Investigation of Relationship Between Deterministic and Probabilistic Prediction Models in Pavement Management

Li, Ningyuan; Haas, Ralph; Xie, Wei‐Chau

doi:10.3141/1592-09

Cited by 40 publications

(20 citation statements)

References 1 publication

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“…Pavement performance prediction models are typically classified as either deterministic or probabilistic models (Wang et al 1994, Li et al 1997, Shohel Reza Amin 2015. The deterministic models generally attempt to predict future pavement conditions with certainty, whereas the probabilistic models recognise the probabilistic performance of pavements, thus assigning different levels of uncertainty to various predicted pavement conditions.…”

Section: Introductionmentioning

confidence: 99%

Empirical approach for estimating the pavement transition probabilities used in non-homogenous Markov chains

Abaza

2015

International Journal of Pavement Engineering

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An empirical approach is proposed to estimate the transition probabilities associated with non-homogenous Markov chains typically used in developing stochastic-based pavement performance prediction models. A reliable pavement performance prediction model is a key component of any advanced pavement management system. The proposed empirical approach is designed to account for two major factors that cause the transition probabilities (i.e. deterioration rates) to increase over time. The first major factor is the progressive increase in traffic loading as represented by the equivalent single axle load applications. The second major factor is the gradual decline in the pavement structural capacity which can be represented by an appropriate pavement strength indicator such as the structural number. The proposed empirical model can recursively estimate the non-homogenous transition probabilities for an analysis period of (n) transitions by simply multiplying the firstyear (i.e. present) transition probabilities by two adjustment factors, namely the load and strength factors. Once the empirical model is calibrated, these two factors can capture the impact of traffic load increases and gradual pavement structural losses on the transition probabilities over time. The calibration process requires the estimation of the model two exponents to be obtained from the minimisation of sum of squared errors wherein the error is defined as the difference between the observed and predicted pavement distress ratings (DRs). The predicted DRs are mainly estimated based on the state probabilities, which are recursively derived from the non-homogenous Markov model. A sample empirical model is presented with results indicating its effectiveness in estimating the pavement non-homogenous transition probabilities.

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Section: Introductionmentioning

confidence: 99%

Empirical approach for estimating the pavement transition probabilities used in non-homogenous Markov chains

Abaza

2015

International Journal of Pavement Engineering

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“…Due to its importance, many sensitivity analysis studies have been conducted for asphalt mixtures and design (Chou, 1990;Haider, Salama, Buch, & Chatti, 2007;Killingsworth & Zollinger, 1995;Li, Haas, & Xie, 1997;Sues, Lua, Dass, & Murfee, 1993;Thyagarajan, 2009). In the past, sensitivity analysis was conducted by altering one parameter, while fixing the others, thus focusing on one variable at a time, which may result in not capturing the effect of the interaction between variables collectively (Thyagarajan, 2009).…”

Section: Sensitivity Analysismentioning

confidence: 98%

Sensitivity analysis of a new dynamic modulus (|E*|) model for asphalt mixtures

Abdo

2012

Road Materials and Pavement Design

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Asphalt pavement designers have been using the dynamic modulus (|E * |) as a fundamental parameter to reflect the hot-mix asphalt (HMA) performance, and since |E * | is considered as an essential input in the new AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG), many models were developed for evaluating and/or predicting this property for HMA. This article focuses on the verification of a newly developed model to predict |E * | by utilising robust sensitivity analyses through tornado plots and extreme tail analyses, which are used to identify the relative importance of the input variables on the predicted |E * |. Results showed that the binder dynamic shear modulus (|G * |) has the most significant effect, followed by the air void content, on the predicted |E * |, and both properties contributed largely to the extreme predicted values.

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“…Pavement performance prediction has been performed using both deterministic and probabilistic approaches (Abaza, 2004;Amin, 2015;Li, Haas, & Xie, 1997). The outcome in both cases is the development of simple mathematical models that enable pavement engineers to estimate the future pavement conditions in terms of reliable KPIs such as the PSI and PCI.…”

Section: Estimation Of Asphaltic Remaining Strength Factormentioning

confidence: 99%

Simplified empirical approach for predicting the remaining strength factor used in pavement rehabilitation applications

Abaza

2019

Cogent Engineering

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This paper presents a simplified empirical model for predicting the asphaltic remaining strength factor to be used in estimating the resurfacing thickness for both thin and thick asphaltic surfaces. The proposed model for predicting the asphaltic remaining strength factor in the case of thin asphaltic surface is mainly a function of key performance indicators and calibration constant (K). In the case of thick asphaltic surface, an average remaining strength factor is proposed which is a function of the existing asphaltic surface thickness, cold milling thickness, and the remaining strength factor associated with thin asphaltic surface. The proposed remaining strength factor is to be used in estimating the resurfacing thickness component due to the strength loss endured by the asphaltic surface. Two case studies are presented to predict the remaining strength factor. The first one applies the remaining strength factor model to estimate the resurfacing thicknesses for two sample projects considering variable rehabilitation scheduling time, while the second one calibrates the remaining strength factor model for a local roadway sample using minimization of the sum of squared errors. The sample results Khaled A. Abaza ABOUT THE AUTHOR Prof. Abaza earned his PhD in Transportation Engineering Systems from the University of Toledo, Ohio, 1990. He is a licensed professional engineer (PE) in the State of California where he worked as a transportation engineer with the California Department of Transportation (Caltrans) from 1991 to 1997. He joined Birzeit University in 1997 and held different teaching and administrative positions including chairperson of the Civil Engineering Department and dean of the Faculty of Engineering and Technology. Prof. Abaza has mainly applied his expertise in stochastic modeling, optimization methods, statistical analysis and pavement engineering to provide reliable solutions to many of the contemporary problems facing pavement engineers. His recent research efforts have mainly focused on pavement performance prediction using discrete-time heterogeneous Markov chains. This has led to the development of Empirical-Markovian models which are used in developing optimal pavement rehabilitation strategies and predicting resurfacing design thicknesses.

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Investigation of Relationship Between Deterministic and Probabilistic Prediction Models in Pavement Management

Cited by 40 publications

References 1 publication

Empirical approach for estimating the pavement transition probabilities used in non-homogenous Markov chains

Empirical approach for estimating the pavement transition probabilities used in non-homogenous Markov chains

Sensitivity analysis of a new dynamic modulus (|E*|) model for asphalt mixtures

Simplified empirical approach for predicting the remaining strength factor used in pavement rehabilitation applications

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