Assessment of pavement deflection under vehicle loads using a 3D-DIC system in the field

1 Vol.:(0123456789) Scientific Reports | (2022) 12:9491 | https://doi.org/10.1038/s41598-022-13176-3 www.nature.com/scientificreports Assessment of pavement deflection under vehicle loads using a 3D‑DIC system in the field Carlos Núñez‑Temes1, Guillermo Bastos1*, Marcos Arza‑García1, Alberte Castro1, Jose Antonio Lorenzana Fernández2, Juan Ortiz‑Sanz1, María Portela2, Mariluz Gil‑Docampo & Francisco Javier Prego3 This study aims to introduce the use of 3D‑digital image correlation (DIC) to the in situ testing of pavements and to support the development of techniques for a rapid evaluation of the conservation status of existing roads. Little research was found on this topic. The passage of a car wheel on an asphalt pavement was adopted as a case study. The DIC measurements were compared to those gathered by contact sensors. From a qualitative point of view, the DIC measurements captured the realistic shape of a deflection basin. From a quantitative point of view, the deflection values provided by the DIC system had a mean error of 0.015 mm and a standard deviation of 0.011 mm. At the moment of highest load, these errors had a mean value and standard deviation of − 0.016 mm and 0.021 mm, respectively. Thus, to improve the accuracy of the system, we propose modifying the camera support, speckle pattern, and control of natural light. Roads are key public infrastructure since they constitute a basic requirement for the social and economic development of any country1. Roads require preservation, maintenance, repair and rehabilitation2,3. To plan and perform these actions, reliable information is needed about the status of preservation of roads. The challenging aspect of road assessment lies in its structural state. The traditional techniques for studying the mechanical properties of bituminous pavements and concrete consist of laboratory testing of samples by using extensometers, strain gauges, and linear-variable differential transformer sensors to measure sample deformation4–7. The insitu modulus and critical stresses and strains of pavements have been determined by measuring the vertical deflection of pavement with falling weight deflectometers since the 1970s8 and fast falling weight deflectometers since 20159. The present paper describes deflection measurements of asphalt pavement using the digital image correlation (DIC) technique to explore the feasibility of performing tests in the field with the accuracy of laboratory techniques. Our objectives were to explore the introduction of DIC technology for testing pavement in the field and, in particular, to accurately measure the deformation of asphalt pavement through 3D-DIC (also known as stereo-DIC). Background on testing pavement in the field. Several insitu nondestructive testing procedures for pavement are available. The main principle consists of applying a load of known value to the pavement and analyzing the direction and size of the induced deformation10. The most common deformation is in the vertical direction11–13. In field tests, there are three typical methods of deflection measurement: static load-based deflectometer, which provides the maximum deflection under a static load14; stationary dynamic impact deflection measurement, as the falling weight deflectometer (FWD)15; and moving dynamic deflection measurement, e.g. the laser dynamic deflectometer (LDD), which captures the deflection along a line on the pavement as the vehicule moves16,17. Though the static load-based Benkelman beam has been used extensively in India and several other countries for structural evaluation of in-service pavements, the FWD is considered to be the most appropriate equipment14,18. OPEN 1CIGEO – Civil and Geomatics Research Group, Agroforestry Engineering Department, University of Santiago de Compostela, Higher Polytechnic School (Lugo), 27002 Lugo, Spain. 2PEMADE – Research Platform on Structural Wood Engineering, Agroforestry Engineering Department, University of Santiago de Compostela, Higher Polytechnic School (Lugo), 27002 Lugo, Spain. 3Department of R+D+I of Misturas, S.A., Orense, Spain. *email: [email protected] 2 Vol:.