Investigation of pavement raveling performance using smartphone

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ScienceDirect International Journal of Pavement Research and Technology xxx (2017) xxx–xxx www.elsevier.com/locate/IJPRT

Investigation of pavement raveling performance using smartphone Aidin Massahi a, Hesham Ali b, Farshad Koohifar c, Mohamadtaqi Baqersad a,⇑, Mojtaba Mohammadafzali d a b

Department of Civil and Environmental Engineering, Florida International University, 10555 West Flagler Street, EC 3715, Miami, FL 33174, United States Department of Civil and Environmental Engineering, Florida International University, 10555 West Flagler Street, EC 3605, Miami, FL 33174, United States c Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695, United States d Department of Civil and Environmental Engineering, Florida International University, 10555 West Flagler Street, EC 3660, United States Received 14 July 2017; received in revised form 21 September 2017; accepted 27 November 2017

Abstract Raveling is one of the critical modes of failure in open graded asphalt mixtures. The Florida Department of Transportation (FDOT) Districts 4 and 6, located in southeastern Florida, have experienced a large amount of premature raveling with Open Graded Friction Course mixes (OGFC) compared to the other districts in Florida. To investigate the reason of raveling occurrence, the research team devised a data collection tool for smartphones and developed software to measure the raveling area and its location and severity in video images using smartphone GPS capability. The data used in this study were extracted from different FDOT database systems, such as the Laboratory Information Management System (LIMS) and the Electronic Data Management System (EDMS), as well as from the National Oceanic and Atmospheric Administration (NOAA). Ten projects were surveyed by the research team, and numerical raveling statistics were developed and compared with qualitative raveling ratings provided by FDOT. Good correlation was found between the two methods. The comparison was used to establish thresholds of good performance. Such thresholds can be used to develop performance specifications or warranty benchmarks. The relative effects of mix design, construction, and environmental factors were also studied. A project level of analysis was conducted. Several hypotheses were evaluated to determine the cause of premature raveling. Data analysis results indicate significant correlations between raveling and ambient temperature, mix temperature, mix spread rate, and gradation. In light of this study, recommendations were made to enhance the longevity of the OGFC mixtures. Ó 2017 Chinese Society of Pavement Engineering. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Ambient temperature; Mix design; Pavement performance; Pavement distress; Asphalt binder

1. Introduction Pavement or road surface is the persistent apparent component of a roadway or walkway that provides the traffic surface for vehicular or pedestrians. Asphalt and ⇑ Corresponding author.

E-mail addresses: amass025@fiu.edu (A. Massahi), heaali@fiu.edu (H. Ali), [email protected] (F. Koohifar), mbaqe001@fiu.edu (M. Baqersad), mmoha020@fiu.edu (M. Mohammadafzali). Peer review under responsibility of Chinese Society of Pavement Engineering.

concrete are the most frequent pavement materials used in recent decades [1–3]. The quality of constructed substructure and pavement during its design life is essential for pavement performance. Previous studies showed that poor substructure conditions may result in considerable increase in life cycle cost [4–6]. As a result, pavement quality and distress must be measured and evaluated during serviceability time to improve pavement performance. Undesirable pavement performance, as a civil infrastructure system, is very difficult to be reversed [7,8]. Due to that, the pavement distress such as raveling, cracking,

https://doi.org/10.1016/j.ijprt.2017.11.007 1996-6814/Ó 2017 Chinese Society of Pavement Engineering. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Please cite this article in press as: A. Massahi et al., Investigation of pavement raveling performance using smartphone, Int. J. Pavement Res. Technol. (2017), https://doi.org/10.1016/j.ijprt.2017.11.007

