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.

scholarly journals Coordinated Pavement Management System between Municipalities and State in Rhode Island

2019 ◽  
Author(s):  
masoud faramarzi
Keyword(s):  
Rhode Island ◽  
Management System ◽  
Surface Condition ◽  
Condition Index ◽  
Pavement Management ◽  
Pavement Management System ◽  
Health Indices ◽  
State Highway ◽  
Budget Analysis ◽  
Transportation Agencies

State of Rhode Island and Providence Plantation (RI) has 6,052 miles roadways: 1,098 miles State-maintained highways, 4,766 miles cities and towns-maintained roadways, 188 miles for other jurisdictions. Most transportation agencies are using their own pavement management system (PMS); however, the coordinated system for state highways and municipally maintained roads appears to be absent. Thus, a coordinated effort has been made successfully among state, municipalities and academia in RI. A standardized PMS with MicroPAVER™ was established for Kingston campus at University of Rhode Island (URI) to help the implementation for cities and towns in 1988. URI team suggested to evaluate only one representative sample unit per section. Condition survey has been performed for 30 years, and current and future condition were determined and predicted for URI campus, respectively. Maintenance and rehabilitation (M&R) strategies were established and budget analysis was performed for needed cities and towns. Rhode Island Department of Transportation (RIDOT) adopted a PMS based on surface condition as well as roughness from 1985. Like MicroPAVER™ pavement condition index (PCI), RIDOT developed and calculated pavement structural health indices (PSHIs) for each 1/10th of mile segment of highway. Gradually RIDOT has been using Deighton Total Infrastructure Management System (dTIMS™) as its PMS for state-maintained highways since 1993. To coordinate two systems, MicroPAVER™ and dTIMS™ were used for network of Cranston city and RI state highway in the present study, respectively. Hope that this model PMS will stimulate more implementation for other transportation agencies.

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.

Application of Traffic Speed Deflectometer for Network-Level Pavement Management

2018 ◽  
Vol 2672 (40) ◽  
pp. 348-359 ◽  
Author(s):  
Shivesh Shrestha ◽  
Samer W. Katicha ◽  
Gerardo W. Flintsch ◽  
Senthilmurugan Thyagarajan
Keyword(s):  
Surface Condition ◽  
Structural Condition ◽  
Pavement Management ◽  
Falling Weight Deflectometer ◽  
Pavement Management System ◽  
State Highway ◽  
Structural Evaluation ◽  
Traffic Speed ◽  
State Highway Agencies ◽  
Falling Weight

In this paper, the traffic speed deflectometer (TSD), a device used for network level structural evaluation, is assessed. TSD testing was performed in nine states on a total of 5,928 miles (some repeated) during three time periods: November 2013, May to July 2014, and June to September 2015. This paper presents (1) the results of repeatability and comparison of the TSD with the falling weight deflectometer (FWD), (2) the results of the comparison of TSD measurements with typical pavement management system (PMS) data, and (3) an approach that can be implemented by State Highway Agencies (SHAs) to incorporate indices derived from TSD data into their PMS decision-making process. The results show that repeated TSD measurements follow similar trends and the TSD measurements and FWD measurements on the same pavement sections follow similar trends as well. Comparing TSD measurements with PMS surface condition data confirmed that the TSD provided valuable information about the structural condition of the tested pavement sections that cannot be derived from the already available pavement surface condition as part of an agency’s PMS. An example of how TSD information can be used to refine the triggered maintenance treatment category as part of a network-level PMS analysis is presented for a roughly 75-mile section of I-81 south in Virginia.

scholarly journals Proposal and Implementation of a Heliport Pavement Management System: Technical and Economic Comparison of Maintenance Strategies

