Javed Farhan

Pavement Maintenance Prioritization Using Analytic Hierarchy Process

The prioritization of maintenance activities is commonly applied in pavement maintenance planning. A widely adopted practice is to express maintenance priority in the form of a priority index, computed by means of an empirical mathematical expression. Though convenient to use, empirical mathematical indices often do not have a clear physical meaning and cannot accurately and effectively convey the priority assessment or intention of highway agencies and engineers. In an attempt to overcome this limitation, this study explores the use of an analytic hierarchy process (AHP) for the prioritization of pavement maintenance activities. The main aim is to identify an approach that can reflect the engineering judgment of highway agencies and engineers more closely. Three forms of AHP are examined, namely, the distributive-mode relative AHP, the ideal-mode relative AHP, and the absolute AHP. The applications of the three methods are illustrated by using an example problem, and the results are compared with the priority assessments obtained by a direct assessment method in which the raters make the evaluation by directly comparing all maintenance activities together. The study concludes that the absolute AHP is suitable for the pavement maintenance prioritization process, on the basis of its ability to provide priority assessments for pavement maintenance activities in good agreement with the priority assessments obtained by the direct assessment method and its operational advantage in evaluating a large number of maintenance activities.

Use of Analytic Hierarchy Process to Prioritize Network-Level Maintenance of Pavement Segments with Multiple Distresses

In network-level pavement management, maintenance prioritization of pavement segments usually involves determining the relative priorities of pavement segments with several distresses of various types and severity levels. A widely adopted practice is to assign maintenance priority to each pavement segment with a priority index computed with an empirical mathematical equation. Though convenient to use, the computed numerical index does not have a clear physical meaning, and it may not accurately and effectively convey the priority assessment or intention of highway agencies and engineers. The analytic hierarchy process (AHP) has been adopted to overcome this limitation. The AHP was previously applied to establish the relative pavement maintenance priorities of single pavement distress types with different severity levels. The problem of establishing maintenance priorities of pavement segments with multiple distresses is much more complex. Many more decision levels and pairwise comparisons must be made because of many possible combinations of distress types and severities. The AHP formulation of the problem is presented, along with a demonstration of an example problem consisting of 30 multidistress pavement segments. The solution of priority ratings by the AHP is compared with the corresponding solution by the widely adopted PAVER pavement maintenance procedure. The reasonableness of the two solutions is compared with the priority ratings obtained from a direct assessment method that provides the baseline reference for comparison.

Airport Pavement Missing Data Management and Imputation with Stochastic Multiple Imputation Model

In practice, missing data in pavement condition databases have been one of the most prevalent problems in airport pavement management systems. Missing data present problems in pavement performance analysis and uncertainties in pavement management decision making. A number of data imputation approaches are available for handling missing data. This paper examines the limitations of the conventional data imputation methods and proposes a stochastic multiple imputation (MI) approach to overcome major limitations associated with conventional data imputation methods. A case study is presented to appraise the effectiveness of the proposed approach against three conventional data imputation methods, namely, substitution by mean, substitution by interpolation, and substitution by regression methods. The roughness and friction data of a 4-km-long runway pavement and the roughness data of a 4-km-long taxiway pavement were considered in the study. The effectiveness of auxiliary variables in data imputation models was also demonstrated. Results from the performance appraisal indicated that the proposed stochastic MI method yielded the smallest errors for the roughness as well as friction data. Furthermore, the substitution by mean method resulted in imputed values with the highest amount of deviations from the observed values, followed by the substitution by regression method, and the substitution by interpolation method. Therefore, it is concluded that the proposed stochastic MI method outperformed conventional methods in handling missing runway and taxiway pavement roughness and friction data and provides an effective approach to impute missing data required in an airport pavement management system.

