scholarly journals Airfield Infrastructure Management Using Network-Level Optimization and Stochastic Duration Modeling

2019 ◽  
Vol 4 (1) ◽  
pp. 2 ◽  
Author(s):  
Mohamadhossein Noruzoliaee ◽  
Bo Zou
Keyword(s):  
Planning Horizon ◽  
Infrastructure Management ◽  
Pavement Condition ◽  
Modeling Approach ◽  
Stochastic Duration ◽  
Planning Framework ◽  
Pavement Deterioration ◽  
Exogenous Factors ◽  
Airport Runway ◽  
Simultaneous Implementation

This paper proposes a facility-specific modeling approach to plan maintenance and rehabilitation (M&R) activities on a network of airport runway pavement facilities. The objective of the modeling approach is to minimize system M&R cost while recommending M&R activities for each runway pavement facility over a planning horizon. To do so, pavement condition forecast is derived from estimating stochastic duration models which capture the inherent uncertainty and dynamics in pavement deterioration and impacts of exogenous factors. Building on the pavement condition forecast, a network optimization-based M&R planning framework is developed which accounts for the interdependence of M&R activities among facilities as reflected in (1) the requirement for aggregate pavement performance and (2) simultaneous implementation of a major M&R action on connected facilities. The budget constraint is also respected. The M&R planning framework with the stochastic duration model-based pavement condition forecast is applied to Chicago O’Hare International Airport. It is found that the proposed approach leads to much reduced M&R cost compared to the state-of-the-practice which does not consider the interdependence of M&R activities among different pavement facilities. On the other hand, accounting for the simultaneous implementation of a major M&R action on connected facilities would substantially increase M&R cost.

scholarly journals Pavement Deterioration Rate and Maintenance Cost for Low-Volume Roads

2020 ◽  
Vol 312 ◽  
pp. 06002
Author(s):  
Turki I Al-Suleiman ◽  
Subhi M Bazlamit ◽  
Mahmoud Azzama ◽  
Hesham S Ahmad
Keyword(s):  
Surface Defects ◽  
Condition Index ◽  
Maintenance Cost ◽  
Pavement Condition ◽  
Pavement Maintenance ◽  
Deterioration Rate ◽  
Pavement Deterioration ◽  
Pavement Condition Index ◽  
Traffic Volumes ◽  
Over Time

Allocated budgets for maintenance of road networks are normally limited. Therefore, not all roads receive the required attention they deserve in a timely manner. These roads are left to deteriorate until the next maintenance round. The cost associated with delayed maintenance is significantly excessive. A Pavement Maintenance Management System (PMMS) can be a useful tool for evaluation, prioritization of Maintenance and Rehabilitation (M&R) projects, and determination of funding requirements and allocations. The pavement condition is normally indexed using a parameter called Pavement Condition Index (PCI) which represents an overall assessment of surface defects by type, severity and extent. Periodic collections of PCI over time for different sections within the roadway network provide an approach to monitor changes in pavement serviceability over time and can produce useful data to predict and evaluate required maintenance solutions and their associated cost. The researchers intend to use available data collected over the span of a year and a half on sections within the roadway network at the campus of Al-Zaytoonah University of Jordan (ZUJ) to study the relation between the maintenance cost and the pavement deterioration rate. This study may incorporate variables such as pavement age, traffic volumes, maintenance history and pavement condition assessment results. The available records of PCI will be analyzed and the findings will be clearly presented. The practical inclusion of the findings within the current PMMS used at the university will also be detailed.

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.

scholarly journals HMCTS-OP: Hierarchical MCTS Based Online Planning in the Asymmetric Adversarial Environment

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 719
Author(s):  
Lina Lu ◽  
Wanpeng Zhang ◽  
Xueqiang Gu ◽  
Xiang Ji ◽  
Jing Chen
Keyword(s):  
Computational Complexity ◽  
State Space ◽  
Planning Horizon ◽  
Search Space ◽  
Monte Carlo Tree Search ◽  
Practical Applications ◽  
Online Planning ◽  
Planning Framework ◽  
Planning Methods ◽  
Planning Problems

The Monte Carlo Tree Search (MCTS) has demonstrated excellent performance in solving many planning problems. However, the state space and the branching factors are huge, and the planning horizon is long in many practical applications, especially in the adversarial environment. It is computationally expensive to cover a sufficient number of rewarded states that are far away from the root in the flat non-hierarchical MCTS. Therefore, the flat non-hierarchical MCTS is inefficient for dealing with planning problems with a long planning horizon, huge state space, and branching factors. In this work, we propose a novel hierarchical MCTS-based online planning method named the HMCTS-OP to tackle this issue. The HMCTS-OP integrates the MAXQ-based task hierarchies and the hierarchical MCTS algorithms into the online planning framework. Specifically, the MAXQ-based task hierarchies reduce the search space and guide the search process. Therefore, the computational complexity is significantly reduced. Moreover, the reduction in the computational complexity enables the MCTS to perform a deeper search to find better action in a limited time. We evaluate the performance of the HMCTS-OP in the domain of online planning in the asymmetric adversarial environment. The experiment results show that the HMCTS-OP outperforms other online planning methods in this domain.

