Research on Relationship between Riding Quality and Pavement Distress of Asphalt Pavement

2012 ◽  
Vol 178-181 ◽  
pp. 1306-1313 ◽  
Author(s):  
Bo Peng ◽  
Lu Hu ◽  
Yang Sheng Jiang ◽  
Liang Yun

For asphalt pavement performance evaluation, pavement roughness, which is subject to cracks, potholes, road repairs and so on, is a major factor to influence riding quality. Therefore, riding quality is partly correlated with pavement distress, and the relationship can be transformed to that between pavement roughness and distress rate. However, this relationship is not clear, and not reflected in existing evaluation models. Thus, correlation analysis and non-parametric test of independent samples were applied in this paper to find that, international roughness index and pavement distress rate are significantly different due to different grades of roads, then, linear and nonlinear regression were used to analyze the relationships between international roughness index and pavement distress rate for different road grades. Furthermore, original data were processed by logarithmic transformation, radical transformation, exponential transformation and so on, based on which, corresponding relationships were analyzed by linear and nonlinear regression. Finally, best models to describe relationships between international roughness index and pavement distress rate for different road grades were solved out, and corresponding 90% confidence intervals were computed. Research in this paper offers a reference for improving asphalt pavement performance evaluation system and models, which is conducive to further theoretical research and practice.

2014 ◽  
Vol 1030-1032 ◽  
pp. 754-757
Author(s):  
Zheng Wang ◽  
Wei Zhang ◽  
Jia Jia Cheng ◽  
Meng Chen ◽  
Wen Jing Liu

The effect of pavement roughness on the roadbed and retaining structure underground was studied. Three different groups of International Roughness Index (IRI) were analyzed based on 6221 data collection instrument in this paper. Results show that different pavement roughness has different effect on retaining structure. Additionally, the vibration RMS increases with IRI when it is in the range of normal driving for the car, but amplitude of the IRI is larger. Finally, the main factors which influence the stability of structure and some corresponding improving measures are presented.


Author(s):  
Kevin K. McGhee

In the summer of 1996 the Virginia Department of Transportation (VDOT) initiated the pilot of a new special provision regarding the smoothness of asphalt pavement surfaces. This special provision is based on the international roughness index (IRI) and is administered with a laser-equipped South Dakota–style inertial road profiler. A critical assessment of the nontraditional equipment and methods used to administer the special provision is provided. Issues addressed in the critique include provision exemptions, the ability to identify and contend with construction variability, and peculiarities of the equipment that affect the ability of VDOT to administer a modern acceptance provision.


2011 ◽  
Vol 97-98 ◽  
pp. 203-207 ◽  
Author(s):  
Ke Zhen Yan ◽  
Zou Zhang

An emerging machine learning technique, the support vector machine (SVM), based on statistical learning theory is very good at analyzing small samples and non-linear regression problem. The particle swarm optimize (PSO) can avoid the man-made blindness and enhance the efficiency and capability in forecasting. In this paper, SVM is applied to establish a model for asphalt pavement performance evaluation, optimized by PSO algorithm. In road engineering, PCI, SSI, SRI and IRI were selected as the asphalt pavement performance evaluation indexes, but it is difficult to get pavement condition index. This paper describes the relationships among the four indicators, and SSI, SRI and IRI were used for establishing the prediction model to forecast PCI based on PSO-SVM. The results show that the method is simple and effective for evaluation of asphalt pavement performance.


TRANSPORTES ◽  
2020 ◽  
Vol 28 (1) ◽  
pp. 147-159
Author(s):  
Jorge Braulio Cossío Durán ◽  
José Leomar Fernandes Júnior

