance evaluation
Regression models
Rehabilitation
Road maintenance
Roads & highways
Root-mean-square errors
Statistical analysis
Statistical models
Structural Materials
Transportation networks
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Summary: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 R 2 (0.86572) which was maximum.
ISSN:1996-6814
1997-1400
DOI:10.1007/s42947-021-00119-w