Pavement Management Systems: Past, Present, and Future

2003 ◽  
Vol 1853 (1) ◽  
pp. 65-71 ◽  
Author(s):  
Ram B. Kulkarni ◽  
Richard W. Miller
Keyword(s):  
Performance Prediction ◽  
Prediction Models ◽  
Pavement Management ◽  
Pavement Performance ◽  
Management Systems ◽  
Seamless Integration ◽  
Positioning Systems ◽  
Pavement Management Systems ◽  
Transportation Organization ◽  
Pavement Performance Prediction

The progress made over the past three decades in the key elements of pavement management systems was evaluated, and the significant improvements expected over the next 10 years were projected. Eight specific elements of a pavement management system were addressed: functions, data collection and management, pavement performance prediction, economic analysis, priority evaluation, optimization, institutional issues, and information technology. Among the significant improvements expected in pavement management systems in the next decade are improved linkage among, and better access to, databases; systematic updating of pavement performance prediction models by using data from ongoing pavement condition surveys; seamless integration of the multiple management systems of interest to a transportation organization; greater use of geographic information and Global Positioning Systems; increasing use of imaging and scanning and automatic interpretation technologies; and extensive use of formal optimization methods to make the best use of limited resources.

Development of a unique deterioration index, prioritization methodology, and foreign object damage evaluation models for Canadian airfield pavement management

2004 ◽  
Vol 31 (4) ◽  
pp. 608-618 ◽  
Author(s):  
Anwar Shah ◽  
Susan Tighe ◽  
Allen Stewart
Keyword(s):  
Prediction Models ◽  
Pavement Management ◽  
Pavement Performance ◽  
Management Systems ◽  
Foreign Object ◽  
Foreign Object Damage ◽  
Condition Rating ◽  
Airport Pavement ◽  
Pavement Management Systems ◽  
Evaluation Models

Modelling of pavement performance deterioration is a critical engineering process in Pavement Management Systems. Most of the existing Airport Pavement Management Systems (APMS) employ limited surface distresses in their performance evaluation models. These systems may not serve the objectives of some agencies. It is essential for an effective APMS to include evaluation models that adequately address the specific needs of the agency. This paper presents the development of a unique pavement deterioration index, i.e., the Condition Rating Index, developed for 1 Canadian Air Division (1 CAD). This index is modelled to serve the specific needs of 1 CAD effectively. Performance prediction models for the various classes of pavements are developed based on Markov Chains. The prioritization methodology employed also reflects the needs of 1 CAD. Consequently, this paper investigates the quantification and prediction of Foreign Object Damage (FODp). The FODp Index is developed as well as defined. Prediction models for FODp are developed along with the establishment of critical states of the FODp Index.Key words: airport pavement management system, Condition Rating Index, pavement performance evaluation models, Foreign Object Damage, 1 Canadian Air Division.

Developing Performance Prediction Models for Pavement Management Systems in Local Governments in Absence of Age Data

2019 ◽  
Vol 2673 (3) ◽  
pp. 334-341 ◽  
Author(s):  
Nima Kargah-Ostadi ◽  
Yuxiao (Mina) Zhou ◽  
Tahmidur Rahman
Keyword(s):  
Performance Prediction ◽  
Local Governments ◽  
Regression Models ◽  
Prediction Models ◽  
Transition Probability ◽  
Pavement Management ◽  
Management Systems ◽  
Deterioration Rate ◽  
Pavement Management Systems ◽  
The Difference

Pavement Management Systems (PMS) use a strategic and data-driven approach to optimize budget allocation to various maintenance and rehabilitation (M&R) projects. Performance prediction models are used in PMS to determine the optimum timing for M&R interventions on every pavement section. Many local roadway agencies use empirical regression models which are based on past condition and age data. Often, these agencies are faced with limited resources for data collection and a high staff turnover rate, which all result in inadequate or unreliable construction history and pavement age data. This paper recommends a simple practical approach for local governments to develop performance prediction models in the absence of reliable pavement age data. Also, best practices for data pre-processing and validation of the model prediction capability are synthesized. Instead of using regression models based on condition and age, the pavement deterioration rate at each condition level is estimated. Similar to the Markovian transition probability concept, it is assumed that deterioration rates for every family of pavements are independent of time and only dependent on the current condition level. For every pair of subsequent condition measurements on a section, the difference in condition score is normalized by the difference in measurement time. These deterioration rates are then classified into bins based on the initial condition level for every pair of measurements. The average deterioration rate for all data records in each bin is then used to build a deterioration curve. This approach is demonstrated in this paper using real but anonymous agency data.

