Predicting Flexible Pavement Deterioration for Pavement Management Systems PDF Download
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Author: Yuhong Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 320
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Book Description
Author: Yuhong Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 320
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Book Description
Author:
Publisher:
ISBN: 9780309055024
Category :
Languages : en
Pages : 50
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Book Description
This session consists of the following papers: Pavement deterioration modeling in India (Sood, VK, Sharma, BM, Kanchan, PK and Sitaramanjaneyulu, K); Predicting roughtness progression in flexible pavements using artificial neural networks (Attoh-Okine, NO); Performance history and prediction modeling for Minnesota pavements (Lukanen, EO and Han, C); Performance models and prediction of increase in overlay need n the Danish state highway pavement management system, BELMAN (Jansen, JM and Schmidt, B); Mechanistic performance model for pavement management (Chua, KH, Monismith, class, and Crandall, KC).
Author: Adrain Reed Gibby
Publisher:
ISBN:
Category :
Languages : en
Pages : 380
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Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 13
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The Ontario Pavement Analysis of Costs system has been in service for Ontario asphalt flexible pavement design and performance prediction since the early 1970s. It uses a deflection-based deterministic model for selecting the best pavement structural design alternative in terms of pavement functional and structural performance and the total life-cycle costs. However, because of the existence of uncertainties and variations in pavement design variables and parameters in the pavement deterioration models, it is not adequate to apply deterministic models to all situations of pavement management. It is therefore necessary to predict pavement performance by employing probabilistic-based models. In this paper, a new concept of system conversion between a deterministic model and a probabilistic model is discussed first. A method by which a deterministic pavement performance prediction model, such as the Ontario asphalt pavement deterioration model, can be converted into a probabilistic model is presented. A transformed probabilistic model is constructed by generating a set of time-related nonhomogeneous Markovian transition probability matrices, which is determined by Monte Carlo simulation. Each of the transition probability matrices characterizes the pavement deterioration rate for the given pavement age and traffic characteristics. A Bayesian technique is then employed to update the predicted pavement performance in terms of the pavement condition state vectors and expected pavement condition state values by integrating additional information such as the actually measured performance data of the pavement.
Author: Gulfam Jannat
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 183
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Book Description
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: Ralph Haas
Publisher:
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 682
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Book Description
Focusing on the process of pavement management, this text covers topics such as data acquisition and evaluation, network level priority programming and project level design. Examples of working systems are provided, as well as guidance for implementation.
Author: Arash Mohammad Hosseini
Publisher:
ISBN:
Category :
Languages : en
Pages : 188
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Book Description
Current pavement performance prediction models are based on the parameters such as climate, traffic, environment, material properties, etc. while all these factors are playing important roles in the performance of pavements, the quality of construction and production are also as important as the other factors. The designed properties of Hot Mix Asphalt (HMA) pavements, known as flexible pavements, are subjected to change during production and construction stages. Therefore, most of the times the final product is not the exact reflection of the design. In almost any highway project, these changes are common and likely to occur from different sources, by various causes, and at any stage. These changes often have considerable impacts on the long-term performance of a project. The uncertainty of the traffic and environmental factors, as well as the variability of material properties and pavement structural systems, are obstacles for precise prediction of pavement performance. Therefore, it is essential to adopt a hybrid approach in pavement performance prediction and design; in which deterministic values work along with stochastic ones. Despite the advancement of technology, it is natural to observe variability during the production and construction stages of flexible pavements. Quality control programs are trying to minimize and control these variations and keep them at the desired levels. Utilizing the information gathered at the production and construction stages is beneficial for managers and researchers. This information enables performing analysis and investigations of pavements based on the as-produced and as-constructed values, rather than focusing on design values. This study describes a geo-relational framework to connect the pavement life-cycle information. This framework allows more intelligent and data-driven decisions for the pavements. The constructed geo-relational database can pave the way for artificial intelligence tools to help both researchers and practitioners having more accurate pavement design, quality control programs, and maintenance activities. This study utilizes data collected as part of quality control programs to develop more accurate deterioration and performance models. This data is not only providing the true perspective of actual measurements from different pavement properties but also answers how they are distributed over the length of the pavement. This study develops and utilizes different distribution functions of pavement properties and incorporate them into the general performance prediction models. These prediction models consist of different elements that are working together to produce an accurate and detailed prediction of performance. The model predicts occurrence and intensity of four common flexible pavement distresses; such as rutting, alligator, longitudinal and transverse cracking along with the total deterioration rate at different ages and locations of pavement based on material properties, traffic, and climate of a given highway. The uniqueness of the suggested models compared to the conventional pavement models in the literature is that; it carries out a multiscale and multiphysics approach which is believed to be essential for analyzing a complex system such as flexible pavements. This approach encompasses the discretization of the system into subsystems to employ the proper computational tools required to treat them. This approach is suitable for problems with a wide range of spatial and temporal scales as well as a wide variety of different coupled physical phenomena such as pavements. Moreover, the suggested framework in this study relies on using stochastic and machine learning techniques in the analysis along with the conventional deterministic methods. In addition, this study utilizes mechanical testing to provide better insights into the behavior of the pavement. A series of performance tests are conducted on field core samples with a variety of different material properties at different ages. These tests allow connecting the lab test results with the field performance survey and the material, environmental and loading properties. Moreover, the mix volumetrics extracted from the cores assisted verifying the distribution function models. Finally, the deterioration of flexible pavements as a result of four different distresses is individually investigated and based on the findings; different models are suggested. Dividing the roadway into small sections allowed predicting finer resolution of performance. These models are proposed to assist the highway agencies s in their pavement management process and quality control programs. The resulting models showed a strong ability to predict field performance at any age during the pavements service life. The results of this study highlighted the benefits of highway agencies in adopting a geo-relational framework for their pavement network. This study provides information and guidance to evolve towards data-driven pavement life cycle management consisted of quality pre-construction, quality during construction, and deterioration post-construction.
