Pavement Performance Evaluations Using Connected 1 Vehicles 2

IRJET, 2020

An India has a large network of roads of length over 5 million kilometers, consisting of different categories of roads. The maintenance and management of such large road network with limited resources is a demanding task in emerging economics like India. The road users expect the performance of these road infrastructures to be above the desired level during the service life. The road authorities expect this performance to be achieved within the limited resources. This contradicting inevitability makes the problem quite interesting and challenging. In the present study, the selected road networks were investigated for International Roughness Index (IRI) and Roughness Index (RI) using machine of evaluating roughness using low-cost instrumentation (MERLIN). This method will measure the pavement condition Index (PCI) and used to evaluate the pavement international roughness index (IRI) and to decide the order of maintenance priority for the selected road networks based on the condition rating. For stretch-1 from Gudlavalleru to Chitram, IRI is 7.2 m/km and condition rating is 40% so we suggested to design the overlay and for stretch-2, from Gudlavalleru to Penjandra IRI is 6.9 m/km and condition rating is 90% so we suggested for good maintenance.

Transportation Research Record: Journal of the Transportation Research Board, 2016

The United States relies on the performance of more than 4 million miles of roadways to sustain its economic growth and to support the dynamic mobility needs of its growing population. The funding gap to build and maintain roadways is ever widening. Hence the continuous deterioration of roads from weathering and usage poses significant challenges. Transportation agencies measure ride quality as the primary indicator of roadway performance. The international roughness index is the prevalent measure of ride quality that agencies use to assess and forecast maintenance needs. Most jurisdictions use a laser-based inertial profiler to produce the index. However, technical, practical, and budget constraints preclude that use for some facility types, particularly local and unpaved roads that make up more than 90% of the road network in the United States. This study expands on previous work that developed a method to transform sensor data from many connected vehicles to characterize ride qua...

Transportation Research Record: Journal of the Transportation Research Board, 2003

Road surface roughness excites low- and high-frequency vibration modes of a heavy articulated vehicle body. These vibrations result in motions in all directions that detract from the driver’s perceived ride and comfort and increase pavement damage due to dynamic wheel loads (DWLs). A subjective assessment survey was conducted to identify surface roughness characteristics that mainly influence the perceptions of heavy-vehicle drivers of pavement rideability and their comfort. The latter was achieved by correlating drivers’ ratings to roughness contents in different roughness wavebands. The results indicated that the drivers mainly object to low-frequency body vibrations excited by roughness wavelengths in the range of 4.88 to 19.5 m. Roughness content in this band was used to establish a new profile-based index called the profile index for truck ( PIt). Drivers consider pavement rideability to be poor when PIt exceeds 2.75 m/km. PIt provides better predictions of heavy vehicle ride t...

The pavement roughness is the main variable that produces the vertical excitation in vehicles. Pavement profiles are the main determinant of (i) discomfort perception on users and (ii) dynamic loads generated at the tire-pavement interface; hence its evaluation constitutes an essential step on a Pavement Management System. The present document evaluates two specific techniques used to simulate pavement profiles; these are the shaping filter and the sinusoidal approach, both based on the Power Spectral Density. Pavement roughness was evaluated using the International Roughness Index (IRI), which represents the most used index to characterize longitudinal road profiles. Appropriate parameters were defined in the simulation process to obtain pavement profiles with specific ranges of IRI values using both simulation techniques. The results suggest that using a sinusoidal approach one can generate random profiles with IRI values that are representative of different road types; therefore, one could generate a profile for a paved or an unpaved road, representing all the proposed categories defined by ISO 8608 standard. On the other hand, to obtain similar results using the shaping filter approximation, a modification in the simulation parameters is necessary. The new proposed values allow one to generate pavement profiles with high levels of roughness, covering a wider range of surface types. Finally, the results of the current investigation could be used to further improve our understanding on the effect of pavement roughness on tire pavement interaction. The evaluated methodologies could be used to generate random profiles with specific levels of roughness to assess its effect on dynamic loads generated at the tire-pavement interface and user's perception of road condition. RESUMEN La rugosidad del pavimento es la principal variable que produce la excitación vertical en los vehículos. Los perfiles de pavimento son los principales responsables de (i) la incomodidad percibida por los usuarios y, (ii) las cargas dinámicas generadas en la interfaz entre el neumático y el pavimento, de ahí que su evaluación constituye un paso esencial en un Sistema de Gestión de Pavimentos. El presente documento evalúa dos técnicas específicas utilizadas para simular perfiles de pavimento; estos son los filtros de forma y la aproximación sinusoidal, ambos basados en la Densidad de Potencia Espectral. La rugosidad del pavimento se evaluó utilizando el Índice Rugosidad Internacional (IRI), el cual es el índice más utilizado para caracterizar rugosidad del perfil longitudinal de una vía. En la presente investigación se definieron parámetros apropiados en el proceso de simulación para obtener perfiles de pavimento con rangos específicos de los valores de IRI utilizando ambas técnicas de simulación. Los resultados sugieren que el uso de la aproximación sinusoidal puede generar perfiles aleatorios con valores de IRI que son representativos de diferentes tipos de vías, por lo tanto, se podría generar un perfil para un pavimento nuevo o una vía sin pavimentar, cubriendo todas las categorías definidas por la norma ISO 8608. Por otro lado, para obtener resultados similares usando la metodología de filtro de conformación, es necesaria una modificación en los parámetros de simulación. Los nuevos valores propuestos permiten generar perfiles de pavimento con altos niveles de rugosidad, que cubren una gama más amplia de tipos de superficie. Por último, los resultados de la presente investigación se podrían utilizar para enriquecer lo que se conoce sobre el efecto de la rugosidad del pavimento en la interacción del pavimento de neumáticos. Las metodologías analizadas se podrían utilizar para generar perfiles aleatorios con niveles específicos de rugosidad (IRI) para evaluar su efecto sobre las cargas dinámicas generadas en la interfaz neumático-pavimento y la percepción del usuario del estado de la carretera. Palabras Clave: Proceso aleatorio, densidad de potencia espectral, aproximación sinusoidal, filtro de forma, perfil del pavimento, rugosidad. Análisis de las metodologías utilizadas para generar perfiles aleatorios de pavimento con base en la densidad espectral de potencia (DEP): un enfoque ajuste basado en el Índice de Rugosidad Internacional Evaluation of thE mEthodologiEs usEd to gEnEratE random pavEmEnt profilEs basEd on thE powEr spEctral dEnsity... IngenIería e InvestIgacIón vol. 37 n.° 1, aprIl-2017 (49-57) 50

KSCE Journal of Civil Engineering, 2016

This study quantified roughness for individual wavebands in the long wavelength range in pavement longitudinal profiles. Particularly, the roughness was related with quantified plausible factors associated with those wavebands. Root Mean Square (RMS) roughness defined under the Power Spectral Density (PSD) profile in the frequency domain was used as a quantified index that can reflect detailed roughness information for a specific waveband. A method to interpret the quantified roughness in terms of the International Roughness Index (IRI) was also developed. It was demonstrated that the RMS roughness evaluated in individual wavebands under the PSD function can be technically interpreted in terms of IRI. Thus, the interpreted IRI could be directly explained by the quantified plausible factors. It was concluded that the demonstrated method can contribute to improved roughness prediction models the Mechanistic-Empirical Pavement Design Guide (MEPDG) with smoothness characteristics in detailed wavelengths underpinnings in future generations of pavement design models.