Sensitivity of Design Parameters on Optimal Pavement Maintenance Decisions at the Project Level

2008 ◽  
Vol 2084 (1) ◽  
pp. 47-54 ◽  
Author(s):  
R. Priya ◽  
K. K. Srinivasan ◽  
A. Veeraragavan
Keyword(s):  
Design Parameters ◽  
Pavement Maintenance ◽  
Maintenance Decisions ◽  
Project Level

Establishing optimal project-level strategies for pavement maintenance and rehabilitation – A framework and case study

2012 ◽  
Vol 44 (5) ◽  
pp. 565-589 ◽  
Author(s):  
Muhammad Irfan ◽  
Muhammad Bilal Khurshid ◽  
Qiang Bai ◽  
Samuel Labi ◽  
Thomas L. Morin
Keyword(s):  
Pavement Maintenance ◽  
Maintenance And Rehabilitation ◽  
Project Level

scholarly journals Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions

2021 ◽  
Vol 11 (6) ◽  
pp. 2458
Author(s):  
Ronald Roberts ◽  
Laura Inzerillo ◽  
Gaetano Di Mino
Keyword(s):  
Deep Learning ◽  
Management Practices ◽  
Prediction Models ◽  
Road Networks ◽  
Pavement Management ◽  
Efficient Operation ◽  
Pavement Maintenance ◽  
Goods And Services ◽  
Maintenance Decisions ◽  
Computational Systems

Road networks are critical infrastructures within any region and it is imperative to maintain their conditions for safe and effective movement of goods and services. Road Management, therefore, plays a key role to ensure consistent efficient operation. However, significant resources are required to perform necessary maintenance activities to achieve and maintain high levels of service. Pavement maintenance can typically be very expensive and decisions are needed concerning planning and prioritizing interventions. Data are key towards enabling adequate maintenance planning but in many instances, there is limited available information especially in small or under-resourced urban road authorities. This study develops a roadmap to help these authorities by using flexible data analysis and deep learning computational systems to highlight important factors within road networks, which are used to construct models that can help predict future intervention timelines. A case study in Palermo, Italy was successfully developed to demonstrate how the techniques could be applied to perform appropriate feature selection and prediction models based on limited data sources. The workflow provides a pathway towards more effective pavement maintenance management practices using techniques that can be readily adapted based on different environments. This takes another step towards automating these practices within the pavement management system.

A semi-Markov formulation of the pavement maintenance optimization problem

1993 ◽  
Vol 20 (3) ◽  
pp. 436-447 ◽  
Author(s):  
Dale M. Nesbit ◽  
Gordon A. Sparks ◽  
Russell D. Neudorf
Keyword(s):  
Pavement Management ◽  
Maintenance Optimization ◽  
Infrastructure Management ◽  
Pavement Maintenance ◽  
Pavement Deterioration ◽  
Maintenance And Rehabilitation ◽  
Computationally Intensive ◽  
Asset Depreciation ◽  
Special Case ◽  
Project Level

The problem of determining optimal pavement maintenance and rehabilitation strategies is a special case of a more general problem termed the asset depreciation problem. Perhaps the most general formulation and solution of the asset depreciation problem is the semi-Markov formulation. This paper illustrates how the semi-Markov formulation and solution of the general asset depreciation problem can be applied to pavements. The semi-Markov formulation, like the Markov formulation, characterizes pavement deterioration probabilistically and represents human intervention (maintenance and rehabilitation) as slowing or modifying the basic probabilities of deterioration. The Markov formulation, first implemented for the state of Arizona, is shown to be a special case of the more general, less computationally intensive semi-Markov formulation. The application of the semi-Markov formulation is illustrated at the project level for a heavy-duty pavement in Manitoba. Key words: asset depreciation, infrastructure management, pavement management, probabilistic modelling, Markov, semi-Markov, maintenance optimization, project level.

Knowledge-based advisory system for flexible pavement routine maintenance

1993 ◽  
Vol 20 (1) ◽  
pp. 154-163 ◽  
Author(s):  
Paulette B. Hanna ◽  
Awad S. Hanna ◽  
Thomas A. Papagiannakis
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Practical Experience ◽  
Asphalt Pavements ◽  
Pavement Maintenance ◽  
Advisory System ◽  
Knowledge Based ◽  
Other Information ◽  
Maintenance Decisions ◽  
Asphalt Concrete Pavement

