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Author: Ghassan Abu-Lebdeh
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 0
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Author: Ghassan Abu-Lebdeh
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 0
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Author: Ghassan Abu-Lebdeh
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages :
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Author: Chenglong Luo
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 79
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Pavement deterioration is one of the most important driver for prioritizing pavement management and preservation (PMP) projects. The primary goal of this thesis is to provide reasonable predictive functions from multiple linear regression (MLR) models and artificial neural networks (ANN) that can be adopted by Kentucky Transportation Cabinet (KYTC). Furthermore, we use analytic hierarchy process (AHP) to design a composite pavement distress index in order to help Kentucky Transportation Cabinet (KYTC) prioritizing PMP projects based on 11 different distress indices. Numerical results show that the MLR models provide relatively high R square values of approximately 0.8. Both MLR and ANN models have small average squared errors (ASE). Finally, for all nine distress indices studied in this thesis, MRL models are recommended to KYTC due to their simplicity, interpretability along with robust performance that is comparable to the ANN model. Finally, a priority rating method is developed using analytical hierarchy process and it integrates 11 pavement distress indices into one priority score. A case study shows that the propose AHP-based rating method overcomes the drawback of KYTC's current rating system for overemphasizing the international roughness index (IRI) among all distress indices.
Author: John P. Zaniewski
Publisher:
ISBN:
Category :
Languages : en
Pages : 428
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Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 68
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Author: Randall E. Wade
Publisher:
ISBN:
Category : Highway planning
Languages : en
Pages : 56
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Author: Gulfam Jannat
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 183
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Pavement Maintenance and Rehabilitation (M&R) are the most critical and expensive components of infrastructure asset management. Increasing traffic load, climate change and resource limitations for road maintenance accelerate pavement deterioration and eventually increase the need for future maintenance treatments. Consequently, pavement management programs are increasingly complex. The complexities are attributed to the precise assessment process of the overall pavement condition, realistic distress prediction and identification of cost-effective M&R schedules. Cost-effective road M&R practices are only possible when the evaluation of pavement condition is precise, pavement deterioration models are accurate, and resources must also be available at the right time. In a Pavement Management System (PMS), feasible M&R treatments are identified at the end of each branch of the decision trees. The decision trees are based on empirical relationships of the pavement performance index. Moreover, the predicted improvements in pavement performance for any treatment are set based on engineering experiences. Furthermore, the remaining service life of the pavement is estimated from the predicted deterioration of the overall condition. The future deterioration of the overall condition is estimated based on the initial condition and by considering only the effect of age notwithstanding the effect of traffic or materials. In assessing the overall condition of the pavement, this research overcomes the limitations of engineering judgment by incorporating a Mechanistic-Empirical (M-E) approach and estimating the improvement in performance for specific treatment types. It also considers the effect of traffic and materials on pavement performance to precisely predict its future deterioration and subsequent remaining service life. The objective of this research is to develop cost-effective pavement M&R schedules by incorporating (a) the M-E approach into the overall condition index and (b) the estimate of performance indices by considering the factors affecting pavement performance. The research objective will be accomplished by (i) incorporating variability analysis of existing performance evaluation practices and maintenance decisions of pavement, (ii) investigating estimates of existing performance indices, (iii) incorporating the M-E approach: sensitivity analysis, prediction, comparison and verification, (iv) estimating the deterioration model based on traffic characteristics and material types, and (v) identifying cost-effective M&R treatment options through Life Cycle Cost Analysis (LCCA). This study uses the pavement performance data of Ontario highways recorded in the Ministry of Transportation (MTO) pavement database. Precise assessment of pavement condition is a significant part in achieving the research goal. In a PMS, an accurate location reference system is necessary for managing pavement evaluations and maintenance. The length of the pavement section selected for evaluation may have a significant impact on the assessment irrespective of the type of performance indices. In Ontario, the highway section lengths range from 50m to 50,000m. For this reason, a variability in performance evaluation is investigated due to changes in section length. This study considers rut depth, Pavement Condition Index (PCI), and International Roughness Index (IRI) as performance indices. The distributions of these indices are compared by the following groupings of section lengths: 50m, 500m, 1,000m and 10,000m. The variations of performance assessments due to changing section lengths are investigated based on their impact on maintenance decisions. A Monte Carlo simulation is carried out by varying section lengths to estimate probabilities of maintenance work requirements. Results of such empirical investigations reveal that most of the longer sections are evaluated with low rut depth and the shorter sections are evaluated with high rut depth. This Monte Carlo simulation also reveals that 50m sections have a higher probability of maintenance requirements than 500m sections. The method of estimating performance indices is also investigated to identify the requirement of improvement in estimation of the prediction models. Generally, in a PMS, the prediction models of Key Performance Indicators (KPIs) are estimated by using the Ordinary Least Square (OLS) approach. However, the OLS approach can be inefficient if unobserved factors influencing individual KPIs are correlated with each other. For this reason, regression models for KPI predictions are estimated by using an approach called the 'Seemingly Unrelated Regression (SUR)' method. The M-E approach is used in this study to predict the future distresses by employing mechanistic-empirical