Anahtar KelimelerÜstyapı performansı, Yüzey bozulmaları, IRI Özet: Esnek üstyapılarda görülen yüzey bozulmaları ile düzgünsüzlük arasında ilişkilerin araştırıldığı çalışmalar incelendiğinde sınırlı sayıda yüzey bozulma türünün dikkate alındığı görülmektedir. Literatüre katkı sağlamak amacıyla bu çalışmada, 13 adet yüzey bozulma türü ve bozulma şiddetleri ile birlikte toplam 32 adet üstyapı bozulması ile IRI arasındaki ilişkilerin matematiksel modelleme analizi yapılmıştır. Modelleme çalışmalarında doğrusal regresyon, değişkenli uyarlamalı regresyon eğrileri (MARS) ve yapay sinir ağları (YSA) yaklaşımları kullanılmıştır. Oluşturulan modellerin tahmin yetenekleri ortalama mutlak hata (OMH), kök ortalama karesel hata (KOKH), ortalama mutlak göreceli hata (OMGH) ve regresyon katsayısı (R 2 ) istatistiksel karşılaştırma yöntemleri kullanılarak değerlendirilmiştir. Tahmin yeteneği en yüksek olan modelin YSA yaklaşımı kullanılarak oluşturulan model olduğu tespit edilmiştir. Ayrıca, YSA yaklaşımında girdi değişkenlerinin çıktı değişkeni üzerindeki etkileri bağlantı ağırlıklarına göre değerlendirilmiştir. Bu değerlendirmeye göre, üstyapı bozulmaları oluşma nedenlerine (mekanizmalarına) göre incelendiğinde, IRI üzerinde % 43.8 yük kaynaklı, % 39 diğer sebepler kaynaklı ve % 17.2 iklim kaynaklı bozulmaların etkili olduğu sonucuna ulaşılmıştır. Some Approaches to the Modeling of Relationships between Surface Distresses and Roughness in Hot-Mixed Asphalts Keywords Pavement performance, Surface distresses, IRIAbstract: When studies investigating the relationship between pavement roughness and surface distresses seen in flexible pavements are examined, a limited number of surface distress types are considered to be taken into account. In order to contribute to the literature, in this study, mathematical modeling analysis of relations between IRI and a total of 32 pavement distresses with 13 types of surface distress types and severities were carried out. Linear regression, multivariate adaptive regression splines (MARS) and artificial neural networks (ANN) approaches are used in modeling studies. Estimating capabilities of the analyzed mathematical models were evaluated using statistical comparison methods such as mean absolute error (MAE), root mean squared error (RMSE), mean absolute relative error (MARE) and regression coefficient (R 2 ). The model constructed using the ANN approach was ascertained to be the model with the highest prediction accuracy. In addition, in the ANN approach, the effects on the output variable of the input variables are evaluated according to the connection weights. According to this evaluation, when pavement distresses are examined according to the cause of distress, it is concluded that 43.8 % of IRI is caused by load, 39 % is caused by other causes and 17.2 % is caused by climate.
This investigation was carried out to study the effect of fly ash on the field performance of asphalt pavement. Fly ash was used in stone mastic asphalt (SMA) during the construction of the wearing course of Gaziantep Ring Road. This SMA is the first application using fly ash in our country. The performance properties (rut depth and roughness and cracking in the pavement) were determined. In this research the inertial profilometer with camera has been used to measure rut depth, roughness and cracking of the road in 5th Regional Division of Highways of Turkey. As a result of this study, surface performance properties have been determined. Additional data will continue to be collected and analyzed during the next years. Performance models will be developed.
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