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and rutting should be measured and detected [9]. Pavement raveling is one of the several substantial pavement distresses that can lead to safety issues and causes frequent road maintenance. Pavement raveling is the loss of aggregate particles from the asphalt surface during the service life of pavement [10,11]. There are different methods to evaluate pavement distresses and raveling. Some use the deduct value approach for various distresses to assess pavement condition [12]. In the deduct value approach, raters use their judgment to determine the severity and extent of raveling. The Florida Department of transportation (FDOT) Flexible Pavement Condition Survey Handbook [13] defined light raveling severity as ‘‘aggregates and binder have begun to wear away but have not progressed significantly, with some loss of aggregate.” Whereas moderate raveling severity was demonstrated as ‘‘aggregate and binder have worn away and the surface texture is moderately rough and pitted.” Severe raveling severity is defined as ‘‘aggregate and binder have begun to wear away and surface texture is very rough and pitted, loss of aggregate very noticeable”. Similar definitions can be found in the Federal Highway Administration’s (FHWA’s) Distress Identification Manual [14]. However, this rating method has a degree of subjectivity in determining raveling based on raters’ judgments [12]. Florida uses open graded friction course (OGFC) in multilane facilities with a design speed limit greater than or equal to 50 miles per hour. The OGFC asphalt has a high void content (approximately 18–22%) and is designed to minimize hydroplaning and reduce splash and spray. However, the OGFC has more durability problems than dense graded mixes [15]. Districts 4 and 6 of the FDOT, which encompass southeast Florida, have had occasional performance problems with the FC-5 open graded friction courses that used oolitic limestone [16–18]. In some cases, the FC-5 layer would start to ravel prematurely within three to five years after construction. This performance contrasts with other areas of the state where premature raveling is quite rare, although the use of oolitic limestone outside of Districts 4 and 6 is not as common. Many factors influence raveling; however, the contribution of each factor is still unknown [11,19]. Inadequate compaction of pavement and its air void percentage, pavement placement in wet weather, pavement mix design and aggregate gradation, losing binder cohesive or adhesive capacity due to binder aging, and ambient condition of asphalt are identified as the main factors that promote pavement raveling [20-24]. Pan and Huang [25] indicated that coarse aggregate volume fraction plays a significant role in mixture fracture behaviors. In addition, the mesosimulation indicated that raveling occurs in a wide range of temperatures, especially in high and low temperatures [26–27]. One of the greatest concerns regarding the raveling of pavement is safety. One particular safety concern is fragments of pavement aggregate flying into the air due

to raveling and causing damage to vehicles [28]. Moreover, raveling can result in rough and uneven surfaces for pavement, which can increase road-tier noise and reduce driver safety [29]. Raveling also generates areas on the highway where water accumulates during wet weather, which increases the chances of hydroplaning [11]. Also, the accumulated water can penetrate into the pavement surface and cause swelling of underground soil [30]. The consequences of raveling reduce the pavement’s design life. Thus, detecting the distress and pavement raveling and then sealing and treating it on time can improve pavement safety, as well as increase pavement life [31]. In recent years, digital cameras have been gaining more attention as a rich source of information, and optical measurements and image sensing techniques were applied on several mechanical and civil applications [32–34]. Pavement raveling can be also detected by analyzing 5 video streams. Vision-based techniques have been wide used for surface condition monitoring [35–38]. In order to investigate the cause of raveling, this study used a smartphone with a high resolution camera and GPS capability to determine the extent and causes of raveling. Using smartphone in transportation studies is becoming more and more popular [39,40]. The study performed the statistical analysis to find the relationship between pavement raveling with construction conditions, ambient weather and gradation of asphalt mix. Experienced experts’ judgment is the most common method used to evaluate pavement raveling conditions. However, this assessment method is not a type of quantitative method that evaluates pavement conditions. Therefore, a quantitative method with a threshold for defining ‘‘Good” and ‘‘Bad” raveling performance was proposed. Although the proposed method in this study has some degree of subjectivity to determine the area and degree of raveling that depends on experts’ judgment, this method can estimate the raveling area more precise than conventional visual inspection method. Furthermore, FDOT considers three well-known hypotheses to explain early failures. To achieve the objectives of this study, the following hypotheses were considered. Hypothesis #1: The gradation of the limestone mixes is slightly coarser than the gradation of the granite mixes; consequently, the thickness at which it is placed (70 lbs/sy) impacts the texture adversely, resulting in raveling. Hypothesis #2: The urbanized nature of paving in south Florida results in longer haul times, piecemeal construction, and in some cases, poor construction practices and poor oversight. Hypothesis #3: The absorptive nature of limestone results in lower effective binder contents and more durability problems. 2. Data acquisition The data received from FDOT included ten projects; six projects were rated as good performance, and four as poor performance from the raveling perspective. The

Please cite this article in press as: A. Massahi et al., Investigation of pavement raveling performance using smartphone, Int. J. Pavement Res. Technol. (2017), https://doi.org/10.1016/j.ijprt.2017.11.007

