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Publication Number: FHWA-HRT-12-023
Date: December 2012
The primary objectives of this study were to identify/develop, verify, and recommend simplified deflection-based analytical techniques suitable for rapid automated screening of pavement structural capacity for inclusion in a network-level analysis, such as PMS. The LTPP database was the primary source of information for this study.(1) The results can be readily implemented in national, State, and local municipality systems alike.
An additional objective was to provide recommendations for data collection procedures that will maximize testing productivity and minimize risk while still providing adequate information for use in a typical PMS. Test point spacings and frequency of data collection were the two primary parameters of interest.
A handful of successful techniques for use in network-level PMS applications were identified through the literature search. A few highway agencies have been using deflections as part of their network condition assessment. Among these, Texas, Virginia, and South African approaches were selected for further investigation. Several techniques commonly used at the project-level were investigated, as well. Combining all sources available, a list of potential applicable techniques was created. These deflection techniques formed the basis for the analyses carried out in this project.
Deflection data analysis provides qualitative and quantitative assessment of the structural integrity and bearing capacity of a pavement. Pavements with poor structural quality are more likely to develop distresses prematurely. It is commonly expected that the rate of deterioration increases as the structural condition worsens. Identifying pavements with poor structural condition is important to prevent early and rapid development of load-related distresses.
Based on the above premises, a probabilistic approach was developed to determine the likelihood of premature failure using simplified and easy-to-apply load deflection techniques. Premature failure was defined by the presence of excessive distress occurring prior to the end of the design life of the pavement section (i.e., levels of distress higher than a design threshold). This was achieved by adopting binary logistic models that utilized deflection techniques derived from the FWD deflection basin coupled with various site-specific parameters.
The probability density function was used to determine the threshold levels that define structural condition. This was accomplished by determining the cutoff value in the logistic model, which determines how to convert a continuous probability prediction to a dichotomous outcome (i.e., predicted probabilities above the threshold were defined as acceptable while others were defined as not acceptable). This process was shown to be simple and straightforward. A structural decision matrix can be generated and incorporated as an integral part of an agency's PMS optimization tools.
Several models were created based on pavement type and critical performance measures. For flexible pavements, the models created were based on roughness, rutting, and fatigue cracking. For rigid pavements, the models were based on roughness, faulting, and transverse slab cracking.
The advantages of the stochastic approach to evaluate the structural condition of the pavement for network-level analysis can be summarized as follows:
It provides a direct link to pavement performance by estimating the likelihood of the development of load-related distresses in the early stages of the pavement's service life. The intent was not to have a regression-based predictive model based on deflection data, but rather a simplified procedure to identify critical sections with a high (stochastic) likelihood of developing structural distresses prematurely.
The probability density function is based on qualitative performance measures - acceptable and not acceptable.
Rating criteria can be created in the same a manner equivalent to the functional decision matrix. Different tiers of structural quality can be defined based on deflection thresholds and the likelihood of acceptable performance. The structural decision matrix can then follow rating criteria similar to the functional decision matrix, which facilitates the implementation in existing PMS algorithms.
The stochastic model is site-specific (i.e., additional variables may be incorporated that reflect site characteristics such as traffic, pavement structure, and climatic conditions).
Different structural decision matrices can be defined based on different types of distresses. Therefore, the final evaluation can be made based on the most critical or most typical distress(es) observed in the network.
The calculations are simple and do not require an interactive, labor-intense process. Therefore, it can be automated and incorporated in virtually any PMS in which deflection data are available.
Deflection data obtained from project-level analyses or quality control after construction can also be used as an agency's initial input data.
The probabilistic models can be locally calibrated to reflect an agency's own network characteristics (e.g., typical surface distresses observed, the interval between M&R projects, and the threshold of acceptable limits for distresses). In addition, the models can be recalibrated every time a new FWD testing campaign is completed and new load deflection data become available.
The importance of local calibration is underscored. Four examples were provided, which showed the potential improvements in accuracy and predictability that can be obtained if local data are used to calibrate the probabilistic models. The examples were simple exercises retrieved based on subsets of the LTPP database. Nevertheless, it was clear that local calibration could ultimately enhance the accuracy and quality of predictions, which would significantly benefit analyses at the network level, especially for the creation and allocation of M&R resources.
The approach was based on an evaluation of errors as a function of different test spacings in a variety of section lengths. The error represents the expected difference between the data sample and the idealized true value of the population, which is represented in this case by the average deflection value of a homogeneous road segment. It was assumed that the true value of the population was the average deflection of a homogenous segment where the spacing between deflection points was at the project-level (i.e., 0.1 mi (0.161 km)). Monte Carlo simulations were used to model the error function.
