Long-Term Pavement Performance Data Research Articles

Global Warming and Its Effect on Binder Performance Grading in the USA: Highlighting Sustainability Challenges

The mounting impacts of climate change on infrastructure demand proactive adaptation strategies to ensure long-term resilience. This study investigates the effects of predicted future global warming on asphalt binder performance grade (PG) selection in the United States using a time series method. Leveraging Long-Term Pavement Performance (LTPP) data and Superpave protocol model, the research forecasts temperature changes for the period up to 2060 and calculates the corresponding PG values for different states. The results reveal significant temperature increases across the majority of states, necessitating adjustments in PG selection to accommodate changing climate conditions. The findings indicate significant increases in average 7-day maximum temperatures across the United States by 2060, with 38 out of 50 states likely to experience rising trends. Oregon, Utah, and Idaho are anticipated to face the largest temperature increases. Concurrently, the low air temperature has risen in 33 states, with notable increases in Maine, North Carolina, and Virginia. The widening gap predicted between required high and low PG poses challenges, as some necessary binders cannot be produced or substituted with other grades. The study highlights the challenge of meeting future PG requirements with available binders, emphasizing the need to consider energy consumption and CO2 emissions when using modifiers to achieve the desired PG properties.

Integrating Tensometer Measurements, Elastic Half-Space Modeling, and Long-Term Pavement Performance Data into a Mechanistic–Empirical Pavement Performance Model

Pavement performance models (PPMs) are utilized to predict pavement network conditions which is an essential part of any sustainable pavement management system (PMS). The reliability of a PMS and its outputs is proportional to the reliability of the PPM used. This article describes a mechanistic–empirical pavement performance model based on pavement response parameters—strains calculated in the pavement layers measured by tensometers embedded in the pavement surface and verified by calculations in the elastic half-space model and supplemented by empirical data from long-term pavement performance monitoring and accelerated pavement testing. Hence, the herein described PPM combines pavement serviceability evaluation, pavement bearing capacity, and the physico-mechanistic properties of paving materials. The analytical methods which were used to ascertain the physico-mechanistic characteristics, the material fatigue degradation model, and the surface degradation, unevenness in particular, are described. A comparison of the empirical PPM created in the last century used by the national road administrator to this day and the newly created PPM is presented. The comparison shows the difference in the calculated socio-economic benefits and subsequent cost–benefit analysis results. The comparison shows that the use of the old PPM may have produced false economic evaluation results that have led to poor decision making, partially explaining the unsustainable trend of road network management in our country.

Evaluation of WIM data consistency based on temporal axle load spectra

It is crucial to evaluate the consistency in weigh-in-motion (WIM) loading over time and quantify the relative accuracy of axle loads based on axle load spectra (ALS) data for different sensor types. This paper presents the temporal evaluation of the ALS from 51 WIM sites and 128 records available in the long-term pavement performance data. Analysis of ALS data over time shows that for single ALS, there is a significant difference in peak loads between the bending plate, and quartz piezo sensor measurements. Also, 100% of the bending plate 86% of the quartz piezo, and 66% of the piezo cables sites exhibited consistent single axle peak loads after 1 year of calibration event. For tandem ALS, significant differences were observed between loaded peaks of bending plate sensor from both quartz piezo and piezo cable sensors. Also, 83% of the bending plate, 78% of the quartz piezo, and 50% of the piezo cable sites exhibited loaded peaks consistently 1 year after calibration. The results show that calibration frequencies longer than 1 year may be acceptable for the bending plate sensors. However, calibration frequencies of at least 1 year for quartz piezo and less than a year for piezo cable sensors are recommended. Pavement designers and analysts should be aware of the changes in WIM data and calibration frequency over time.

Machine learning based pavement performance prediction for data-driven decision of asphalt pavement overlay

This study is to develop pavement performance models using support vector regression and ensemble machine learning methods for selection of pavement overlay strategy. Predictive models of pavement distresses were developed based on the Long-Term Pavement Performance (LTPP) data and compared using support vector machine, random forest regression, gradient boosting machine, and stacking ensemble. Gradient boosting machine was found to be a more effective method to establish predictive models of rut depth and International Roughness Index. Stacking ensemble and random forest regression would provide reliable prediction of alligator cracking. The models developed based on the clusters of climate and traffic parameters were found to be more effective. Based on the developed performance models, the effects of asphalt overlays on pavement distresses and service lives were investigated. When the overlay with recycled asphalt concrete (AC) was applied, the propagation of alligator cracking was faster compared to the overlay with virgin asphalt mixture. Milling before overlay tended to slow the increase of IRI but fasten the development of rut depth.