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performance rating was based on Pavement Condition Survey data, as well as windshield surveys conducted by FDOT personnel. These correspond to approximately 500 lane-miles of pavement. The survey was conducted by FDOT experts and rating the asphalt pavement condition between zero and ten from raveling perspective. Then, the pavement was categorized as Good or Bad. A software application written in Java script was also developed to measure the extent of raveling. The software was installed in a high resolution smartphone camera that was mounted on the windshield of a van to videotape the roads at the speed of traffic. Each lane was recorded individually and reviewed later using a computer screen. Additionally, another software application was developed in MATLAB to allow the user to pause the video, select the appropriate speed for the video, mark the raveling area on the screen, and compile a table consisting of the raveling area (in square feet), location, and severity of raveling. All area computations were programmed in the software and used the lane width to scale the image. It should be noted that the longitude and latitude were determined using GPS data [41], which allows the analysis of raveling to occur at a high level of detail on segment and lot levels. 3. Methodology In order to investigate the cause of raveling, a smartphone with a high resolution camera and GPS capability to determine the extent of raveling was used. The developed Android application captured consecutive pictures and stored them along associated GPS locations. These data were transferred to a PC, running the post processing software. The software was capable of measuring the raveling area, as well as determining the severity and location of raveling using GPS data and user inputs [42]. As stated earlier, there were two methods used to rate raveling; one was qualitative, performed by FDOT based on visual windshield surveys and the Pavement Condition Survey (PCS). This method yielded a classification of each project as good or bad. The second method was quantitative, performed by the research team using cameras mounted on a van to videotape each lane. To evaluate the consistency between the two methods, the data were separated into two groups, either Good or Bad, as rated by FDOT. Since the Student t-test is the most commonly used method to evaluate the difference in means [43], it was used to compare the mean of Good and Bad pavement raveling performance. Then, the thresholds were developed for the purpose of defining acceptable and unacceptable raveling levels. The threshold is the average distance between the mean of the annual percentage of Good and Bad raveling performance cases.

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many key quantitative measurements, such as total area of damaged surface and the location of each defect. To address the limitations of this traditional method, several studies have used computer vision techniques combined with mathematical approaches, such as fractal analysis to automatically quantify the properties of surface defects [35–37]. Specifically, in this research, the research team automated the process using two software packages. The first software package ran on an Android Galaxy S4 smartphone and captured consecutive images while logging the GPS location of each frame. These images are stored on the smartphone’s SD card and can later be transferred to a PC to be further analyzed. This software is called ‘‘data collection software.” Fig. 1 shows a sample frame used to measure the area of the raveling and identify the raveling segment location. The second software package ran on a Windows operating system and took the frames and GPS tags from the smartphone and allowed the user to mark the boundaries of the defected areas. Then, it generated an automated Excel result spreadsheet containing the total number of defects and affected area, severity, images, and GPS location. This software was called ‘‘analysis software.” The output document can be used to pinpoint each of the defects, and other users can further investigate each of the defects without the need to examine all of the images. The principles of the area measurement method used in the analysis software rely on extracting the actual surface area between the points that are marked by the user. The user marked the area of defect on the image using a polygon. The software translated each of the nodes of the selected polygon from frame coordinates to real world coordinates relative to the camera. Afterward, using the real world coordinates, the real world surface of the defect was calculated. Fig. 2 illustrates the setup. In what follows, the image coordinates (pixel coordinates) translation to real world coordinates was described. The points on the

3.1. Software description Visual inspection is the traditional method used for fast and qualitative assessment. However, this method lacks

Fig. 1. Sample frame saved by software to quantify the raveling.

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image was defined with P and corresponding point in real world with x. Let x0 represent the real world coordinate of the nearest point of pavement that can be seen in a frame and mark its corresponding pixel coordinate with P0. Similarly, let x1 and xd be the horizon and a defect point, and P1 and Pd be their corresponding pixels. Any pixel in between P0 and P1 represents a point on the pavement, and any pixel after P1 can be discarded since it corresponds with the point above the horizon. The camera measures the difference between P0 and Pd (Dp), while the distance on the pavement is represented by Dx. There are several factors that affect the relationship between Dp and Dx that depend on the camera’s optics (namely field of view) and the physical orientation and position of the camera. These parameters are measured and used to calibrate the analysis software, but before utilizing the calibration methods, it is necessary to derive the parametric relationship between Dp and Dx. Let a be the field of view of the camera, and b represent the orientation of the camera with respect to the pavement. A parallel line is drawn from x0, and since this line was drawn parallel to the camera, the OP0Pd triangle is similar to ox0a. Therefore, the ratio of corresponding edges is defined as: k¼

jp0 pd j jx0 aj

ð1Þ

It can now be written as: jDxj ¼ jx0 hp j þ jhp xd j jx0 hp j ¼ jx0 aj  cosðbÞ ¼

ð2Þ jp0 pd j  cosðbÞ k

ð3Þ

jhp xd j ¼ jahp j  tanðp  c  bÞ ¼ jx0 aj  sinðbÞ  tanðp  c  bÞ jp0 pd j  sinðbÞ  tanðp  c  bÞ k jp p j jDxj ¼ 0 d ½cosðbÞ þ sinðbÞ  tanðp  c  bÞ k ¼

