Evaluation of Probability Distribution Function for the Life of Pavement Structures

Romanoschi, Stefan A; Metcalf, J B

doi:10.3141/1730-11

Cited by 9 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Determination of the RSL helps in the decision-making of maintenance strategies and optimal usage of budgets (Vepa, George, & Shekharan, 1996). Precise RSL models facilitate better budget allocation for pavement maintenance programs (Romanoschi & Metcalf, 2000). Determination of the RSL pavement requires the actual characteristics, a description of unacceptable condition and a mechanism to anticipate deterioration.…”

Section: An Overview Of the Rsl Models Of Pavementmentioning

confidence: 99%

Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road)

Karballaeezadeh

Shamshirband

et al. 2019

Engineering Applications of Computational Fluid Mechanics

View full text Add to dashboard Cite

Accurate prediction of the remaining service life (RSL) of pavement is essential for the design and construction of roads, mobility planning, transportation modeling as well as road management systems. However, the expensive measurement equipment and interference with the traffic flow during the tests are reported as the challenges of the assessment of RSL of pavement. This paper presents a novel prediction model for RSL of road pavement using support vector regression (SVR) optimized by particle filter to overcome the challenges. In the proposed model, temperature of the asphalt surface and the pavement thickness (including asphalt, base and sub-base layers) are considered as inputs. For validation of the model, results of heavy falling weight deflectometer (HWD) and groundpenetrating radar (GPR) tests in a 42-km section of the Semnan-Firuzkuh road including 147 data points were used. The results are compared with support vector machine (SVM), artificial neural network (ANN) and multi-layered perceptron (MLP) models. The results show the superiority of the proposed model with a correlation coefficient index equal to 95%.

show abstract

Section: An Overview Of the Rsl Models Of Pavementmentioning

confidence: 99%

Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road)

Karballaeezadeh

Shamshirband

et al. 2019

Engineering Applications of Computational Fluid Mechanics

View full text Add to dashboard Cite

show abstract

“…Rough surfaces are caused by the fatigue imparted by the dynamic vehicle loading (Cebon 1989), environmental wear and tear and various other reasons (Romanoschi and Metcalf 2000). Self affine fractal has the potential to mimic such roughness (Power and Tullis 1991;Matsushita and Ouchi 1989) typically measured by the Acoustic sensors (Pieter et al 1996), laser or mechanical Profilometers (Sommargren 1981) and Interferometers (Bennett 1985).…”

Section: Introduction and Literaturementioning

confidence: 98%

Wavelet based identification of dominant scales for self-affine road roughness in context of riding comfort in vehicles

Mishra

Mishra²

2008

Geotech Geol Eng

View full text Add to dashboard Cite

We present a simulation based study of multiscale roughness of road surfaces and its effect on riding comfort in vehicles, given the fact that characterization of the measured roughness is important to ensure smooth ride. Self-affine fractals are used to simulate typically measured roughness data. Multiscale characteristics of such surfaces are obtained through varied level of spatial resolutions. The hierarchical nature of the multiscale fractal roughness and their role in affecting the riding comfort is investigated herein. Wavelet transform technique is exploited for multiscale decomposition. Roughness is synthesized from cumulation of scales in a multiscale fashion. Single degree of freedom car model is used for characterizing riding comfort parameters in terms of dynamic response of vehicle suspension system subjected to jerks exerted by rough profiles. The dominant scales of roughness governing the comfort parameters are identified through parametric study. It is shown that not all the scales are equally important in deciding the comfort parameters; rather, these parameters remain nearly intact with the inclusion of only a few initial scales. This facilitates multiscale visualization of roughness data and allows representing the profiles in a precise form by excluding the spurious higher scales. Apparently, it gives an economic estimate of the required resolution in the physical measurement for specific purpose.

show abstract

“…Knowledge of RSL facilitates decision making in regard to strategies for reconstruction-rehabilitation of roads, thereby leading to efficient use of existing resources (Vepa et al, 1996). Accurate RSL models improve the process of allocating funds and resources for maintenance and rehabilitation of asphalt pavements (Romanoschi and Metcalf, 2000).…”

Section: Remaining Service Life (Rsl)mentioning

confidence: 99%

Estimation of Remaining Service Life of Flexible Pavements from Surface Deflections

et al. 2010

View full text Add to dashboard Cite

Remaining service life (RSL) has been defined as the anticipated number of years that a pavement will be functionally and structurally acceptable with only routine maintenance. The Kansas Department of Transportation (KDOT) has a comprehensive pavement management system, network optimization system (NOS), which uses the RSL concept. In support of NOS, annual condition surveys are conducted on the state highway system. Currently KDOT uses an empirical equation to compute RSL of flexible pavements based on surface condition and deflection from the last sensor of a falling-weight deflectometer (FWD). Due to limited resources and large size, annual network-level structural data collection at the same rate as the project level is impractical. A rolling-wheel deflectometer (RWD), which measures surface deflections at highway speed, is an alternate and fast method of pavement-deflection testing for network-level data collection. Thus, a model that can calculate RSL in terms of FWD first sensor/center deflection (the only deflection measured by RWD) is desired for NOS.In this study, RWD deflection data was collected under an 18-kip axle load at highway speed on non-Interstate highways in northeast Kansas in July 2006. FWD deflection data, collected with a Dynatest 8000 FWD on the KDOT network from 1998 to 2006, were reduced to mile-long data to match the condition survey data collected annually for NOS. Normalized and temperature-corrected FWD and RWD center deflections and corresponding effective structural numbers (SNeff) were compared. A nonlinear regression procedure in Statistical Analysis Software (SAS) and Solver in Microsoft Excel were used to develop the models in this study.Results showed that FWD and RWD center deflections and corresponding SNeff are statistically similar. Temperature-correction factors have significant influence on these variables.FWD data analysis on the study sections showed that average structural condition of pavements of the KDOT non-Interstate network did not change significantly over the last four years. Thus, network-level deflection data can be collected at four-year intervals when there is no major structural improvement.Results also showed that sigmoimal relationship exists between RSL and center deflection. Sigmoidal RSL models have very good fits and can be used to predict RSL based on center deflection from FWD or RWD. Sigmoidal equivalent fatigue crack-models have also shown good fits, but with some scatter that can be attributed to the nature and quality of the data used to develop these models. FWD data analysis on the study sections showed that average structural condition of pavements of the KDOT non-Interstate network did not change significantly over the last four years. Thus, network-level deflection data can be collected at four-year intervals when there is no major structural improvement.Results also showed that sigmoimal relationship exists between RSL and center deflection. Sigmoidal RSL models have very good fits and can be used to predict RSL based on cen...

show abstract

Evaluation of Probability Distribution Function for the Life of Pavement Structures

Cited by 9 publications

References 8 publications

Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road)

Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road)

Wavelet based identification of dominant scales for self-affine road roughness in context of riding comfort in vehicles

Estimation of Remaining Service Life of Flexible Pavements from Surface Deflections

Contact Info

Product

Resources

About