2021 ◽  
Vol 13 (16) ◽  
pp. 9201 ◽  
Author(s):  
Paola Di Mascio ◽  
Alessio Antonini ◽  
Piero Narciso ◽  
Antonio Greto ◽  
Marco Cipriani ◽  
...  
Keyword(s):  
Management System ◽  
Condition Index ◽  
Pavement Management ◽  
Pavement Management System ◽  
Pavement Condition ◽  
Airport Pavement ◽  
Maintenance And Rehabilitation ◽  
Pavement Condition Index ◽  
Rotary Wing ◽  
Economic Comparison

Maintenance and rehabilitation (M&R) scheduling for airport pavement is supported by the scientific literature, while a specific tool for heliport pavements lacks. A heliport pavement management system (HPMS) allows the infrastructure manager to obtain benefits in technical and economic terms, as well as safety and efficiency, during the analyzed period. Structure and rationale of the APSM could be replicated and simplified to implement a HPMS because movements of rotary-wing aircrafts have less complexity than fixed-wing ones and have lower mechanical effects on the pavement. In this study, an innovative pavement condition index-based HPMS has been proposed and implemented to rigid and flexible surfaces of the airport of Vergiate (province of Varese, Italy), and two twenty-year M&R plans have been developed, where the results from reactive and proactive approaches have been compared to identify the best strategy in terms of costs and pavement level of service. The result obtained shows that although the loads and traffic of rotary-wing aircrafts are limited, the adoption of PMS is also necessary in the heliport environment.

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.

Protocol for FWD Data Collection at Network-Level Pavement Management in Iran

2018 ◽  
Vol 2672 (40) ◽  
pp. 68-77 ◽  
Author(s):  
Narges Matini ◽  
Nader Tabatabaee ◽  
Mojtaba Abbasghorbani
Keyword(s):  
Management System ◽  
Condition Index ◽  
Structural Condition ◽  
Pavement Management ◽  
Pavement Management System ◽  
Potential Safety ◽  
Time Required ◽  
Falling Weight Deflectometers ◽  
Ground Penetrating ◽  
Falling Weight

The objective of this study was to develop an approach for incorporating techniques used to interpret and evaluate deflection data for network-level pavement management system applications. A national pavement management system is being developed in Iran and the use of falling weight deflectometers (FWDs) at the network level was deemed necessary to compensate for the lack of vital construction history data in the pavement inventory. Because FWD measurements disrupt traffic flow and are a potential safety hazard, it is imperative to increase the interval between FWD testing points as much as possible to allow scanning of the entire 51,000 km network of freeways, highways, and major roads in a reasonable time span with the least traffic disruption. A project-level dataset at 0.2 km intervals in different environments and diverse traffic categories was selected for analysis. In addition, data from continuous ground-penetrating radar was collected concurrently and compared with a limited number of cores. The overall analysis included evaluation of interval variation, segmentation, the structural condition index (SCI), and layer moduli calculated using the AASHTO and ELMOD methods. The analysis was done to determine the optimum interval between test points. Analysis showed that the collection intervals could be increased from 0.2 to 0.6 km. Subsequently, the applicability and time efficiency of the network-level intervals were verified by calculating overlay thickness and time required.

scholarly journals Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

2020 ◽  
Author(s):  
Nader Karballaeezadeh ◽  
Danial Mohammadzadeh S. ◽  
Dariush Moazami ◽  
Narjes Nabipour ◽  
Amir Mosavi ◽  
...  
Keyword(s):  
Random Forest ◽  
Surface Condition ◽  
Monitoring And Evaluation ◽  
Condition Index ◽  
Structural Condition ◽  
Condition Assessment ◽  
Sample Unit ◽  
Safety Risks ◽  
Pavement Condition Index ◽  
Structural Condition Assessment