ð4Þ ð5Þ

c can be expressed in terms of the known parameters:


c ¼ tan1

pmax p0 2 pmax p0  2

tanðaÞ ðpd  p0 Þ

 ð6Þ

Therefore: jDxj ¼

jp0 pd j ½cosðbÞ þ sinðbÞ k
tanðp  tan

1

pmax p0 2 pmax p0  2

  tanðaÞ  bÞ ðpd  p0 Þ

ð7Þ

According to the above equation, it is necessary to know k, a, b and Pmax-P0 to find Dx for each Dp. These parameters allowed the software to calibrate easily so that it could perform the mapping of image pixels to the distance on the pavement. Finding k follows the exact definition of k. A ruler with a known length is placed parallel to the camera’s x0, and the pixels that represent it in the image are noted. To find a, the camera’s field of view was measured; this process was done by placing the camera in a known distance from a ruler, whereupon the ruler’s length was captured in the frame. Then, using the field of view, a was found. Measuring b was done by measuring the angle of the camera with respect to the horizon. Several applications on the smartphone have the ability to use an onboard accelerometer to measure the smartphone’s orientation with respect to the horizon. Pmax-P0 is the amount of pixels in the vertical axis of the phone, which can be specified in the software. The software requests an Android hardware driver to use that specified resolution when capturing the frames. Based on the research team’s experience, a resolution of 1080  810 was used. Note that the pictures were in two dimensions, but the third dimension of each picture followed exactly the same geometry. After mapping the points in each frame back to a distance on the pavement, the methodology used the following well-known formula to calculate the area of a nonself-intersecting (simple) polygon with n vertices:    n1 1 X  ð8Þ A ¼  ðxi y iþ1  xiþ1 y i Þ  2  i¼0 Note that in order to close the polygon; the 0th point must be the same as the nth point.

Fig. 2. Schematic of image scaling and variables needed to scale image. Please cite this article in press as: A. Massahi et al., Investigation of pavement raveling performance using smartphone, Int. J. Pavement Res. Technol. (2017), https://doi.org/10.1016/j.ijprt.2017.11.007

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3.2. Factors affecting raveling In order to find the factors influence raveling, it is necessary to recognize the potential design, construction and ambient factors that may influence raveling [44]. These factors are classified into four categories: mix design variables, mix property variables, construction condition variables, and ambient variables. Mix design data include gradation, binder content and type, and mix temperature. Production data consist of the date that the sample was taken, the sample level, gradation, and Asphalt Content (AC). The ambient condition data contain temperature and precipitation of the paving of a day. The temperature data include minimum, maximum, and average ambient temperatures on the day of paving with accuracy to the nearest 0.1 °C. It should be noted that the precipitation was measured to the nearest 0.1 mm. The placement data were extracted from the FDOT Electronic Document Management System (EDMS) [45]. Mix design and production data also were provided by FDOT’s Material Office. The ambient data were obtained from the National Oceanic and Atmospheric Administration (NOAA) website [46].

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raveling spread over several locations may be perceived worse than severe raveling located in only one spot. 4. Percent of total segments with low, moderate, severe, patched and total raveling.

3.4. Annualized raveling data The case study projects had age variety; some projects were older than others. Since the age of the mix is critical in raveling performance, two sets of parameters were defined by dividing the raveling statistical dependent variables to the project’s age in years [47]. There was a statistical difference in the age of Bad versus Good projects. Bad projects had an average age of 7.8 years and Good projects had an average age of 3.7 years. Accordingly, it was assumed that the plots of raveling versus age showed a substantially linear trend. The first statistical set includes four statistical parameters that were developed by dividing the annual number of the raveling segments by the total number of segments for the specific lane. The second statistical set was developed by dividing the annual area of raveling by the total area of the specific segments in percentage. These sets are listed, as follows:

3.3. Data compiling Since the data were extracted from different sources, the data have different structures. Thus, manipulating and compiling the data were critical in this research. This study used the database SQL script to compile the data. The final independent database variables contained the construction, mix design, gradation, and ambient conditions, whereas the dependent variables of the road raveling were provided by the designed software. Furthermore, several statistics were developed to compile data for each lane and each project, including: 1. Area (SF) in the whole lane with low, moderate, severe, patched and total raveling. The research team used captured videotape to determine the area and severity of raveling to classify lane with low, moderate, severe, patched and total raveling, 2. Percent area of the lane with low, moderate, severe, patched and total raveling. It is noted that when raveling attained unacceptable levels, the FDOT maintenance staff removed and patched the bad spots (a minimum of 100-ft. long patches). As such, the ‘‘total raveling” statistics include the areas of low, moderate and severe raveling, in addition to the patched areas. 3. The lane was then divided into 150-ft. long segments, and statistics were recorded of the number of segments with low, moderate, severe, patched and total raveling. This provides a variable that addressed the user’s perspective. A user may perceive a larger raveling problem if the frequency of raveling segments increased, regardless of the extent. For instance, a 100-square-foot low

1. 2. 3. 4. 5. 6. 7. 8.