Author: H. Yokota
Publisher:
ISBN:
Category : Falling weight deflectometer (FWD)
Languages : en
Pages : 15
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Book Description
Prediction models for pavement deterioration of major roads in Miyazaki Pre-fecture, Japan, using falling weight deflectometer (FWD) data are presented. The models would be incorporated in a pavement management system (PMS), for the prefecture, which is under development. At first, a relation between Japanese maintenance control index (MCI) and cumulative equivalent single axle loads (CESAL) was established. MCI was computed using variables that were automatically measured by a vehicle mounted with laser beam, cameras and profilometers. AASHO performance equation provided the basis for the development of these new models. Strength (deflection) factor was introduced into the coefficient that controls the slope of the performance curve through multiple regression analysis. Given FWD deflection and CESAL, these models could predict the trend of future pavement deterioration. This method increases the range of applicability of FWD data and may complement pavement condition rating systems that provide a measure of current pavement conditions only.
Author: Wael Elias Tabara
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Keeping asphalt-surfaced highways and roads in an acceptable condition is the major goal that departments of transportation and pavement engineers always strive to achieve. According to ASCE 2009 report card, an estimated spending of $186 billion is needed annually to substantially improve highways conditions. Hence, prediction models of current and future pavement condition should be rationalized and studied from cost effective perspective. In modeling the pavement condition, two major categories of models have been used: (1) deterministic and (2) stochastic. Existing models consider some factors that might be more critical than others, such as roughness measurements and distress information. They ignore other factors that could have a real effect on the accuracy of the pavement performance model(s), such as climate conditions. Therefore, the current research aims at developing a comprehensive condition-rating model that incorporates a wider range of possible factors significantly affecting flexible pavement performance. Data for this research were collected from the records of Nebraska Department of Roads (NDOR) called "Tab Files". In addition to a questionnaire that was designed and sent to pavement engineers and experts in North America. An integrated model was developed using Multi-Attribute Utility Theory (MAUT) and multiple regression analysis. Sensitivity analysis of the developed regression models is done using Monte-Carlo simulation to quickly identify the high-impact factors. Models' validation shows robust results with an average validity percent of 94% in which they can be utilized by Departments of Transportation (DOT) and/or Pavement Management Systems (PMS) as a useful tool for assessing and predicting pavement conditions.
Author: Jianxiong Yu
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 200
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Book Description
Remaining service life estimation and pavement condition prediction are two essential functions of Pavement Management Systems. Survival curves are often developed to obtain remaining life of a pavement family at network level. Regression equations are often developed to predict future pavement condition at project level. The two objectives of this study are: (1) To develop the Cox Proportional Hazard model to analyze the effects of influential factors on pavement remaining life; (2) To develop linear mixed effects prediction model to improve the condition prediction accuracy for individual pavements. In this study, by specifying pavement condition rating (PCR) of 70 as the terminal pavement status, survival curves were developed based on historical PCR data using Cox Proportional Hazards method. Further, the estimated service lives of pavements were obtained from these survival curves. As an example, the survival data of asphalt overlays on flexible pavements in Ohio were analyzed for this study. The effects of influential factors such as structure thickness, climate, traffic loading, and pavement conditions prior to repair on pavement service life, were assessed. The results show that the Cox Proportional Hazards model is applicable in estimating the effects of influential factors on pavement service life. The service life obtained from this study can be used to assist in pavement rehabilitation decision-making, overlay design, and budget allocation. Condition prediction of individual pavement is usually required in project-level management and is often based on adjusting corresponding pavement family deterioration trend. This study proposes using the Linear Mixed Effects Model (LMEM) method to predict future conditions of a specific pavement section by a weighted combination of the deterioration trends of the family average and that of the specific pavement. The weights are determined by the number of historical condition measurements available and the variations of the measured historical conditions of the specific pavement. The results of the LMEM showed significantly better accuracy in predicting specific pavement conditions compared with two commonly used adjustment methods, which use the latest condition measurement to adjust family model for individual pavement. The findings in this study show that the LMEM is useful for project level pavement condition prediction.
