A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Determination of ride comfort thresholds based on international roughness index for asphalt concrete pavements
The study is aimed to mathematically model the relationship between the amount of whole-body vibration exposed in a passenger car type vehicle and the ride speed of the vehicle in the same section and the International Roughness Index (IRI), which is used as a pavement performance indicator. Vibration measurements were analysed according to the evaluation method determined in the ISO 2631 standard, and frequency-weighted root-mean-square acceleration (aw) values were obtained in the vertical direction. Mathematical relationships between real measurement data performed in 1114 road sections were investigated using regression analysis, artificial neural networks (ANN) and Adaptive Neural Fuzzy Inference System (ANFIS) modelling techniques. The models' performance levels were determined using comparison metrics, and it was determined that ANFIS was the model with the best goodness of fit, with the regression coefficient 0.955, mean absolute error (MAE) 0.013, and root mean squared error (RMSE) 0.020. The threshold values affecting the ride comfort of IRI and the ride comfort values corresponding to the IRI limit values recommended by the Federal Highway Administration (FHWA) were determined through the ANFIS mathematical model. Then, a sensitivity analysis was conducted to determine the effects of a certain increase in the IRI value on discomfort.
Determination of ride comfort thresholds based on international roughness index for asphalt concrete pavements
The study is aimed to mathematically model the relationship between the amount of whole-body vibration exposed in a passenger car type vehicle and the ride speed of the vehicle in the same section and the International Roughness Index (IRI), which is used as a pavement performance indicator. Vibration measurements were analysed according to the evaluation method determined in the ISO 2631 standard, and frequency-weighted root-mean-square acceleration (aw) values were obtained in the vertical direction. Mathematical relationships between real measurement data performed in 1114 road sections were investigated using regression analysis, artificial neural networks (ANN) and Adaptive Neural Fuzzy Inference System (ANFIS) modelling techniques. The models' performance levels were determined using comparison metrics, and it was determined that ANFIS was the model with the best goodness of fit, with the regression coefficient 0.955, mean absolute error (MAE) 0.013, and root mean squared error (RMSE) 0.020. The threshold values affecting the ride comfort of IRI and the ride comfort values corresponding to the IRI limit values recommended by the Federal Highway Administration (FHWA) were determined through the ANFIS mathematical model. Then, a sensitivity analysis was conducted to determine the effects of a certain increase in the IRI value on discomfort.
Determination of ride comfort thresholds based on international roughness index for asphalt concrete pavements
Kırbaş, Ufuk (author)
2023-12-06
Article (Journal)
Electronic Resource
English
Analytical Evaluation of Ride Comfort on Asphalt Concrete Pavements
| British Library Online Contents | 2016
Investigation of ride comfort limits on urban asphalt concrete pavements
| Online Contents | 2016
Investigation of ride comfort limits on urban asphalt concrete pavements
| Taylor & Francis Verlag | 2018
Development of Speed-Related Ride Quality Thresholds Using International Roughness Index
| British Library Conference Proceedings | 2006
Development of Speed-Related Ride Quality Thresholds Using International Roughness Index
| British Library Online Contents | 2006