Annual Annual Annual Annual Annual Annual Annual Annual

percentage percentage percentage percentage percentage percentage percentage percentage

of of of of of of of of

low raveling segments moderate raveling segments severe raveling segments raveling segments low raveling area moderate raveling area severe raveling area raveling area

It should be noted that the annual percentage of raveling segments and annual percentage of raveling area include patches and raveling, regardless of severity. The Pearson correlation was conducted to investigate the correlation between the dependent variables (annualized raveling data) and the independent variables. The correlation coefficient (R) represents the slope of the best fit line of the data points relating the two variables. A correlation coefficient of 1.0 indicates that the variables are directly proportional at an angle of 45°. A correlation coefficient of 0.0 indicates that the line representing the relationship between the two variables is horizontal, and there is no dependency between the two variables. In addition to the value of the correlation coefficient, the p-value was computed. 4. Results 4.1. Comparison between the FDOT and proposed methods of evaluating raveling The mean of Good and Bad raveling performance of pavement was evaluated by the t-test with a 95%

Please cite this article in press as: A. Massahi et al., Investigation of pavement raveling performance using smartphone, Int. J. Pavement Res. Technol. (2017), https://doi.org/10.1016/j.ijprt.2017.11.007

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confidence level. Test results show the p-value for all of the dependent variables less than 5% (Table 1). This indicates that the difference between the mean of Good and Bad pavement raveling performance is significant. It also shows that all raveling-dependent variables have higher mean for Bad projects than Good projects. To classify Good and Bad raveling performance, the average distance between the mean of the annual percentage of Good and Bad raveling performance cases was used to define the threshold of each dependent variable computed in Table 1. To better depict the variability of measured raveling, the annual percentage of raveling segments and annual percentage of raveling area variables are shown in Fig. 3. Fig. 3 shows the mean annual percentage of raveling segments in Bad cases (3.19), which is more than three times the mean annual percentage of raveling segments in Good projects (1.07). In addition, the mean annual percentage of raveling area for Bad performance cases is more than three times the Good projects. The threshold lines were drawn to classify the Good and Bad ranges of the annual percentages of raveling. The threshold values can be used to determine acceptable performance levels, such as those used for warranty specifications. For instance, if a 3-year warranty is desired, the pavement should not have more than 6.39% raveled segments (3 years * 2.13 (annual percent segments raveled) = 6.39%). Linear growth in raveling with age was assumed to determine the acceptable performance level. 4.2. Project-level bivariate correlation analysis In this section, bivariate graphs and correlation coefficients were produced to evaluate the relationship between mix, construction and ambient variables versus raveling statistics. In Table 2, significant variables shown in red, correspond to p-values less than 5%. 4.2.1. Effect of ambient condition on raveling The ambient condition database file includes the ambient average daily temperature and the average daily precipitation. The ambient variables matrix, which shows the coefficients of the correlation of all pairs of dependent and independent variables, is displayed in Table 2.

Fig. 3. Box and whisker plots for (a) the annual percentage of raveling area, and (b) the annual percentage of raveling segments.

The correlation coefficients between annualized statistical variables and daily average of precipitation are small (closer to 0 than 1.0), indicating a low dependency between precipitation and raveling performance. Table 2 shows statistically significant positive coefficients between all annualized raveling statistical variables and the average ambient temperature. This is due to an incident of high raveling at a high ambient temperature (more than 90°F). Fig. 4 also shows a plot of the annual percentage of raveled area

Table 1 T-test results that compare Bad and Good raveling performance and suggested thresholds for Good raveling performance for dependent variables. Raveling severity level

Mean bad

Mean good

Threshold (Max)

t-value

p-value

Annual Percentage Segments with Low Ravel Annual Percentage Segments with Moderate Ravel Annual Percentage Segments with Severe Ravel Annual Percentage Segments with Patched Annual percentage Segments with Ravel Low Ravel Area Annual Percentage Moderate Ravel Area Annual percentage Severe Ravel Area Annual percentage Patched Area Annual percentage Ravel Area Annual percentage Ravel

1.861 0.608 0.498 0.227 3.194 0.040 0.024 0.012 0.004 0.063

1.377 0.269 0.096 0.011 1.069 0.013 0.001 0.004 0.001 0.015

1.619 0.439 0.297 0.119 2.132 0.026 0.012 0.008 0.002 0.039

7.036 8.878 12.379 17.957 16.459 25.653 30.209 8.257 8.195 20.062

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

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Table 2 Ambient variable correlation matrix. Variable Annual Annual Annual Annual Annual Annual Annual Annual a b

percentage percentage percentage percentage percentage percentage percentage percentage

of of of of of of of of

low raveling segments moderate raveling segments severe raveling segments raveling segments low raveling area moderate raveling area severe raveling area raveling area

Mean

Std. Dev.