The construction of different roads, such as freeways, highways, major roads or minor roads must be accompanied by constant monitoring and evaluation of service delivery. Pavements are generally assessed by engineers in terms of the smoothness, surface condition, structural condition and surface safety. Pavement assessment is often conducted using the qualitative indices such as international roughness index (IRI), pavement condition index (PCI), structural condition index (SCI) and skid resistance value (SRV), which are used for smoothness assessment, surface condition assessment, structural condition assessment, and surface safety assessment, respectively. In this paper, Tehran-Qom Freeway in Iran has been selected as the case study and its smoothness and pavement surface conditions are assessed. At 2-km intervals, a 100-meter sample unit is selected in the slow-speed lane (totally, 118 sample units). In these sample units, the PCI is calculated after a visual inspection of the pavement and the recording of distresses. Then, in each sample unit, the average IRI is computed. The purpose of this study is to provide a method for estimating PCI based on IRI. The proposed theory was developed by Random Forest (RF), and Random Forest optimized by Genetic Algorithm (RF-GA) methods and these methods were validated using correlation coefficient (CC), scattered index (SI), and Willmott’s index of agreement (WI) criteria. The proposed method reduces costs, saves time and eliminates the safety risks.

Development of Adaptive Performance Models for Oklahoma Airfield Pavement Management System

2003 ◽  
Vol 1853 (1) ◽  
pp. 44-54 ◽  
Author(s):  
Jie Yuan ◽  
Michael A. Mooney
Keyword(s):  
Management System ◽  
Condition Index ◽  
Pavement Management ◽  
General Aviation ◽  
Portland Cement Concrete ◽  
Performance Models ◽  
Pavement Management System ◽  
Pavement Condition ◽  
Maintenance And Rehabilitation ◽  
The Family

The Oklahoma airfield pavement management system (APMS) is a set of pavement management tools that can assist with pavement condition evaluation, as well as prioritization and scheduling of pavement maintenance and rehabilitation activities. Pavement performance models were developed to support the APMS for more than 70 Oklahoma general aviation airports. The family modeling method based on the pavement condition index was tailored to fit the deterioration characteristics of these airfield pavements. The statistical and engineering significance of seven levels of pavement factors was investigated, and pavement factors that affect pavement deterioration significantly were identified as family variables. Asphalt concrete pavement families were formed by sorting pavement function, distress cause, and pavement thickness, while portland cement concrete pavements were divided into families according to pavement function and climate zone. The family polynomial curves were able to reveal the expected deterioration patterns and are logical in engineering principle. Rooted by an adaptive database, the system accepts expert opinion and automatically integrates effects of major maintenance and rehabilitation activities into modeling. From the up-to-date database, the performance models update forecasts automatically.

scholarly journals Optimization Model of Network-Level Pavement Maintenance Decision considering User Travel Time and Vehicle Fuel Consumption Costs

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Leilei Chen ◽  
Zepeng Fan ◽  
Pengfei Liu ◽  
Zhendong Qian
Keyword(s):  
Travel Time ◽  
Fuel Consumption ◽  
Optimization Model ◽  
Evaluation Method ◽  
Optimal Strategies ◽  
Optimization Method ◽  
Condition Index ◽  
Ride Quality ◽  
Price Ratio ◽  
Pavement Maintenance

The maintenance management decisions of network-level asphalt pavements have long been a challenge to highway agencies, and a great amount of factors have been involved. In this study, a network-level optimization method was established by integrating the maintenance benefits into the zero-one programming optimization model. An optimized performance evaluation method of asphalt pavement was proposed which contains 11 different kinds of combinations. The benefit model quantifies the cost savings of user travel time and vehicle fuel consumption to the pavement condition index (PCI) and ride quality index (RQI), respectively. Based on the simplified evaluation method as well as the quantified maintenance benefit model, an optimization model was established by employing the zero-one programming. This optimization model aimed to maximize the improvements/price ratio of pavement maintenance for the whole pavement network. The calculation results present the optimal strategies of maintenance for every road section in the network. The applicability of the newly proposed model was validated by a case study. The methodology developed in this study helps to offer guidelines to highway agencies in managing and making decisions about network-level pavement maintenance.

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.

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