Stochastic Imputation of Missing Physical Commodity Trade Information Using Monetary Trade Data

International trade had conventionally been expressed in monetary values until the recommendations by the UN Statistics Division. Because international trade in monetary terms alone is not entirely representative of global trade from a transportation and logistics perspective, the physical dimension of international trade is attaining increased importance. However, missing physical quantities in the trade flow databases are observed; these quantities are due largely to (a) noncompliance of reporter countries with the standard units of measurement or classification, (b) confidentiality issues, and (c) erroneous collection and reporting of certain data. This paper, therefore, first presents the existing methods used in the literature to treat the issue of missing commodity weight information in international physical commodity trade databases and then proposes a stochastic multivariate imputation model using auxiliary variables such as monetary trade data and the price index to impute missing physical quantities. The relative performance of those methods in resolving the issue of incomplete physical commodity trade data is then evaluated and compared through a case study. It is concluded that the proposed approach outperforms the existing approaches for commodity flow data imputation.

Augmented Stochastic Multiple Imputation Model for Airport Pavement Missing Data Imputation

This paper presents a research study to handle the problem of missing data in airport pavement management systems. This study was a continuation of an earlier study addressing the same concern. In the earlier study, a stochastic multiple imputation (SMI) approach was adopted to overcome major limitations associated with conventional data imputation methods. The SMI approach considered the variation of multiple plausible imputations and obtained an unbiased estimate in replacing a missing data value. This approach was found to outperform the three most commonly used imputation methods for missing data handling in pavement management: the linear interpolation method, the substitution by mean method, and the regression method. However, the SMI approach estimated missing data values by using purely statistical techniques, without making use of any unique characteristics of pavement performance data. The present study explored the possibility of further improving the data imputation process by exploiting parallel pavement performance-related data available from pavement condition and performance surveys of airfield pavements. An augmented stochastic multiple imputation (ASMI) approach was proposed to incorporate auxiliary parameters to aid in reducing uncertainty and improving prediction performance of runway pavement condition data. Pavement friction data were used as illustration; the related parameter data included aircraft landing volume, rainfall, and temperature. This study showed that the proposed ASMI approach provided an analytically meaningful method from a pavement engineering point of view that can further improve the quality and reliability of imputing missing data for airport pavement management systems.

Evaluation of Effects of Priority Preferences on Optimal Resource Allocation in Pavement Management

Certain aspects of prioritization have commonly been employed in the optimum resource allocation program for pavement maintenance management. However, issues associated with incorporating priority preferences into pavement maintenance programming have not been evaluated. For example, application of priority weights to certain problem parameters will affect the optimality of the end solution with respect to the original objective. However, the degree of loss in optimality is related to the form or structure, the magnitudes, and the range of priority weights adopted. Decision makers, who adopt such an approach, are often oblivious to the degree of sub-optimality of the solutions. Therefore, this paper presents a study that examines the implications of applying priority weights, of varying magnitudes and ranges, in the pavement maintenance programming analysis in terms of optimality of the solution/strategy.

Effect of Proportion of Missing Data on Application of Data Imputation in Pavement Management Systems

Instances of missing data are common in pavement condition–performance databases. A common practice today is to apply statistical imputation methods to replace the missing data with imputed values. Pavement management decision makers must know the uncertainty and errors involved in the use of data sets with imputed values in their analysis. Equally important information of practical significance is the maximum allowable proportion of missing data (i.e., the level of missing data) that can still produce results with an acceptable magnitude of error or risk when the imputed data are used. This paper proposes a procedure for determining such useful information. A numerical example analyzing pavement roughness data is presented to demonstrate the procedure through evaluating the error and reliability characteristics of imputed data. The roughness data of three road sections were obtained from the Long-Term Pavement Performance database. From these data records, data sets with different proportions of missing data were randomly generated to study the effect of level of missing data. The analysis shows that the errors of imputed data tend to increase with the level of missing data and that their magnitudes are significantly influenced by the effect of pavement rehabilitation. On the application of data imputation in pavement management systems, the study suggests that, at a 95% confidence level, 25% of missing data appears to be a reasonable allowable maximum limit for analyzing time series data on pavement roughness that include no rehabilitation within the analysis period. When pavement rehabilitation occurs within the analysis period, the maximum proportion of imputed data should be limited to 15%.