Fatigue of Cold Recycled Cement-Treated Pavement Layers: Experimental and Modeling Study

Fatigue is the main design criterion for cold recycled cement-treated mixtures (CRCTMs). However, the literature shows that the fatigue behavior of such mixtures is still not well known. For example, the effect of increasing reclaimed asphalt pavement (RAP) contents is yet a topic of discussion. This experimental and modeling study helps fill knowledge gaps on CRCTM fatigue behavior using long-term curing fatigue tests and three design methods currently being used in different countries. The objectives of this study were: (1) to characterize the mechanical and fatigue behavior of mixtures of RAP, aggregates and cement; (2) to evaluate the fatigue life of pavements with base and subbase layers of such mixtures using the novel Brazilian design method (MeDiNa); and (3) to compare the results with those obtained using the South African Pavement Engineering Manual (SAPEM) transfer functions and the American Association of State Highway and Transportation Officials AASHTOWare Pavement Mechanistic-Empirical Design (PMED) software. The mixtures were tested in the laboratory using flexural static and cyclic tests, and the required parameters to use the methods were obtained. Experimental results and modeling demonstrated a superior fatigue behavior of recycled layers with higher RAP contents. On the other side, layers with lower RAP contents abruptly lost stiffness in short periods, making thicker structures necessary. Therefore, using high RAP contents is not only a sustainable practice, but also a technical benefit. The equivalent single axle loads obtained using the SAPEM were higher than those obtained using MeDiNa, while the PMED ones were higher than both previous methods. Despite the inherent differences, this suggests that MeDiNa is more conservative. It also highlights the importance of calibration based on long-term pavement performance data.

Analysis on Effects of Joint Spacing on the Performance of Jointed Plain Concrete Pavements Based on Long-Term Pavement Performance Database.

With the day-night temperature and moisture levels changing every day, expansion and shrinkage of concrete slabs is always occurring; therefore, joints provide extra room for concrete slab deformation. The joint spacing in jointed plain concrete pavement (JPCP) is continuously affecting long-term pavement behaviors. In this study, data from the Long-Term Pavement Performance (LTPP) program were analyzed, and the behaviors of JPCP with different joint spacings were compared to discover the joint spacing effects. Since LTPP has an enormous database, three representative sections located in different states were selected for analysis, where the variable factors such as temperature, moisture, and average annual daily truck traffic (AADTT) were almost the same between the three sections. Three different joint spacings, including 15 ft (4.5 m), 20 ft (6 m), and 25 ft (7.5 m), were compared based on the collected LTPP data. The involved long-term pavement performances, such as average transverse cracking (count), average JPCP faulting, international roughness index (IRI), and falling weight deflectometer (FWD) deflections were compared between JPCP with different joint spacings. Based on the comparative analysis, the JPCP constructed with a 15 ft joint spacing demonstrated the best long-term performance. It showed no transverse cracking, the lowest average JPCP faulting, the best IRI value, and the smallest FWD deflection during the entire in-service period. With proper joint spacing, the cost of road maintenance throughout the life cycle could be significantly reduced due to there being less distress. Therefore, it is recommended to optimize the joint spacing to about 15 ft in JPCP in future applications.

Survival Analysis for Asphalt Pavement Performance and Assessment of Various Factors Affecting Fatigue Cracking Based on LTPP Data

