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Pavement roughness, commonly estimated by the International Roughness Index (IRI), plays an essential role in pavement assessment. However, accessibility to IRI data requires the operation of profiling equipment, which may be costly for the agencies. In this regard, the use of IRI prediction models from pavement distresses could be an alternative solution. In this research, 507 kilometres of asphalt pavements in Kermanshah, Iran, were investigated using IRI and Pavement Surface and Evaluation Rating (PASER) as a rapid and cost-effective index. The IRI prediction models from PASER were developed using regression ( R2  = 0.66) and Artificial Neural Network (ANN) ( R2  = 0.69). Regarding the restrictions of the results, the data clustering using k-means and fuzzy c-means (FCM) was taken into consideration to acquire the IRI ranges based on the pavement condition. Using the FCM as the superior approach, the IRI prediction model from PASER and the corresponding membership degrees was developed based on ANN. The results of model development ( R2  = 0.97) and validation ( R2  = 0.85) indicated the desirable performance of the ANN model. This case study can be counted as a practical approach for the agencies to economically investigate the pavement condition, predict the roughness, and also make decisions for maintenance targets at the network level.

Estimation of IRI from PASER using ANN based on k-means and fuzzy c-means clustering techniques: a case study