PRCP (mm)a

TAVG (F)b

1.500 0.361 0.206 1.662 0.021 0.008 0.006 0.028

0.723 0.408 0.365 1.589 0.017 0.013 0.010 0.032

0.104 0.033 0.081 0.138 0.101 0.072 0.055 0.112

0.433 0.580 0.592 0.509 0.410 0.548 0.575 0.458

Daily average of precipitation (PRCP). Daily average ambient temperature (TAVG).

versus average ambient temperature. The lower portion shows a nonlinear U-shape fit trend. Higher raveling occurred at temperatures lower than 65°F and higher than 90°F. This trend should be further verified with a larger data sample. It should be noted that other researchers (e.g., Mo et al. [26]) observed raveling that increased with higher temperatures. They justified that raveling in high temperatures is related to confining stress due to deflection. For the most part, FDOT Specification 337 does not allow paving FC-5 at an ambient temperature less than 65°F. Some readings in the data were below 65°F. This does not necessarily mean that the paving took place at the average daily temperature. According to the top portion of the graph in Fig. 4, paving at a low temperature did not coin-

cide with significant raveling, as shown by the percentage of segments raveled. When the percentage of the raveled area of the scatterplot was fitted with a nonlinear trend line, it showed the U-shaped trend that was discussed above. It indicates that raveling is temperature-dependent process, but temperature is not the only reason of raveling. 4.2.2. Effect of placement parameters on raveling Placement parameters include the mix temperature, tack temperature, and mix spread rate. The placement statistical correlation matrix, which shows the coefficients of correlation of all pairs of raveling variables such as dependent variables, placement parameters, and independent variables is shown in Table 3. Since the tack temperature was

Fig. 4. The annual percentage of raveling segments and raveling area vs. the average daily temperature. Please cite this article in press as: A. Massahi et al., Investigation of pavement raveling performance using smartphone, Int. J. Pavement Res. Technol. (2017), https://doi.org/10.1016/j.ijprt.2017.11.007

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A. Massahi et al. / International Journal of Pavement Research and Technology xxx (2017) xxx–xxx

constant (150°F) for all samples, it was taken out of the analysis, as it has no variability and could not be analyzed. The following observations can be made from the correlation analysis. Pavement placement data were limited, as not all of the projects had data available. As such, the correlation matrix and the scatterplots did not show any legible trends between tack spread rate and raveling. Tack spread rate values ranged between 0.026 and 0.046 Gal/SY. According to FDOT Specs (300), the target is 0.045 Gal/SY, and the tolerance is +/0.01 Gal/SY. The acceptable range is 0.035 to 0.055. The observed range was lower than the acceptable range, and some readings were lower than the acceptable minimum amount of 0.035 Gal/SY. Nevertheless, the rate of raveling did not seem to correlate with tack spread rate values. There is a statistically significant positive correlation between the mix spread rate and raveling rate. This would result from fitting a line in the data. In reality, the FC-5 mix is designed to be placed as one aggregate layer thick. The mix is placed at a target rate based on the specific gravity of the aggregate with limestone mixes placed closer to 70 lb/sy and granite mixes closer to 80 lbs/sy. One expects that deviation in either direction from the target would produce more raveling. The data and the nonlinear trend line conform to the expected rational trend. A spread rate more than 90 lb/SY and less than 50 lb/SY corresponds to a higher raveling rate. The mix spread rate near the design value corresponds to less raveling. Furthermore, the correlation matrix shows a negative and statistically significant relationship between raveling and mix temperature. This is consistent with expectations that the hotter the mix, the easier it is to place and compact, and the less prone to raveling. It is noted that all temperature data were in the normal acceptable range, and were typically in the range between 310°F and 340°F. 4.2.3. Effect of mix properties on raveling The mix properties data were obtained during production by means of quality control and verification sampling and testing. Tested data included gradation and AC, which were used for acceptance and computation of pay. Data analyses show that for the most mix properties, the