Pavement performance is the ability of pavement to remain in an acceptable condition to serve the intended users over a period of time. There are several principal, combined factors that affect flexible pavement performance such as environmental conditions, pavement materials, and traffic loads. Vehicle overloading is considered one of the most significant causes of accelerating flexible pavement deterioration, reducing the pavement’s design life, and affecting the overall sustainability of the pavement system. Therefore, researchers are continuously examining pavement systems with a view to finding the most suitable solutions for sustainable development in road construction systems in order to reduce both costs and pollution. In this study, we present a framework to conduct nonparametric and parametric survival analysis for asphalt pavement test sections, to assess the influence of using reclaimed asphalt pavement (RAP) on fatigue service life, to indicate the most significant subset of risk factors (covariates), and to study the effect of overweight axles on flexible pavement performance. All the data concerned were extracted from the long-term pavement performance (LTPP) program. The Kaplan–Meier (KM) survival probability curves of multiple pavement distresses were developed to compare the failure probability for various distresses and to determine the median survival time for each distress. The fatigue survival curves for the test sections using RAP and virgin materials were developed separately and the equality of the two survival curves was tested and affirmed. Several parametric survival analyses were conducted to select the most significant subset of covariates. For fatigue cracking and, after dropping the insignificant predictors, a model was developed to show the quantitative relationship between fatigue failure time and potentially influential factors. The analysis indicated that the increase in the percentage of overloaded axles from 0% to 20% can reduce the fatigue survival life of flexible pavement by up to 55%. In the absence of overweight axles, a one-inch increase in asphalt layer thickness can extend the fatigue service life by about half a year. However, in the presence of 20% of overweight axles, a one-inch increase in thickness can extend the fatigue service life by only 0.22 years. Therefore, additional virgin materials and resources are needed to maintain traffic conditions in the road network and to compensate for the reduction in fatigue service life. Moreover, the effect of the increase in overweight axles from 0% to 15% on reducing the fatigue survival life is found to be similar to the effect of increasing the AADTT tenfold. Therefore, the sustainability of pavement is directly affected by the fatigue survival life.

Forecast of post-overlay flexible pavement roughness progression: a random coefficient linear regression with autocorrelation model

Abstract A pavement roughness progression model is an essential component in a pavement management system. Asphalt overlay is a typical pavement maintenance and rehabilitation (M&R) technique. To schedule asphalt overlay in a timely fashion, the relationship between the asphalt overlay design and post-overlay roughness progression is required. However, due to difficulty in data compilation and model development, the effect of endogenous overlay design and continuous variation of asphalt overlay thickness on post-overlay roughness progression is not documented. This study aims to develop a comprehensive post-overlay flexible pavement roughness model with long-term pavement performance (LTPP) data. The asphalt overlay projects from the LTPP SPS-3, SPS-5, and GPS-6 programs were incorporated for data analysis. A random coefficient linear regression with autocorrelation (RCLRA) model is proposed to simultaneously address endogenous overlay design issue, between-section heterogeneity issues, and within-section serial correlation issues in the post-overlay roughness progression. By addressing the within-section serial correlation, the proposed post-overlay roughness model can reduce the mean absolute percentage error (MAPE) from 21.26 percent to 2.19 percent. The model estimation results provide some new insights into the relationship between post-overlay roughness and asphalt overlay design factors. An endogenous overlay design indicator, continuous variation of asphalt overlay thickness, the relative fatigue cracking area, and severe rutting indicator are first identified to have significant effects on post-overlay roughness progression.

Machine Learning Approach to Predict International Roughness Index Using Long-Term Pavement Performance Data

On-time pavement maintenance and rehabilitation are required to maintain or improve pavement roughness, which is an indicator of pavement performance and road safety. Better prediction of maintenance time can help in budget planning and allocation for highways as well as a better and safer driving experience for drivers. In this research, the International Roughness Index (IRI) for asphalt concrete pavement is predicted based on the 12,637 observations in the Long-Term Pavement Performance (LTPP) data set for 1,390 roads and highways in 50 states of the US and the District of Columbia from 1989 to 2018. To identify the research gaps and to better understand the state-of-the-art research in IRI prediction, a systematic literature review (SLR) has been performed to develop a comprehensive view of machine learning techniques used for IRI prediction. We used a machine learning algorithm that can handle missing data in the LTPP data set. Extreme gradient boosting (XGBoost) was used to predict the IRI. Also, the support vector regression (SVR) and random forest (RF) models were used to compare the results. Our results show that XGBoost provides a better model fit in terms of mean absolute error and coefficient of determination. Moreover, our results show that No.-200-passing, hydraulic conductivity, and equivalent single-axle loads in thousands (KESAL) are the most important factors in predicting the IRI.