In addition to modeling the predicted (average) error, the results from the Monte Carlo simulation were also used to model a probabilistic component to the calculation of the expected error. This component was easily related to the length of the section and the expected error for a given reliability level. The reliability component is an important characteristic of this approach, as it fits well with current design practices such as the MEPDG.(27) Tables were created that relate reliability level and section length with expected error, which can be used to define the optimum test spacings for a given section in a network-level analysis.
Ultimately, it is an agency's budget that controls the quantity of deflection data that can be collected in any given year. Objective recommendations and guidelines were provided in this report to determine optimum test spacings given the level of accuracy and reliability desired.
The recommended frequency of FWD data collection on pavements is dependent on the overall "rate of change" of structural conditions over time. The analysis of the deflections available in the LTPP database provided the following conclusions for flexible pavements:
Based on the analysis of these pavement sections, 5 years between FWD testing is recommended at the network-level for flexible pavements.
A similar analysis was conducted for rigid pavements. As expected, the variations in deflections measured at the center of the slab were significantly smaller than observed for flexible pavements. The recommendation is for a frequency of 10 years maximum between network-level testing.
Time of Day and Season of Year
The vast majority of deflection testing - whether at the network or project level - is carried out during normal daytime (or nighttime in high trafficked areas) work hours. In fact, any agency that may want to limit network-level testing to anything more or less than their normal working hours is unlikely to end up using deflection testing in their PMS at all. Similarly, with seasonal testing, it is generally believed that gathering deflection data should occur when climatic conditions allow it (i.e., pavement temperature is above freezing), except during spring thaw conditions, which are important to avoid since they are not representative of the rest of the year.
It is also important to consider the issue of P&MT. In reality, for most medium to high traffic roads, FWD testing must be carried out to avoid periods of high traffic flow, which often occurs during the morning rush hour (6 to 9 a.m.) and afternoon rush hour (3 to 5 p.m.).
Regarding the adjustment of deflections based on temperature, it is recommended that no adjustment is needed if the deflection data are going to be used at the network level.
Depending on the location and environmental circumstances, the overall recommendations as to when to conduct FWD test surveys at the network level may differ significantly. Obviously, limitations will inhibit any potential network testing program to the degree these limitations impose testing restrictions not generally encountered otherwise by agencies that manage roadway networks.
The incorporation of structural analysis provided a new dimension in the prediction of pavement performance and prescribed treatments to mitigate ride quality and structural deterioration of the overall pavement network. This process is relatively simple and can be easily incorporated in existing PMS. Algorithms can be written to include the structural analysis as part of the optimization routine. It was also shown that this process can be effectively done externally.
The incorporation of the new structural analysis changed the outcome of prescribed treatments on about 60 percent of the sections. Sections that initially did not have any prescribed treatment received preventive treatments. Preventive maintenance is key to an effective level of service in any pavement network. The identification of segments where early structural failure is likely to happen helps mitigate them through early preventive maintenance. As a consequence, more M&R can be done with the same budget level, and the backlog of pavement repairs can be reduced.
The new allocation of treatments obtained from the structural analysis provided greater improvement in ride quality. On average, about 10 percent improvement in IRI was expected when the optimization was done with a structural analysis for roughness compared to 6.9 percent when it was only a functional optimization.
The interval between treatments also increased. The previous functional analysis had an expected increase of 2.1 years in the period between treatments. The incorporation of a structural analysis in the optimization results expanded this period by 60 percent to a total of 3.4 years (structural analysis based on roughness) and by 33.7 percent to a total of 2.9 years (structural analysis based on rutting).
The incorporation of structural analysis in the example presented in this report had a clear impact on pavement preventive M&R program budget allocation, with a nominal increase in M&R cost of 50 percent, considering the critical scenario. However, there was a reduction in cost per mile of about 4 percent, indicating that more could be done with the same allocated budget. At the same time, there was a significant change in the improvements expected from the new M&R scenario based on the structural analysis - a 44 percent increase in expected ride quality and a 60 percent increase in the interval between treatments.
Overall, the consideration of structural condition in the network-level analysis improved performance, minimized maintenance, and reduced M&R costs per mile. The pay-off in lowered agency costs of pavement M&R easily surpasses the cost of adding FWD load deflection testing to the agency.
Topics: research, infrastructure, pavements and materials
Keywords: research, infrastructure, pavements and materials, Pavement performance, Rehabilitation, Maintenance, Pavement design, Long-term performance, Flexible pavements, Rigid pavements
TRT Terms: research, facilities, transportation, highway facilities, roads, parts of roads, pavements