coefficients are low and insignificant, indicating a lack of correlation with raveling (Table 4). However, the results show that finer aggregates and AC are more significant than coarser aggregates. The analysis results contained a lot of scatter and supports. The null hypothesis from the scatters assumed that there was no correlation between the two variables. The sixth column in the correlation matrix (Table 4) shows the correlation coefficients between raveling statistics in the first column, and the percentage passing through the 3/800 sieve. There is some reasonable correlation marked by a low correlation coefficient, but is statistically significant. For most raveling statistics, the coefficient is negative, indicating that the finer material correlates with less raveling. This is probably due to some high raveling observed with a coarser mix. The last column in the correlation matrix (Table 4) shows the correlation coefficients between raveling statistics in the first column, and percent of asphalt content. For the most part, the coefficients are low, indicating a weak correlation with raveling. The correlation coefficient was statistically significant for 7 of the 8 raveling measures. The majority of the coefficients are positive, indicating that a higher raveling correlates with higher AC. This is probably due to the fact that the project with the most raveling had a higher AC (close to 7%). 5. Conclusion and summary of observed trend This research effort aimed to investigate the raveling of FC-5 mixes in south Florida. In order to achieve this purpose, a data collection tool that utilized a high resolution camera mounted on a vehicle was designed, and then a package of software was developed to measure the raveling area and determine the severity and location of raveling using GPS data. The construction and performance data received from FDOT included ten projects; six were rated as having good performance, and four as having poor performance from the raveling perspective. Several statistical analyses were performed at project level. The statistical analyses included the Student t-test, multivariate correlation analysis, and box and Whisker plots. The following is a summary of the analysis results.

Table 3 Correlation coefficients or raveling vs. placement variables. Variable Annual Annual Annual Annual Annual Annual Annual Annual

percentage percentage percentage percentage percentage percentage percentage percentage

of of of of of of of of

low raveling segments moderate raveling segments severe raveling segments raveling segments low raveling area moderate raveling area severe raveling area raveling area

Means

Std. Dev

Tack spread rate (Gal/SY)

Mix spread rate (LB/SY)

Mix temperature

1.7713 0.6527 0.3746 1.6106 0.0136 0.0060 0.0130 0.0264

0.846319 0.457687 0.489234 2.305845 0.016322 0.012293 0.011990 0.043275

0.063688 0.063688 0.063688 0.063688 0.063688 0.063688 0.063688 0.063688

0.5998 0.6232 0.5406 0.4825 0.4678 0.7637 0.6008 0.4761

0.451 0.451 0.451 0.451 0.451 0.451 0.451 0.451

The correlation matrix in Table 3 shows that the mix spread rate correlates with raveling. Please cite this article in press as: A. Massahi et al., Investigation of pavement raveling performance using smartphone, Int. J. Pavement Res. Technol. (2017), https://doi.org/10.1016/j.ijprt.2017.11.007

0.35 0.02 0.32 0.37 0.22 0.21 0.07 0.01 0.02 0.030 0.03

0.11

0.07

0.13

0.20

0.18

0.20

0.13 0.32 0.54 0.50 0.49 0.51 0.25 0.06 0.03 0.007 0.01

0.11

0.01

0.13

0.00

0.50

0.04

0.20 0.08 0.12 0.42 0.03 0.01 0.16 0.03 0.06 0.008 0.01

0.18

0.013

0.03

0.14

0.03

0.12

0.23 0.19 0.277 0.28 0.18 0.16 0.06

0.11 0.04 0.07 0.021 0.02

0.00

0.03

0.27

0.12

0.29

0.23 0.00 0.28 0.48 0.20 0.18 0.21

0.20 0.02 0.03 1.733 1.67

0.03

0.09

0.05

0.15

0.01

0.21 0.24 0.50 0.50 0.53 0.55 0.18 0.10 0.02 0.02 0.221 0.39

0.03

0.06

0.00

0.56

0.04

0.10 0.36 0.55 0.48 0.53 0.55 0.17 0.03 0.06 0.03 0.375 0.43

0.00

0.14

0.05

0.55

0.02

0.06 0.18 0.15 0.47 0.14 0.03 0.17 0.19 0.50 0.36

Annual % of low raveling segments Annual % of moderate raveling segments Annual % of severe raveling segments Annual % of Raveling segments Annual % of low raveling area Annual % of moderate raveling area Annual% of sever raveling area Annual % of Raveling area

0.03 0.02 1.528 0.75

0.25

0.05

0.06

0.6 mm (#30) 1.19 mm (#16) 4.75 mm 2.38 mm 2 mm (#4) (#8) (#10) 9.5 mm (3/800 ) 12.7 mm (1/200 ) 25 mm 19 mm (100 ) (3/400 ) Mean Std. Dev.

Correlations (all data in Annual statistic) Marked correlations are significant at p < 0.05000 Variable

Table 4 Correlation coefficients between raveling and mix properties.

