Today, authorities responsible for the operation of highways aim to provide comfort to road users as well as safety while driving. While driving, the most important component that determines comfort for road users is the pavement. The relative effects of various surface distress types in bituminous, hot mixed asphalt pavements on the International Roughness Index (IRI) component—used to evaluate the present performance, and hence the comfort, of pavements—are determined in this study. The presence of only one type of surface distress is very difficult to achieve in practice, especially in regards to pavements where a high degree of deterioration is observed. The presence of different types of surface distress in road pavements, due to similar problems in very close positions and even in nested forms, makes it difficult to assess this issue. The relationships between surface distress and IRI have been modelled to overcome this challenge. To this end, the Multivariate Adaptive Regression Splines (MARS) modelling approach, which is very successful in investigating the relationships between a large number of independent variables and dependent variables, has been used. The sensitivities of the surface distress inputs are evaluated singularly by means of a model with 29 input variables calibrated using the pavement distress data collected in 3295 highway pavement sections. As a result of this analysis, the sensitivity of surface distress inputs collected, as an area, has been determined to have an effect on the increase in IRI. The results are interpreted with the help of figures and tables.
The pavement condition index (PCI) provides an indication of the current performance of a pavement, and takes the form of a numerical rating, with 0 being the worst possible condition and 100 being the best. The PCI is a subjective method for the evaluation of pavements that is based on inspection and observation. Knowledgeable and experienced pavement engineers make a systematic evaluation of road conditions by driving on the road network, and enter their observations into a database for further evaluation. This case study made use of PAVER software to obtain PCI values for twenty road sections that experience different volumes of traffic in the Samsun region. First, the condition of each road section was evaluated using the PAVER procedure, after which, vertical vibration data was recorded for the same road sections using accelerometers mounted at three different locations within the car: the driver's seat, over the central axis and the front passenger seat. The car was driven at a constant 40 km/h during the tests. In the final stage, the relationship between the vibration data and the PCI values obtained during the site study was examined, and PCI prediction models were created for use in the assessment of urban roads.
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