Exploratory analysis of LTPP faulting data using statistical techniques

Long-Term Pavement Performance (LTPP) database that stores considerable and free-to-access pavement information provides beneficial resources to researchers to develop transverse joint faulting prediction models that are very useful for joint concrete pavement design, rehabilitation and management. To develop accurate and robust faulting prediction models, the investigation of the LTPP faulting data is a prerequisite. This study conducted an Exploratory Data Analysis (EDA) of LTPP data by performing statistical analysis and graphically displaying the relevant factors and their correlation with faulting. This analysis was conducted based on two parts of the LTPP historical data, i.e., the pre-repair and post-repair faulting. For the pre-repair data, the relevant factors in faulting are classified into four categories, namely, traffic repetition, pavement information, local climate and material properties. To better examine the effect of the relevant factors in faulting, the descriptive statistics of factors were calculated and the grey relational analysis and the simple linear regression with one variable at a time were performed. In the regression testing, the P-value shows the significance of the individual relevant factors but it is likely to contradict the realistic relationships when the spurious correlation occurs. Through a thorough investigation, the study illustrated the rationale of the occurrence of the spurious correlation. For the post-repair data, the LTPP maintenance data were examined to evaluate the effectiveness of the individual maintenance treatment by calculating the faulting reduction after applying the treatment.

A Simplified Mechanistic-Empirical Flexible Pavement Design Method for Moderate to Hot Climate Regions

Flexible pavement structure design is a complex task because of the variability of design input parameters and complex failure mechanisms. Therefore, the aim of this study is to develop and implement a simplified Mechanistic-Empirical (M-E) pavement design method based on the 1993 American Association of State Highway and Transportation Officials (AASHTO), the National Cooperative Highway Research Program (NCHRP) 9-22, and NCHRP 1-37A and 1-40D projects. This simplified methodology is implemented into a computer code and a user-friendly software called “ME-PAVE”. In this methodology, only two equivalent temperatures, as per the NCHRP 9-22 project, are estimated to adjust the dynamic modulus of the asphalt layer(s) for Asphalt Concrete (AC) rutting and AC fatigue cracking prediction instead of using the hourly climatic data, as in the AASHTOWare Pavement ME. In ME-PAVE, the structural responses at critical locations in the pavement structure are determined by a Finite Element Module (FEM), which is verified by a Multi-layer Elastic Analysis (MLEA) program. To ensure that the simplified methodology is practical and accurate, the incorporated transfer functions in the proposed simplified methodology are calibrated based on the Long-Term Pavement Performance (LTPP) data. Based on statistical analyses, the built-in FEM results exhibit very similar trends to those yielded by MLEA, with a coefficient of determination, R2 of 1.0. For all practical purposes, the proposed methodology, despite all simplifications, yields acceptable prediction accuracy with R2 of 0.317 for the rut depth compared to the current practices, NCHRP 1-37A and 1-40D (R2 = 0.399 and 0.577, respectively); while the prediction accuracy for fatigue cracking with R2 of 0.382 is comparable to the NCHRP 1-40D with R2 of 0.275. Nonetheless, the standard error for both distresses is in good agreement based on the investigated data and the developed methodology. Finally, the conducted sensitivity analysis demonstrate that the proposed methodology produces rational pavement performance.

Developing a distress-based traffic equivalency to efficiently evaluate the effect of traffic loads on pavement performance

ABSTRACT Traffic loading is considered one of the main factors contributing to pavement deterioration. Large illegal loads in the form of vehicles traveling with axle loads above the permissible legal limits and/or overall overweight magnify the traffic impact on the performance of pavements. These overloads contribute to the deterioration of pavement sections which leads to substantial maintenance and repair costs. This paper illustrates an example of an approach to determine the effect of both normal and overweight loads on the performance of rigid pavements using the Long-Term Pavement Performance (LTPP) traffic data. The traffic load was evaluated by estimating the legal and illegal ESALs related to both fatigue and erosion damage by visual observation and by field-assessment of the degree of bonding based using the Falling Weight Deflectometer (FWD). The illustrated example utilized LTPP data from nine Jointed Plain Concrete (JPC) pavement sites with different traffic mixes. This study reveals that the damage in JPC pavements could be either (i) visible by the formation of transverse cracking as would be related to fatigue related ESALs or/and (ii) invisible by the reduction of the effective thickness as would be related to erosion ESALs.