(1234567890) Scientific Reports | (2022) 12:9491 | https://doi.org/10.1038/s41598-022-13176-3 www.nature.com/scientificreports/ Methods applying stationary dynamic loading allow the measurement of the deflection basin under steadystate loads or impacts. This type of equipment consists of an oscillating force generator, a calibration unit and several deflection-measuring devices (transducers, accelerometers, seismometers, etc.)19,20. The FWD is the accepted worldwide standard15,21. It is mounted in a vehicle that must be stationary to perform a test at the desired location20. The FWD imparts a dynamic load to a pavement structure; the dynamic load is similar in magnitude and duration to that of a moving wheel load, typically 50 kN22,23, and ranging from 7 to 150 kN for the standard FWD24. A light version (LWD) applies 1–15 kN and various heavy versions (HWD) apply up to 250 kN25. The peak deflections at each test location are measured in micrometers. First introduced in Europe, FWD has been in use in the United States since the 1980s21. The U.S. Federal Highway Administration website provides information about the most commonly used steady-state devices and FWDs19. FWDs use a realistic simulation of actual wheel loading and have the ability to measure load transfer across joints and cracks26. However, FWDs also have drawbacks; their stationary operating mode limits the coverage of a test site in a given time, and interruptions due to stop-and-go operations can disturb traffic and cause hazardous conditions. To continuously measure pavement deflections at higher speeds, extensive efforts have been carried out over the past decades to develop methods using moving vehicular loads and continuous profiling devices15. Some of both stationary dynamic loading27 and moving dynamic loading devices15 adopted the test principle of the Benkelman beam test. Some examples are the traffic speed deflectometer (TSD)28,29, rolling weight deflectometer (RWD)30,31, rolling dynamic deflectometer (RDD)32,33, and road deflectometer (RDT)34. Elseifi etal.35 compared these devices and found that the RDT and the RWD can acquire data while moving at 96.6km/h with deflection accuracies of 0.25mm and 0.064mm, respectively. Currently, new measurement techniques are gaining momentum for characterizing heterogeneous materials36. In particular, DIC systems are increasingly used in testing in civil engineering laboratories37 and for determining asphalt microstructure38,39, studying crack initiation and damage distribution40,41, and validating models42,43. Introduction to DIC. As the requirements for information about a specimen increase, the large-scale deployment of wired strain gauges becomes more laborious and expensive. In addition, strain gauges are susceptible to drift and damage44. To address these limitations, DIC was introduced in the 1980s as a contactless sensing method that records grayscale digital images during the loading of a specimen and applies image processing techniques to estimate the full-field deformation of the specimen45. The basic operation in DIC involves tracking points (or pixels) in consecutive photos. Heterogeneous materials can have surface features that suffice as a natural pattern that facilitates this tracking. However, an artificial pattern, called a speckle pattern, is usually applied to the surface to facilitate image correlation. For each pair of images, matching is performed between subsets of the speckle pattern. The subsets are usually square but can also be conformal, in order that geometries capture the outline of a particular part or region46. Two key parameters in a DIC calculation are the subset size and step size47. The subset size is critical to the accuracy of the computed displacements48. After the full-field displacements are evaluated, spatial strains are generally computed from the displacements with a spatial derivative49,50. Since the introduction of the first DIC technology, the quality of the digital images and processing algorithms have improved enormously. Due to its flexibility and a very simple measurement setup (two synchronized cameras and lighting), DIC techniques are applied widely, not only in the field of engineering but also in medicine, multimedia, conservation of cultural heritage, etc.51. Currently, DIC methods with high sensitivity and accuracy are utilized for testing specimens and, most recently, for testing whole structures. It is possible to obtain sub-pixel accuracy of DIC measurements by means of interpolation of image intensity between pixels. Most commercial DIC packages integrate optimized interpolants, allowing to detect image variations up to 0.02 pixel. Depending on the scene configuration, this resolution level could even reach sub-micron accuracies52. Researchers have proved the utility of DIC for measuring deformations in laboratory testing of wood53, concrete54, masonry55, glass panels of curtain walls56, and composites57, etc. Measurements using DIC in the field are becoming more widespread, including applications in metal additive manufacturing58,59, welding-induced deformations60, trees61,62, thermal barrier coatings63, and aircraft crashes64, from the microto the structural scale65. Several insitu experiments were carried out also in civil engineering on concrete bridges66; other large structures67; diaphragm walls, which are used as a support for deep excavations68; and