Predicting Pavement Condition Index Using International Roughness Index in a Dense Urban Area

By S. Arhin, L. Williams, Asteway Ribbiso, Melissa F. Anderson. 2015

97 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d6a20b84d92e06bebdc5b2f28a0613476e5c633d

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Sherif M. El-Badawy
2. Amr A. Elhadidy
3. M. Hossain
4. Emad Elbeltagi
5. M. S. Miah
6. L. S. P. Gopisetti

Citations in the corpus

• Artificial neural network modelling to predict international roughness index of rigid pavements (2020, methodology): “IRI is measured by automation using a road profiler, which produces a series of numbers to represent the profile of the road by combining a reference elevation, height relative to the reference, and longitudinal distance [7].”
• A simplified pavement condition index regression model for pavement evaluation (2019, methodology): “In another work, Arhin et al. (2015) proposed models predicting PCI using IRI for a dense urban area by functional classification and pavement type in the District of Columbia.”