Research on Maintenance Management of Flexible Pavement Based on C-LTPP

Based on the Canadian Long-Term Pavement Performance (C-LTPP) data, this paper chooses the road performance related data as variable factors. According to the Dipstick detection results, the characteristic values such as rut depth, wheel track width and cross section area are extracted to describe the rutting. This paper established the relationship between the pavement rutting and the cumulative service time, determined the rutting evaluation index, and proposed the pavement maintenance time threshold. Comparing the flexible layer overlay repair schemes used in various test points, it provides a reference for the pavement performance management decision-making and the road repair scheme design after repair.

Empirical evaluation of utility of anti-frost layer in pavement structure considering regional climate characteristics

ABSTRACT Anti-frost layers in road pavement structures are widely applied to cold areas internationally. They mitigate cracks, potholes and roughness caused by the freeze–thaw phenomenon. Although the anti-frost layer design has been introduced to Korean road networks since the 1990s, its utility has not yet been empirically proven. This study attempts to evaluate the utility of the anti-frost layer with statistical methods using empirical data. As an analytical tool, the Bayesian Markov mixed hazard model for deterioration modelling and comparative analysis was introduced. In empirical studies, 8-year nation-wide network long-term pavement performance data (2316 sections) collected by the Korean Pavement Management System was applied. The analysis results signify that the anti-frost layer is good for prevention of cracks in network levels but is not enough for some cold areas with a freezing index of over 100. To secure the utility of anti-frost layers in the entire road network, it is necessary to revise the current Korean design standard.

Characterization of rutting on asphalt pavement in terms of transverse profile shapes based on LTPP data

This study aims to characterize the transverse profile shapes (TPSs) of rutting on asphalt pavement and to produce useful information to the highway administration in what concerns rutting. A novel framework was developed to implement an accurate description on rutting, which integrates four steps (data acquisition, TPS adjustment, TPS categorization and indicator analysis). Various indexes were analyzed and used to characterize these profiles. A new dimensionality reduction method was constructed based on correlation coefficient matrix (CCM) to avoid the repetition of indexes. These data from the Long-Term Pavement Performance (LTPP) program were collected and screened in the study. The categorization and time-dependent evolution of TPS were conducted based on the developed framework. The statistical analysis and dimensionality reduction of these indexes were carried out. Results show that the established framework allows to categorize and accurately describe the TPS of rutting. The TPSs are divided into five typical shapes and an irregular shape, and the type 5 and the irregular shape are not very valuable. There are different time-dependent evolutions for various types of TPS, which may be associated with trajectory of vehicles. The obtained critical value of these indexes should be attracted an attention from road administrations. The constructed dimensionality reduction method is capable of eliminating the redundancy of indexes, and thus optimizing the characterization effects. Different types of TPS correspond to different index systems. Extensive work on the application of information proposed in this study is still need in predicting pavement performance and conducting maintenance decision-making in further.

Examining the relationship between two road performance indicators: Pavement condition index and international roughness index

Two of the most popular pavement performance indicators are the International Roughness Index (IRI) and the Pavement Condition Index (PCI). The Long-Term Pavement Performance (LTPP) database does not include the latter. Therefore, limited research is available on the relationship between the PCI and IRI based on the LTPP roads. This study aims to cast light on the relationship between these two performance indicators using LTPP data. To this end, 3,954 records of IRI and PCI were collated to determine the correlation. The aggregate goodness of fit was not satisfactory (R2 = 0.31) as the data was collected over 61 different states and provinces and in a 28-years timeline. So, in the next step the data was clustered into more meaningful groups based on location (province/state) and functional class in the hope of improving the goodness of fit. It was observed that the R2 within each group was substantially higher than the aggregate data, with some reaching above 0.70. Preparing an unprecedentedly large dataset gave us the freedom of segmenting the data into smaller and less noisy subsets, which can result in more robust models with higher coefficients of determination. Moreover, another dataset collected by Ontario Ministry of Transportation (MTO) was studied and the results were contrasted against each other. It was observed that the MTO data is more cohesive, and the correlation between the IRI and the PCI was stronger in that dataset. Finally, this study investigated the variations not explained by regression models, i.e. reasons that road sections can have an excellent PCI and poor IRI and vice versa. The findings show that the relationship between the PCI and IRI can vary significantly based on factors such as location, functional class and slope.