displacements and strains of pipelines due to temperature variations of the fluid68. Several authors have discussed the attractiveness of DIC for testing pavements. In fact, this technique has been applied successfully in the laboratory in compression tests69,70, fatigue tests71, shear tests72 and indirect tensile tests73,74. However, few studies have been performed in the field37. DIC has already been tested on moving vehicles by Shoop etal.75 and in structural monitoring. These authors used DIC to capture the roughness of terrain at the millimeter scale, as their objective was to improve the maneuverability of vehicles by monitoring the road or terrain surface before and after a tire passes over it. The high spatial resolution achievable with DIC, could allow to measure the pavement deflection induced by conventional vehicle loads. This would represent a key advantage of DIC over other major pavement assessment techniques mentioned above, and would make it possible to dispense with the use of heavy towed equipment. Nevertheless, this technology is not yet being used to its highest potential as a reliable and flexible method for measuring displacements and strains at high spatial resolution in the field76. 3 Vol.:(0123456789) Scientific Reports | (2022) 12:9491 | https://doi.org/10.1038/s41598-022-13176-3 www.nature.com/scientificreports/ Materials and methods This study examined a pavement built near the asphalt plant owned by the paving company Extraco S.A. in northwest Spain (42.204193° latitude and − 7.791827° longitude), in a consortium of three more private organizations and two university research groups. The pavement was built as an experimental stretch of 25m long and 3.5m wide, with the geometry of a conventional road lane. The pavement section adopted (Fig.1a) consists of a 5cm wearing course of hot bituminous mix AC 16 surf 50/70 D (according to the Spanish standard UNE-EN 13108-1:2019) with a bitumen content of 5.0% and with 0.5% of the additive rich in lignin, a 5cm intermediate layer with a hot bituminous mix AC 22 surf 50/70 D with a bitumen content of 5.0% and without additives, and a 16cm base layer based on artificial gravel ZA 0/32. The sub-base was made up of a 20cm layer of soil stabilized insitu with also 2% of the lignin-rich additive. A geotextile separates the built layers from the natural ground. The co-product rich in lignin from the eucalyptus wood panel industry77 was added in the hot bituminous mix, aiming to reduce the proportion of binder. The object of study of that project was to know how this additive affects the workability of the mix, the construction of the pavement and its long-term behavior. A speckle pattern was sprayed on the monitored region of interest of the pavement, using a fine aerosol white coating followed by a spot distribution of black paint (25–40% of coverage). The DIC equipment was the commercial Aramis 3D system by GOM GmbH (Braunschweig, Germany), with dual 12M rev03 cameras and Titanar B 24-mm lenses. As shown in Fig.1b, the DIC cameras were mounted on tripods. The cameras have 12-megapixel resolution and a maximum image capturing rate of 25 fps at maximum resolution78, and the angle between their optical axes was 25.2°. The first stage in the general workflow of stereo-DIC (Fig.2) is the system calibration (required for stereo triangulation). For each camera of a typical stereo-pair, calibration aims to find the intrinsic parameters (defining the geometric and optical characteristics of the camera) and the extrinsic parameters (defining the position and the orientation of the camera with respect to a reference coordinate system). These parameters can be calculated through the comparison of optically measured deformations and theoretical predictions, for which images of a calibration plate with known dimensions are normally used. In particular, Aramis 3D uses its own in-house calibration plate with a regular grid of dots and auto-detectable coded targets and a specific calibration routine based on the Bundle Adjustment (BA) method. The user is required to take a series of stereo pairs of images by varying the position of the plate (rotations and translations). BA allows for the estimation of both the intrinsic and extrinsic camera parameters by using these repetitive observations of sparse scene points in those different viewing directions79,80. Together, the intrinsic and extrinsic parameters serve to describe the transformation that maps each 3D material point P in the global coordinate system into its image point on the camera sensor81. As these parameters (camera poses) define for themselves the relationships between the image space and the 3D space, there is some flexibility with regards to calibrating the cameras outside of the actual experimental setup. For this reason, the cameras can first be calibrated in a horizontal position (Fig.2a) and oriented towards the pavement after completing the procedure. The second stage of DIC processing consists in tracking the speckled pattern in the sequence of stereo-images of the surface during the test. This process involves multiple 2D-DIC correlation runs, including cross-pair and Figure1. Experimental setup: (a) diagram of pavement structure and data acquisition systems, and (b) overall setup of the equipment used to monitor the wheel load. 4 Vol:.