Pavimentos irregulares são geralmente responsáveis por acelerações verticais (VA) que afetam as aeronaves, aumentam a distância de parada e dificultam a leitura dos instrumentos de navegação na cabine dos pilotos. O International Roughness Index (IRI) e o Boeing Bump Index (BBI) são utilizados atualmente para quantificar a irregularidade longitudinal dos pavimentos aeroportuários e identificar seções que demandam atividades de manutenção e reabilitação (M&R). Contudo, tais índices baseiam-se apenas nas respostas dinâmicas de um automóvel a 80 km/h às irregularidades longitudinais dos pavimentos rodoviários, bem como nas características físicas das irregularidades (comprimento e altura), respectivamente, ignorando o efeito das VA nas aeronaves. Ainda, limites críticos atuais, sugeridos por Sayers & Karamihas e ANAC para IRI (2,0 e 2,5 m/km, respectivamente) e pela FAA para BBI (1,0) podem subestimar a condição real do pavimento. Este artigo avalia o efeito da irregularidade longitudinal nas acelerações na cabine dos pilotos (VACP) e no centro de gravidade (VACG). O software ProFAA permitiu calcular os índices e simular as VA em 4 aeronaves representativas atravessando 20 pistas de pouso e decolagem em 10 velocidades de taxiamento variando de 37 a 370 km/h. Comparações estatísticas e análises de regressão foram realizadas. Principais resultados mostram que VACP é 50% maior do que VACG e que ultrapassa o limite critico de 0,40 g quando o IRI e BBI estão acima de 3,7 m/km e 0,20, respectivamente. Um estudo de caso é também apresentado para comparar esses limites e sugere que a tomada de decisão baseada em IRI e VA pode trazer diferenças significativas na quantidade de atividades de M&R.


Author(s):  
Jinsong Qian ◽  
Chen Jin ◽  
Jiake Zhang ◽  
Jianming Ling ◽  
Chao Sun

Pavement performance prediction after maintenance and rehabilitation is important to pavement management. A two-parameter exponential international roughness index (IRI) regression model for thin hot mix asphalt overlay was developed based on information from the U.S. Long Term Pavement Performance (LTPP) database. The model influence parameters α and β, which represent the initial IRI as the thin overlay completion and shape factor of IRI deterioration curve, were statistically analyzed. The results suggested that the IRI deterioration trends in high-temperature and low-temperature regions are different. This is because β was mainly affected by the structural strength and equivalent single axle loads in the high and medium temperature region and mainly affected by the average annual precipitation in low temperature region. In-situ data from LTPP database was used to verify the IRI prediction model, and it was found that the predicted IRI and measured IRI exhibited similar trends.


2013 ◽  
Vol 742 ◽  
pp. 104-108
Author(s):  
Shao Wen Liu ◽  
Xiao Zhang

In this paper, pavement roughness is assumed as random stationary variable and used as the exciting force of theoretical analyses of the quarter car model of International Roughness Index (IRI). From the frequency response function of the quarter car, the response function of the displacement difference between sprung and unsprung mass is obtained based on random process theory. Then the relationship between IRI and power spectral density (PSD) is established from statement characteristic of the response function. Finally, the longitudinal road profiles of typical asphalt roads in China are used to validate the proposed model.


2014 ◽  
Vol 505-506 ◽  
pp. 180-183 ◽  
Author(s):  
Zong Tao Zhang ◽  
Quan Man Zhao ◽  
Wan Qiao Yang

The most widely used pavement roughness index is the international roughness index (IRI), but it is a poor predictor of ride comfort. In addition, the rider has not yet been included in the vehicle model used to evaluate pavement roughness. In this paper, in order to evaluate the comfort of the rider directly and consider the effects on ride comfort of pitch movement, a five-degree-freedom vibration model was built when a rider was added to a pitch-plane vehicle model. The vertical weighted root-mean-square (RMS) acceleration of the rider was suggested to be pavement roughness indices, which were related to ride comfort, respectively. The new roughness indices were calculated and a new pavement roughness evaluation method was developed.


2021 ◽  
Vol 13 (4) ◽  
pp. 2184
Author(s):  
Yu Tian ◽  
Shifu Liu ◽  
Le Liu ◽  
Peng Xiang

Pavement roughness is a critical airport pavement characteristic that has been linked to impacts such as safety and service life. A properly defined roughness evaluation method would reduce airport operational risk, prolong the life of aircraft landing gear, and optimize the decision-making process for pavement preservation, which together positively contribute to overall airport sustainability. In this study, we optimized the parameters of the International Roughness Index (IRI) model to resolve the current poor correlation between the IRI and aircraft vibration responses in order to adapt and extend the IRI’s use for airport runway roughness evaluation. We developed and validated a virtual prototype model based on ADAMS/Aircraft software for the Boeing 737–800 and then employed the model to predict the aircraft’s dynamic responses to runway pavement roughness. By developing a frequency response function for the standard 1/4 vehicle model, we obtained frequency response distribution curves for the IRI. Based on runway roughness data, we used fast Fourier transform to implement the frequency response distribution of the aircraft. We then utilized Particle Swarm Optimization to determine more appropriate IRI model parameters rather than modifying the model itself. Our case study results indicate that the correlation coefficient for the optimized IRI model and aircraft vibration response shows a qualitative leap from that of the original IRI model.


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