A systematic approach to estimate global warming potential from pavement vehicle interaction using Canadian Long-Term Pavement Performance data

Pavement vehicle interaction (PVI) is one of the significant components in the use phase of pavement life cycle assessment. This component has attribution with excess fuel consumption, resulting in an increase in the global warming potential (GWP). Several research have been conducted to model and quantify the PVI effect based on pavement roughness and deflection. These models estimate excess fuel consumption considering traffic loading, engineering properties of pavement materials and pavement design specifications. However, a big question remains unanswered: how do various climatic parameters including precipitation, temperature, and freezing index influence the PVI and subsequent GWP? Canada is the second largest country in the world and different climate regions can be found in each of its ten provinces and three territories. Therefore, a new, climate-based clustering approach—rather than geometric boundaries is introduced for climate impact analysis from road system. Twenty-two Long-Term Pavement Performance (LTPP) road sections located in the Canadian highways have been selected for this study. These road sections were clustered based on homogenous climate conditions, using a statistical approach. The research finds that a combination of high precipitation, freezing index and medium temperature are associated with an accelerated rate of International Roughness Index (IRI) and consequent GWP because of a high PVI for poor pavement surface. GWP for roughness is found to be very low where high temperature and a low freezing index exist. Very high traffic loading and low subgrade stiffness, when combined, produced the highest GWP emissions because of deflection based PVI. It was also found that the PVI effect in terms of GWP for both roughness and deflection is always dominant for heavy vehicles over light vehicles traffic.

A modified pavement condition rating index for flexible pavement evaluation in Egypt

Pavement evaluation is conducted to assess the functional and/or structural condition of existing pavement systems which can be done on project or network levels. Through pavement condition assessment, pavement deterioration models can be established and thus maintenance and rehabilitation alternatives can be proposed. The prediction of pavement performance which is primarily based on the pavement evaluation results is a critical element of a successful pavement management system (PMS). The main objective of this research is to revise and modify the current pavement condition rating (PCR) method for pavement evaluation as a major component of an effective PMS for roads and transport directorates in Egypt. Pavement condition data were collected from literature, General authority of roads, bridges and land transport in Egypt and the long-term pavement performance (LTPP) database. Due to its accuracy, only the LTPP data were used to modify the current PCR method to yield comparable values to the very well-known pavement condition index (PCI). The proposed modified PCR was validated using pavement distress data collected from two rural roads in Egypt, and the data showed reasonable accuracy as compared to the PCI method.

Development of An Artificial Neural Network Model for Asphalt Pavement Deterioration Using LTPP Data

Deterioration models are important and essential part of any Pavement Management System (PMS). These models are used to predict future pavement situation based on existence condition, parameters causing deterioration and implications of various maintenance and rehabilitation policies on pavement. The majority of these models are based on roughness which is one of the most important indices in pavement evaluation. High correlation between International Roughness Index (IRI) and user comfort led to modeling pavement deterioration based on IRI during PMS history. On the other hand, in recent years Artificial Neural Network (ANN) which is a valuable tool of soft computing is used in pavement modeling, widely. This study assessed the development of an ANN pavement deterioration model based on IRI using Back-Propagation Neural Networks (BPNN) technique. The Long-Term Pavement Performance (LTPP) data was extracted from two General Pavement Study (GPS) sections including GPS-1 and GPS-2. After training and testing the developed model, results were compared with a polynomial regression model. Results showed that predicted IRI values with developed ANN model have a good correlation with measured values rather than the polynomial regression model for both GPS-1 and GPS-2 sections.

Comparison of Master Sigmoidal Curve and Markov Chain Techniques for Pavement Performance Prediction

Recently, highway agencies have become in need to enhance their pavement management systems (PMSs) using sound engineering and economic principles that find more prudent solutions to support wise investment choices and preserve the value of infrastructure assets. As pavement performance prediction models are one of the most important and fundamental components in any PMS, this paper focuses on comparing the master sigmoidal curve-based model as a deterministic technique with the Markov chain-based model as a probabilistic technique for the prediction of the international roughness index (IRI) as a pavement performance indicator. The IRI data obtained from the long-term pavement performance (LTPP) program were used to evaluate and compare both methods. In this paper, 44 flexible pavement sections from the GPS-1 experiment incorporating 432 IRI measurements ranging from 0.32 to 5.12 m/km were selected. Results showed that the predictive ability of the investigated models for the selected LTPP data was good with a reasonable coincidence ratio of about 65 percent between the predicted results of the two modeling techniques.