(1234567890) Scientific Reports | (2022) 12:9491 | https://doi.org/10.1038/s41598-022-13176-3 www.nature.com/scientificreports/ cross-camera subset matching, where the image from one camera is the reference image and the image from the other camera is the deformed image. During this process, the software correlates homologous points along the whole dataset, identifying their coordinates in each of the images. Then, the calibration parameters from stage 1 are used to perform a stereo-triangulation which transforms the 2D points matched in stage 2 for each stereo-pair into point clouds. In order to obtain a more efficient 3D data analysis, point clouds are converted to triangular meshes. The surface displacements along the test can finally be obtained by using 3D surface comparison algorithms. Data was acquired on a sunny and windless day, with temperatures ranging from 17.1 to 23.6°C, falling within the recommended range of DIC operation83. The major problems with the temperature in indoor DIC experiments usually occur because almost all cameras and lights become hotter than room temperature when run continuously. Outdoors, this problem should be in part minimized, but instead, other relevant issues that could introduce errors in DIC results may arise. Some of these potential issues are the thermal expansion of the components of the cameras, the lenses, or the mounting structures and the induced convective air currents. In that sense, several precautions have been taken to prevent possible errors. The cameras and other equipment were not calibrated or used to take measurements until they have reached a stable operating temperature in the field (at least 15min). On the other hand, the lighting required for DIC may introduce heat waves that could refract light between the test surface and the imaging system. A special “cool” (blue) LED source with an integrated fan was used (Fig.1b) to minimize this effect. We also recorded the deflection values through four 1-µm-precision extensometers, which we adopted as the reference. In addition, four reference point markers (tie points) were attached to these sensors to read their displacement according to the DIC device, as presented in Fig.3. Extensometer 1 was closest to the loaded area. The comparison of the DIC readings at these points with the readings provided by the extensometers allowed a direct assessment of the agreement between the DIC device and the contact sensors. The minimum distance between the middle point between the camera lenses and the loaded ground was 380mm. The measured area had a width of 195mm and a maximum length of 205mm approximately, which decreased during the test to the minimum length of 145mm, because the wheel partially obstructs the view of Figure2. Workflow with the core steps in stereo-DIC processing: (a) Stereo calibration procedure to find intrinsic and extrinsic parameters of the optical system; (b) cross-correlated 2D-DIC and (c) 3D-DIC postprocessing to calculate 3D coordinates of the triangular mesh’s vertices and to derive the full-field displacements. Adapted from Arza etal.82. 5 Vol.:(0123456789) Scientific Reports | (2022) 12:9491 | https://doi.org/10.1038/s41598-022-13176-3 www.nature.com/scientificreports/ the cameras. The extensometers were attached to a square aluminum beam, and they were lowered until they made contact with the asphalt pavement. The loading wheel was initially placed 2280mm from the DIC-monitored area. The pavement was loaded by a pass of a moving car, more specifically through one of its wheels, with a load of approximately 3.8 kN. The tire covered an approximately rectangular area of 135mm in the direction of motion and 190mm in the orthogonal direction. The pass of the wheel started after the contact sensors stabilized. Results and discussion After the sensing equipment was turned on and the extensometers stabilized, the vehicle moved at approximately 78mm/s. The DIC system acquired the images with a shutter speed of 1/500s and at a frequency of 0.5 images per second, resulting in a total of 21 images. Therefore, in terms of DIC processing, we consider 20 time “steps”, as the software matches the pattern between the reference image (first) and the other ones, computing the displacements of the pattern in each one of these moments. The noise-floor was measured across the vertical deformation along a line parallel to the wheel motion (see Fig.4). This noise is a multiple of the spatial standard deviation of the quantities-of-interest (QOI) computed under conditions in which the QOI should be zero. It does not reflect any systematic bias errors that may be Figure3. GOM’s auto-detectable targets (tie points) attached to the extensometers. Figure4. The line across which the noise-floor was evaluated. 