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.

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

Development of Structural Condition Index to Support Pavement Maintenance and Rehabilitation Decisions at Network Level

2003 ◽  
Vol 1827 (1) ◽  
pp. 10-17 ◽  
Author(s):  
Zhanmin Zhang ◽  
German Claros ◽  
Lance Manuel ◽  
Ivan Damnjanovic
Keyword(s):  
Evaluation Method ◽  
Surface Condition ◽  
Condition Index ◽  
Structural Condition ◽  
Pavement Management ◽  
Structural Deformation ◽  
Pavement Maintenance ◽  
State Highway ◽  
Structural Reinforcement ◽  
Maintenance And Rehabilitation

Every year, state highway agencies apply large amounts of seal coats and thin overlays to pavements to improve the surface condition, but these measures do not successfully address the problem. Overall pavement condition continues to deteriorate because of the structural deformation of pavement layers and the subgrade. To make effective decisions about the type of treatment needed, one should take into consideration the structural condition of a pavement. Several different structural estimators can be calculated by using falling weight deflectometer data and information stored in the Pavement Management Information System (PMIS) at the Texas Department of Transportation. The analysis considers pavement modulus and structural number as the structural estimators of a pavement. The evaluation method is based on the sensitivity of the structural estimators to deterioration descriptors. The deterioration per equivalent single-axle load of all major scores stored in the Texas PMIS is proposed as the primary indicator of pavement deterioration. In addition, the use of the structural condition index is recommended as a screening tool to discriminate between pavements that need structural reinforcement and those that do not. This index is calibrated for use in maintenance and rehabilitation analysis at the network level.

Uncertainty, time, and risk in the optimization of maintenance and rehabilitation of pavements

1984 ◽  
Vol 11 (2) ◽  
pp. 308-323
Author(s):  
Peter Bein
Keyword(s):  
Utility Theory ◽  
Decision Model ◽  
Pavement Management ◽  
Multiattribute Utility ◽  
Transition Matrices ◽  
Pavement Maintenance ◽  
Maintenance And Rehabilitation ◽  
Markov Decision ◽  
Powerful Research Tool ◽  
Markov Decision Model

A Markov decision model for the optimization of one section of highway or street pavement maintenance and rehabilitation incorporating utility theory is outlined. The model takes account of the uncertain pavement behaviour and of the interdependence of maintenance actions over time. An approach for estimating and updating Markov transition matrices for pavements is proposed.The objective function quantifies the pavement manager's attitudes toward the risk posed by the probabilities of pavement condition, magnitudes of consequences, and timing of decisions. Multiattribute utility theory is employed to aggregate multiple criteria, and to model the pavement manager's preferences in multiyear planning scenarios.The methodology is applied to the optimization of maintenance and rehabilitation of one highway pavement section. The preferences of five engineers are tested. These tests show that additive evaluation models are not appropriate for pavement management. Utility functions of one engineer are used in an illustrative example to demonstrate feasibility of the approach.The presented model deals with the project level of decisions. However, the Markov decision approach combined with multiattribute utility can also be useful when modified to deal with questions arising at the network level. At both levels, the approach provides a powerful research tool capable of answering a variety of pavement management questions. Key words: pavements, maintenance and rehabilitation, management aids, Markov decision model, multiattribute utility, probability updating.

Investigating Pavement Maintenance and Rehabilitation Requirement Based on Material Types and Traffic Levels through an Application of Probabilistic Approach for Ontario Highways

2019 ◽  
Vol 2673 (11) ◽  
pp. 561-572
Author(s):  
Gulfam Jannat ◽  
Susan L. Tighe
Keyword(s):  
Probabilistic Approach ◽  
Condition Index ◽  
Probability Of Failure ◽  
Pavement Management ◽  
Deterministic Approach ◽  
Pavement Management System ◽  
Pavement Condition ◽  
Pavement Maintenance ◽  
Maintenance And Rehabilitation ◽  
Pavement Condition Index

In a pavement management system (PMS), time to maintenance is generally estimated based on the predicted condition of the pavement. Usually a deterministic approach is applied in the PMS to estimate the time to maintenance by following the deterioration equation of the performance index. However, it is necessary to be aware of the probability of failure to investigate whether the estimated time to maintenance by the deterministic approach is reasonably probable. For this reason, a probabilistic approach is applied in this study to estimate the probability of failure over the estimated time to maintenance. In this approach, the probability of failure is estimated from the distribution of the mean time to maintenance by considering both the overall condition of the pavement and individual instances of distress. These mean times to failure or maintenance are calculated from the overall condition of pavement in relation to the pavement condition index (PCI) when the trigger value becomes 65 or less. A pavement may be expected to fail, however, because of any specific distress before it reaches the PCI trigger value for maintenance. For this reason, the probability of failure of each specific distress is also investigated by using a Monte Carlo simulation. It is found that the survival probability up to the fifth year is approximately 80% to 90% for each category of traffic and material type based on the overall condition, and the probability of failure for individual distress is very low over the performance cycle.