models to analyze the impact of traffic, climate, materials and pavement structure. The Mechanistic-Empirical Pavement Design Guide (MEPDG) software uses a three-level hierarchical input to predict performance in terms of IRI, permanent deformation (rut depth), total cracking (reflective and alligator), asphalt concrete (AC) thermal fracture, AC bottom-up fatigue cracking and AC top-down fatigue cracking. However, these inputs have different levels of accuracy, which may have a significant impact on performance prediction. It would be ineffective to put effort for obtaining accuracy at Level 1 for all inputs. For this reason, a sensitivity analysis is carried out based on an experimental design to identify the effect of the accuracy level of inputs on the distresses. Following this, a local sensitivity analysis is carried out to identify the main effect of input variables. Interaction effects are also analyzed based on a random combination of the inputs. Since the deterioration of pavement is affected by site-specific traffic, local climate and properties of materials, these variables are carefully considered during the development of the pavement deterioration model to assess overall pavement conditions. The prediction model is developed by using a regression approach considering distresses of the M-E approach. In this study, the deterioration model is estimated for three groups of Annual Average Daily Traffic (AADT) to recognize their individual impact along with properties of materials. The time required for maintenance is also estimated for these categories. The investigations reveal that the expected time to maintenance for overlay with Dense Friction Course (DFC) and Superpave mixes is higher than other Hot Laid (HL) asphalt layers. This will help pavement designers and managers to make informed decisions. The probability of failure is also investigated by a probabilistic approach. With the increasing trend towards M&R of existing pavements, it is essential to make cost-effective use of the M&R budget. As such, identification of associated cost-effective M&R treatments is not always simple in most PMS. For this reason, a LCCA is carried out for alternate pavement treatments using the deterioration model based on traffic levels and material types. Comparing the Net Present Worth (NPW) value of alternative treatment options reveals that the overlay of pavement with DFC is the most cost-effective choice in the case of higher AADT. On the other hand, overlay with Hot Laid-1 (HL-1) is a cost-effective treatment option for highway sections with lower AADT. Although the results are related to the Ontario highway system, this can also be applied elsewhere with similar conditions. The outcome of the empirical investigations will result in the adoption of efficient road M&R programs for highways based on realistic performance predictions, which have significant impact on infrastructure asset management.
Author:
Publisher: Transportation Research Board
ISBN: 9780309055024
Category : Pavements
Languages : en
Pages : 180
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Author: Amr Ayed
Publisher:
ISBN:
Category :
Languages : en
Pages : 192
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Performance prediction models are a vital component in pavement management systems (PMS). Along with decision trees, prediction models are used to set priorities for maintenance and rehabilitation planning, and ultimately for budget allocations at the network level. Reliable and accurate prediction of pavement deterioration over time helps transportation agencies accurately predict future spending and save significant amounts of money. Within a PMS, raw performance data is often converted into aggregated performance indices, such as the Riding Comfort Index (RCI), to quantify the road's roughness, or the Distress Surface Index (SDI), to quantify accumulated pavement distress. Technology has evolved rapidly in the last two decades, making data collection for pavement conditions (i.e. roughness and distress data) more feasible for transportation agencies. However, transportation agencies, especially at the municipal level, only maintain condition data to evaluate the present pavement status. Only limited attempts have so far been made to develop or enhance existing deterioration models in pavement management systems, using periodically collected condition data over time. A well-maintained historical database of pavement condition measurements and performance indices can be a useful source for the development of performance prediction models. In some cases, however, the database may contain incomplete data and insufficient information to develop reliable performance models. In addition to inconsistency in the historical performance data, the age of the pavement or the date of the last maintenance/ rehabilitation treatment may not be available to develop the pavement performance over time. The goal of this research is to develop enhanced empirical performance models capable of capturing the unpredictable and indeterminate nature of pavement deterioration behavior. This research provides a methodology to develop empirical models in the absence of the construction and/or rehabilitation dates. The models developed in this research use limited available historical data, and examine different parameters, such as pavement thickness, traffic pattern, and subgrade condition. Parameters such as the date of pavement construction and the age of the pavement are also incorporated into the proposed models, and are constrained by local experience and engineering judgment. A linear programming optimization technique is employed to develop the empirical models presented in this research. The approach demonstrated in this research can also be expanded to account for additional parameters, and can easily be adapted to match the needs of different agencies based on their local experience. In addition, the current research develops a second set of deterioration models based on mechanistic-empirical principles. Models incorporated into the mechanistic-empirical design guide are locally calibrated. A genetic algorithm optimization technique is employed to guide the calibration process, in order to determine the coefficients that best represent pavement performance over time. The two sets of performance models developed in this research are compared at both the project and network level of analysis. A decision-making framework is implemented to incorporate the two sets of models, and a comprehensive life cycle cost analysis is carried out to compare design alternatives in the project level analysis. The two model sets are also evaluated at the network level analysis using a municipal pavement management system. Two budget scenarios are executed, based on the developed performance models, and a comparison between network performance and budget spending is presented. Finally, a summary and current research contribution to the pavement industry will be presented, along with recommendations for future research.
Author: W. Ronald Hudson
Publisher:
ISBN:
Category :
Languages : en
Pages : 20
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