scholarly journals Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models

2021 ◽  
Vol 13 (9) ◽  
pp. 5248
Author(s):  
Rita Justo-Silva ◽  
Adelino Ferreira ◽  
Gerardo Flintsch
Keyword(s):  
Performance Prediction ◽  
Prediction Models ◽  
Pavement Management ◽  
Machine Learning Techniques ◽  
Pavement Performance ◽  
Road Maintenance ◽  
Road Transportation ◽  
Advantages And Disadvantages ◽  
Modeling Techniques ◽  
Pavement Performance Prediction

Road transportation has always been inherent in developing societies, impacting between 10–20% of Gross Domestic Product (GDP). It is responsible for personal mobility (access to services, goods, and leisure), and that is why world economies rely upon the efficient and safe functioning of transportation facilities. Road maintenance is vital since the need for maintenance increases as road infrastructure ages and is based on sustainability, meaning that spending money now saves much more in the future. Furthermore, road maintenance plays a significant role in road safety. However, pavement management is a challenging task because available budgets are limited. Road agencies need to set programming plans for the short term and the long term to select and schedule maintenance and rehabilitation operations. Pavement performance prediction models (PPPMs) are a crucial element in pavement management systems (PMSs), providing the prediction of distresses and, therefore, allowing active and efficient management. This work aims to review the modeling techniques that are commonly used in the development of these models. The pavement deterioration process is stochastic by nature. It requires complex deterministic or probabilistic modeling techniques, which will be presented here, as well as the advantages and disadvantages of each of them. Finally, conclusions will be drawn, and some guidelines to support the development of PPPMs will be proposed.

Development of Performance-Related Specifications for Hot-Mix Asphalt Pavements Through WesTrack

1997 ◽  
Vol 1575 (1) ◽  
pp. 85-91 ◽  
Author(s):  
Stephen B. Seeds ◽  
Rudramunniyappa Basavaraju ◽  
Jon A. Epps ◽  
Richard M. Weed
Keyword(s):  
Performance Prediction ◽  
Prediction Models ◽  
Hot Mix Asphalt ◽  
Permanent Deformation ◽  
Fatigue Cracking ◽  
Primary Objective ◽  
Pavement Performance ◽  
Void Content ◽  
Target Values ◽  
Pavement Performance Prediction

The primary objective of the FHWA-sponsored WesTrack project is to further the development of performance-related specifications for hotmix asphalt construction. This objective is being achieved, in part, through the accelerated loading of a full-scale test track facility in northern Nevada. Twenty-six hot-mix asphalt test sections constructed to meet the criteria set forth in a statistically based experiment design are providing performance data that will be used to improve existing (or develop new) pavement performance prediction relationships that better account for the effects that “off-target” values of asphalt content, air-void content, and aggregate gradation have on such distress factors as fatigue cracking, permanent deformation, roughness, raveling, and tirepavement friction. The concept of the planned new performance-related specification and how it will incorporate the modified pavement performance prediction models are described. The current plan for assessing contractor pay adjustments (i.e., penalties and bonuses) based on data collected from the as-constructed pavement is also discussed.

scholarly journals Comparisons of Faulting-Based Pavement Performance Prediction Models

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Weina Wang ◽  
Yu Qin ◽  
Xiaofei Li ◽  
Di Wang ◽  
Huiqiang Chen
Keyword(s):  
Performance Prediction ◽  
Prediction Models ◽  
Pavement Performance ◽  
Physical Parameters ◽  
Ann Model ◽  
Climate Data ◽  
Good Tool ◽  
Advantages And Disadvantages ◽  
Lower Accuracy ◽  
Pavement Performance Prediction

Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR) model, artificial neural network (ANN) model, and Markov Chain (MC) model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.

scholarly journals Calibration of HDM4 for Use in Pavement Management Systems (PMS)

2009 ◽  
Vol 32 (2) ◽  
pp. 103-113
Author(s):  
N.K. Mushule
Keyword(s):  
Developing Countries ◽  
Large Scale ◽  
Field Experiments ◽  
Developed Countries ◽  
Pavement Management ◽  
Pavement Performance ◽  
Management Systems ◽  
Local Conditions ◽  
Road Management ◽  
Pavement Management Systems

One of major problems of road management in developing countries is the lack of decision making toolsfor preparing road maintenance programmes. This results in poor resource allocation and poor utilisationof existing assets. Pavement management systems (PMS) have been developed around the world in order to assist with the pavement management process in a co-ordinated and systematic manner. Some developed countries have derived pavement performance models that are suited to local conditions by setting up long term pavement performance studies. Conversely, most developing countries do not have the resources required to set up similar large-scale field experiments. However, validation and calibration of models developed from comprehensive studies elsewhere offers a viable lternative for such countries. This paper evaluates the feasibility of using HDM-4 as a support software and determines level 1 calibration factors for PMS in Tanzania. The paper demonstrates the use of a calibrated HDM-4 to determine the required road management information in developing countries.

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