Finite element analysis of fire-damaged flexible pavement deterioration

2017 ◽  
Vol 8 (2) ◽  
pp. 106-116
Author(s):  
Rudi van Staden ◽  
Sam Fragomeni
Keyword(s):  
Finite Element ◽  
Fatigue Cracking ◽  
Element Analysis ◽  
Content Type ◽  
Operational Conditions ◽  
Pavement Maintenance ◽  
Pavement Deterioration ◽  
Maintenance And Rehabilitation ◽  
The Impact ◽  
Cent Decrease

Purpose This research aims to use the finite element method to examine critical distress modes in the pavement layers due to changes in the structural properties brought upon by fire damage. Design/methodology/approach A full dynamic analysis is performed to replicate heavy vehicle axle wheel loads travelling over a pavement section. Findings Results show a 72 per cent decrease in the number of load repetitions which a fire-damaged pavement can experience before fatigue cracking of the asphalt. Further, there is a 51 per cent decrease in loading cycles of the subgrade before rutting of the fire-damaged system. Originality/value Fatigue of asphalt and deformation of subgrade from repeated vehicular loading are the most common failure mechanisms, and major attributors to pavement maintenance and rehabilitation costs. Pavement analysis has always been concentrated on evaluating deterioration under regularly occurring operational conditions. However, the impact of one-off events, such as vehicle petroleum fires, has not been evaluated for the effects on deterioration.

scholarly journals Rutting Model for HMA Overlay Treatment of Composite Pavements

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Mohammad Abdullah Nur ◽  
Mohammad Jamal Khattak ◽  
Mohammad Reza-Ul-Karim Bhuyan
Keyword(s):  
Pavement Management ◽  
Management Tool ◽  
Performance Models ◽  
Factors Affecting ◽  
Pavement Condition ◽  
Pavement Maintenance ◽  
Treatment Performance ◽  
Maintenance And Rehabilitation ◽  
Pavement Service Life ◽  
Good Agreement

Timely rehabilitation and preservation of pavement systems are imperative to maximize benefits in terms of driver’s comfort and safety. However, the effectiveness of any treatment largely depends on the time of treatment and triggers governed by treatment performance models. This paper presents the development of rutting model for overlay treatment of composite pavement in the State of Louisiana. Various factors affecting the rutting of overlay treatment were identified. Regression analysis was conducted, and rut prediction model is generated. In order to better predict the pavement service life, the existing condition of the pavement was also utilized through the model. The developed models provided a good agreement between the measured and predicted rut values. It was found that the predictions were significantly improved, when existing pavement condition was incorporated. The resulting rutting model could be used as a good pavement management tool for timely pavement maintenance and rehabilitation actions to maximize LADOTD benefits and driver’s comfort and safety.

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.

Detection of Pavement Maintenance Treatments using Deep-Learning Network

2021 ◽  
pp. 036119812110078
Author(s):  
Lu Gao ◽  
Yao Yu ◽  
Yi Hao Ren ◽  
Pan Lu
Keyword(s):  
Deep Learning ◽  
Measurement Errors ◽  
Short Term Memory ◽  
Learning Networks ◽  
Pavement Maintenance ◽  
Time Period ◽  
Learning Techniques ◽  
Maintenance And Rehabilitation ◽  
History Of ◽  
Deep Learning Network

Pavement maintenance and rehabilitation (M&R) records are important as they provide documentation that M&R treatment is being performed and completed appropriately. Moreover, the development of pavement performance models relies heavily on the quality of the condition data collected and on the M&R records. However, the history of pavement M&R activities is often missing or unavailable to highway agencies for many reasons. Without accurate M&R records, it is difficult to determine if a condition change between two consecutive inspections is the result of M&R intervention, deterioration, or measurement errors. In this paper, we employed deep-learning networks of a convolutional neural network (CNN) model, a long short-term memory (LSTM) model, and a CNN-LSTM combination model to automatically detect if an M&R treatment was applied to a pavement section during a given time period. Unlike conventional analysis methods so far followed, deep-learning techniques do not require any feature extraction. The maximum accuracy obtained for test data is 87.5% using CNN-LSTM.

Sign in / Sign up

Export Citation Format

Share Document