Determination of Pavement Performance Thresholds for Comfortable Riding on Urban Roads


Kırbaş U., KARAŞAHİN M.

JOURNAL OF TESTING AND EVALUATION, cilt.47, sa.1, ss.57-77, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 47 Sayı: 1
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1520/jte20170319
  • Dergi Adı: JOURNAL OF TESTING AND EVALUATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.57-77
  • Anahtar Kelimeler: pavement performance, threshold, ride comfort, pavement condition index, ARTIFICIAL NEURAL-NETWORKS, ASPHALT PAVEMENTS, FUZZY-LOGIC, ROUGHNESS, CONCRETE, TERM
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

Whole body vibration (WBV) exposure inside vehicles during transportation affects the driver and passengers adversely, particularly in terms of comfort, health, safety, etc. The main causes of the WBV to which the driver and passengers are exposed are the mechanical structure of the vehicle and road pavements. This study aimed to investigate the relationships between the current performance of pavements and ride comfort for standard passenger cars on urban hot mix asphalt-paved roads, and to specify the threshold values for the pavement condition index (PCI) according to comfort criteria recognized by current standards. For this purpose, PCI values were identified according to the PAVER system on road sections with various performance levels. Vibration data in a vertical direction were measured as defined by ISO 2631-1, Mechanical Vibration and Shock - Evaluation of Human Exposure to Whole-Body Vibration - Part 1: General Requirements, inside the vehicle on the driver seat at ride (driving) speeds of 20, 30, 40, and 50 km/h on the same road section. Assessing these measurements according to the method described in the same standard, a(wz) values were calculated. Subsequently, the relationships between PCI and a(wz) for each ride speed were mathematically modeled by employing artificial neural network (ANN) and fuzzy logic methods. With the aid of these mathematical models, comfort thresholds were determined for each ride speed within an interval of 0 to 100 PCI. The ANN approach was found to be more successful than the fuzzy logic in evaluating the data. The results were evaluated comparatively.