420 mm (#40)

300 mm (#50)

177 mm (#80)

150 mm (#100)

75 mm (#200)

AC%

A. Massahi et al. / International Journal of Pavement Research and Technology xxx (2017) xxx–xxx

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5.1. Good fit between qualitative and quantitative raveling surveys The quantitative method was performed by the research team using cameras mounted on a van. The Student t-test showed a very good match between the FDOT and proposed method. The data were divided into two groups, Good versus Bad raveling performance. The t-test showed that all raveling statistics were significantly different in the two groups, with a higher amount of raveling in the Bad projects. The comparison above afforded the opportunity to establish thresholds for Good raveling performance. The thresholds were established for each raveling statistic as the mid-point between the means of the Good and Bad groups. These thresholds can potentially be used in warranty specifications. However, further research needs to implement the warranty specification and determine more solid threshold. Because warranty specification tested in this study only for ten projects and the capability of defining the warranty specification should be tested in further projects with different environmental and construction conditions, and mix design. 5.2. Project-level correlation analysis identified variables significantly correlated with raveling There seems to be some correlation between average ambient temperature and raveling. A nonlinear relation seems to exist with more raveling at an ambient temperature below 65°F and above 90°F. Pavement placement data from Roadway Reports were limited; not all projects contained data. Tack spread rate values ranged between 0.026 and 0.046 Gal/SY. Most data were below the Spec Target of 0.045 Gal/SY. No clear trends were observed between the tack spread rate and raveling. There is a statistically significant positive correlation between the mix spread rate and raveling rate. The asphalt was placed at a target rate of 70–80 lb/SY. The data and the trend line conformed to the expected rational trend. Spread rates more than 90 lb/SY and less than 50 lb/SY corresponded with a higher raveling rate. There is a negative and statistically significant relationship between raveling and mix temperature. This is consistent with expectations that the hotter the mix, the easier it is to place and compact (seat), and it becomes less prone to raveling. All temperature data were in the normal acceptable range and ranged between 310°F and 340°F. 5.3. Evaluation of raveling hypotheses As previously stated, one of the objectives of this paper is to examine relevant, well-known hypotheses to explain early raveling in southeast Florida, as listed below: Hypothesis #1: The gradation of the limestone mixes is slightly coarser than the gradation of the granite mixes.

Please cite this article in press as: A. Massahi et al., Investigation of pavement raveling performance using smartphone, Int. J. Pavement Res. Technol. (2017), https://doi.org/10.1016/j.ijprt.2017.11.007

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A. Massahi et al. / International Journal of Pavement Research and Technology xxx (2017) xxx–xxx

Consequently, the thickness at which it is placed (70 lbs/sy) impacts the texture adversely, resulting in raveling. Coarser sieves (3/400 , 1/200 and 3/800 ) mostly showed a negative and statistically significant correlation coefficient between raveling and percentage of passing. This means that coarser mixes are likely to increase raveling. This hypothesis is supported by the analysis. Most experts who were surveyed supported this hypothesis. Hypothesis #2: The urbanized nature of paving in south Florida results in longer haul times, piecemeal construction, and in some cases, poor construction practices and poor oversight. There was not a sufficient amount of data to evaluate oversight quality and phasing of work. However, the research team observed a significant negative correlation between the mix placement temperature and raveling in project-level analyses. This supports the idea that longer haul times are more likely to cause a lower mix temperature, which is associated with increased raveling. Hypothesis #3: The absorptive nature of limestone results in lower effective binder contents and correspondingly more durability problems. The research team did not observe a negative correlation coefficient between AC content and raveling. In the mix design, we account for asphalt absorption and increase the asphalt content accordingly. It is noted that two out of ten experts surveyed supported this hypothesis. Acknowledgments The work presented herein was the result of a team effort. The authors would like to acknowledge FDOT’s State Materials Office and the District 4 and 6 Materials Office for their assistance with the data collection effort and technical advice. Disclaimer The content of this paper reflects the views of the authors, who are solely responsible for the facts and accuracy of the data, as well as for the opinions, findings, and conclusions presented herein. The contents do not necessarily reflect the official views or policies of the Florida Department of Transportation. This report does not constitute a standard, specification, or regulation. References [1] M. Baqersad, E.A. Sayyafi, H.M. Bak, State of the art: mechanical properties of ultra-high performance concrete, Civil Eng. J. 3 (2017). [2] M. Shafieifar, M. Farzad, A. Azizinamini, Experimental and numerical study on mechanical properties of Ultra High Performance Concrete (UHPC), Constr. Build. Mater. 156 (2017) 402–411. [3] E. Amir-sayyafi, A. Chowdhury, A. Mirmiran, A supper lightweight hurrican-resistant thin-walled box-cell roofing system, in: International Symposium on Structural Engineering, 2016. [4] M. Fesharaki, A. Hamedi, Effects of high-speed rail substructure on ground-borne vibrations, Florida Civ. Eng. J 2 (2016) 38–47.

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