scholarly journals Influence of Pavement Structure, Traffic, and Weather on Urban Flexible Pavement Deterioration

2020 ◽  
Vol 12 (22) ◽  
pp. 9717
Author(s):  
David Llopis-Castelló ◽  
Tatiana García-Segura ◽  
Laura Montalbán-Domingo ◽  
Amalia Sanz-Benlloch ◽  
Eugenio Pellicer
Keyword(s):  
Condition Index ◽  
Flexible Pavement ◽  
Average Height ◽  
Annual Average ◽  
Climate Conditions ◽  
Pavement Condition ◽  
Traffic Demand ◽  
Pavement Deterioration ◽  
Deterioration Process ◽  
Pavement Structure

Various studies have been recently conducted to predict pavement condition, but most of them were developed in a certain region where climate conditions were kept constant and/or the research focused on specific road distresses using single parameters. Thus, this research aimed at determining the influence of pavement structure, traffic demand, and climate factors on urban flexible pavement condition over time. To do this, the Structural Number was used as an indicator of the pavement capacity, various traffic and climate variables were defined, and the Pavement Condition Index was used as a surrogate measure of pavement condition. The analysis was focused on the calibration of regression models by using the K-Fold Cross Validation technique. As a result, for a given pavement age, pavement condition worsens as the Equivalent Single Axle Load and the Annual Average Height of Snow increased. Likewise, a cold Annual Average Temperature (5–15 °C) and a large Annual Average Range of Temperature (20–30 °C) encourage a more aggressive pavement deterioration process. By contrast, warm climates with low temperature variations, which are associated with low precipitation, lead to a longer pavement service life. Additionally, a new classification of climate zones was proposed on the basis of the weather influence on pavement deterioration.

Reliability-Based Processing of Markov Chains for Modeling Pavement Network Deterioration

1996 ◽  
Vol 1524 (1) ◽  
pp. 203-213 ◽  
Author(s):  
Ningyuan Li ◽  
Wei-Chau Xie ◽  
Ralph Haas
Keyword(s):  
Growth Rate ◽  
Transition Probability ◽  
Transition Process ◽  
Design Parameters ◽  
Pavement Condition ◽  
Monte Carlo Simulation Technique ◽  
Pavement Deterioration ◽  
Traffic Growth ◽  
Highway Network ◽  
Condition State

Accurate prediction of pavement deterioration is the most important factor in the determination of pavement repair years and optimization programming of highway network maintenance. The Nonhomogeneous Markov Probabilistic Modeling Program, developed to determine pavement deterioration rates in different stages, is described. In this program the transition probability matrices (TPMs) are considered as a time-related transition process. Each element of the TPMs is determined on the basis of a reliability analysis and a Monte Carlo simulation technique. This avoids the use of the existing conventional methods, which involve taking an average subjective opinion of pavement engineers or observing a large number of multiyear pavement performance data and conducting a number of statistical calculations. As a result a series of TPMs for an individual pavement section for different stages can be determined by running the program. Furthermore, the pavement condition state in terms of a probability vector at each stage (year) is calculated. In applying the models both the predicted actual traffic (in terms of equivalent single axle loads) at each stage and the maximum traffic that the pavement can withstand at each defined pavement condition state interval are considered to be random variables. In addition, the sensitivities of pavement deterioration rates to pavement design parameters, such as traffic growth rate, subgrade strength, and material properties, are studied. Finally, an example of calculating the TPMs for a pavement section located in southeastern Ontario, Canada, is demonstrated. It shows that the sensitivities of the TPMs to traffic growth rate, subgrade deflection, and pavement thickness are significant.

Development of Pavement Condition Rating Model and Pavement Roughness Model for Saudi Highways

2013 ◽  
Vol 723 ◽  
pp. 820-828 ◽  
Author(s):  
Muhammad Mubaraki
Keyword(s):  
Iri Model ◽  
International Roughness Index ◽  
Pavement Condition ◽  
Safety Issues ◽  
Condition Rating ◽  
Pavement Roughness ◽  
Roughness Model ◽  
Pavement Deterioration ◽  
Total Observation ◽  
Rating Model

The Pavement Condition Rating (PCR) has been used by the Ministry of Transport (MOT) in Saudi Arabia to report pavement condition. The World Bank developed the PCR in 1986. PCR is based on International Roughness Index (IRI), Rutting (RUT), Cracking (CRA), and Raveling (RAV). The MOT collects pavement condition data using a digital inspection vehicle called Road Surface Tester (RST) vehicle. On some expressways, the MOT measures the Skid Number (SN) using a Skid Test Unit as complimentary measurement for safety issues. The objective of this paper is to develop PCR model and pavement roughness model using survey data for overlaid sections on some expressways in the network with total observation number is 3469. The PCR model is a function of pavement age (T), Traffic Volume (TV), and IRI. The IRI model is a function of RUT, RAV, and CRA. Overlaid sections across the entire network have been selected to study the mechanisms of pavement deterioration, to develop the model and to draw conclusions.

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