One of the most pressing problems facing Canada is the condition of roadway infrastructure. Making good maintenance decisions requires years of practical experience and judgement. Expert systems have great potential for solving pavement maintenance problems that usually require significant human expertise for solution. Expert systems, also known as knowledge-based systems, have been used as a means for conveying pavement maintenance knowledge gained through research and field experience to individuals responsible for maintaining asphalt pavements. An expert system is defined as an interactive computer program which documents judgement, experience, intuition, and other information in order to provide knowledgeable advice.This paper describes the development of PMAS, a pavement maintenance advisory system, which can assist highway engineers in planning effective flexible or asphalt concrete pavement maintenance strategies. The system uses two alternative commercial expert system shells. The system questions the user in a multiple-choice format in everyday English and (or) by using pictures. The user responds by selecting one or more of the choices provided by the system. At the end of the consultation session, the system displays the most appropriate maintenance strategy along with its life expectancy. PMAS facilitates the decision-making process and could serve as a consultant for field engineers. Key words: expert system, knowledge-based system, pavement maintenance.

scholarly journals Assessing pavement conditions and their effect on road safety: Ontario experience

2021 ◽  
Author(s):  
Iliya Nemtsov
Keyword(s):  
Road Safety ◽  
Empirical Bayes ◽  
Prediction Models ◽  
Cross Sectional ◽  
Pavement Maintenance ◽  
Pavement Distress ◽  
Pavement Roughness ◽  
Maintenance Decisions ◽  
Safety Effect ◽  
The Impact

The increasing need to rebuild and repair Ontario highways has motivated this research aimed at maximizing the efficiency of pavement maintenance and design. The first of two complementary objectives were to evaluate the safety improvements of reduced pavement roughness on two-lane undivided Ontario highways using the Empirical Bayes and Cross-Sectional analysis methods. The second objective was to improve the prediction of pavement distress and surface roughness by examining the impact of local calibration of prediction models. The findings suggest that better pavement conditions can reduce the severity of fatal and injury collisions by as much as 12% in some cases and therefore that pavement maintenance decisions should incorporate road safety when assessing cost-life analysis. The results provide a basis for those decisions in that they can be used to estimate the safety effect of a specific improvement in roughness.

Probabilistic assessment of pavement conditions

2001 ◽  
Vol 28 (5) ◽  
pp. 813-822 ◽  
Author(s):  
H P Hong ◽  
S Somo
Keyword(s):  
Early Stage ◽  
Rectangular Pulse ◽  
Traffic Load ◽  
Pavement Performance ◽  
Design Parameters ◽  
Pavement Maintenance ◽  
Comfort Index ◽  
Simple Simulation ◽  
Reliability Method ◽  
Environmental Actions

Pavements are subjected to repeated traffic and environmental actions. These actions lead to the degradation of pavement and affect the pavement performance. The assessment of pavement performance is important not only for selecting pavement design parameters but also for choosing pavement maintenance and rehabilitation strategies. The traffic loads, the environmental actions, and the properties of pavement material are uncertain. These uncertainties must be taken into account when predicting the pavement performance. Since the traffic and environmental actions vary with time, they should be modeled as stochastic processes. In this study, stochastic models of the net traffic load growth and environmental actions are proposed based on the rectangular pulse processes. These models can be used in conjunction with the Ontario Pavement Analysis of Cost (OPAC) model to predict flexible pavement performance in a probabilistic framework. The prediction of pavement performance for a typical pavement was carried out by using the simple simulation technique and the first-order reliability method. The analysis results suggest that the predicted pavement serviceability measured in terms of the riding comfort index depends on the correlation between traffic effects in each year and the correlation between the environmental actions in each year. The results also suggest that the uncertainty in the riding comfort index is controlled by the uncertainty in traffic and environmental actions in the early stage of service, while it is dominated by the material property of the subgrade in the later stage of service.Key words: deterioration, reliability, pavement, serviceability, stochastic process.

scholarly journals Assessing pavement conditions and their effect on road safety: Ontario experience

2021 ◽  
Author(s):  
Iliya Nemtsov
Keyword(s):  
Road Safety ◽  
Empirical Bayes ◽  
Prediction Models ◽  
Cross Sectional ◽  
Pavement Maintenance ◽  
Pavement Distress ◽  
Pavement Roughness ◽  
Maintenance Decisions ◽  
Safety Effect ◽  
The Impact

The increasing need to rebuild and repair Ontario highways has motivated this research aimed at maximizing the efficiency of pavement maintenance and design. The first of two complementary objectives were to evaluate the safety improvements of reduced pavement roughness on two-lane undivided Ontario highways using the Empirical Bayes and Cross-Sectional analysis methods. The second objective was to improve the prediction of pavement distress and surface roughness by examining the impact of local calibration of prediction models. The findings suggest that better pavement conditions can reduce the severity of fatal and injury collisions by as much as 12% in some cases and therefore that pavement maintenance decisions should incorporate road safety when assessing cost-life analysis. The results provide a basis for those decisions in that they can be used to estimate the safety effect of a specific improvement in roughness.

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