6 Vol:.(1234567890) Scientific Reports | (2022) 12:9491 | https://doi.org/10.1038/s41598-022-13176-3 www.nature.com/scientificreports/ present in the QOI, but only the random variance error of the QOI83. In this case, the QOI is the deflection. We obtained the noise-floor by taking the first 9 steps. They are considered quasi-stationary, what is corroborated not only by the DIC images, but also by the readings of the extensometers. These readings indicate that significant deflection (> 0.01mm) are only detectable from step 12 onwards. A representative sample of the noise-floor is presented for three steps in Fig.5a–c. In step 6 (Fig.5c), the wheel axle was at 1728mm on the X axis. In these graphs, the deformation reached a positive peak of 0.29mm and a negative peak of − 0.09mm. The Q-Q plot (Fig.5d) of these data reveals that they fit a normal distribution. The z-score of these extreme values ranged Figure5. Noise baseline computed from DIC processing. Sample of initial steps not affected by the wheel load: (a) Step 2; (b) Step 4; (c) Step 6; (d) the Q-Q plot for step 2. 7 Vol.:(0123456789) Scientific Reports | (2022) 12:9491 | https://doi.org/10.1038/s41598-022-13176-3 www.nature.com/scientificreports/ from 2.4 to 8.4. We hypothesize that this was the result of frame mismatching due to the potential poor quality of the speckle pattern in some specific areas. In fact, during the unloaded steps, the deflection in some of these areas alternated between positive and negative values, as shown in Fig.5. These anomalous data were avoided by applying in the DIC software a median filter of three points to the values of displacement. In these initial steps, the detected deflections are not related to the pavement load. With regard to the noise in the detection of the tie points (auto-detectable targets), in the four first Steps, when the car wheel reached a distance to the sensors of 1.85m, the reported values had a mean of absolute values of 1.2µm and a standard deviation of 1.1µm. The maximum absolute value was 3.3µm, which is the 4.3% of the maximum value reported. An adequate balance between exposure time and the moving velocity is essential to limit motion blur in DIC. The noise-floor can be considered the most conservative estimate for the maximum allowable object motion over the course of the exposure time83. In this case, the estimated displacement per exposure is almost negligible (~ 0.4µm) and much lower, in any case, than the noise-floor (considering a very conservative estimate of the displacement velocity of the points on speckled pattern (~ 0.2mm/s) and the exposure time employed (1/500s)). A sample of representative load conditions is presented in Fig.6a–i. The captured deformation becomes more evident in step 11, as it can be seen in Fig.6c. In view of the images of step 13 and step 16, the cameras captured not only the pavement next to the tire but also part of the area behind and front of the tire, respectively. The variation with time of the point P (introduced in Fig.6) is shown in Fig.7. This figure also contains the position of the wheel over the test, through its X coordinate. The deflection in the vertical direction is displayed in Fig.8 along seven curves spaced 30mm apart. These curves are the intersection between the pavement surface and vertical planes that are parallel to the extensometers. The vertical displacement along the seven curves is shown in Fig.9 for step 16, which captured the deepest basin. The deflection of the pavement under the sensors was calculated by interpolating the DIC-based curves. Figure6. Representative deflection conditions captured by the DIC system. The direction of wheel motion is the X-axis and the border of the pavement contact area coincides approximately with Y = 160mm. The point P is the closes point in which measures were recorded in all steps. 8 Vol:.(1234567890) Scientific Reports | (2022) 12:9491 | https://doi.org/10.1038/s41598-022-13176-3 www.nature.com/scientificreports/ Time step time (s) X coordinate (mm) 12 -2040 24 -1884 36 -1728 48 -1572 510-1416 612-1260 714-1104 816-948 918-792 10 20 -636 11 22 -480 12 24 -324 13 26 -168 14 28 -12 15 30 144 16 32 300 17 34 456 18 36 612 19 38 768 20 40 924 Figure7. Variation with time of: (left) the deflection at point P; (right) the center point of the loaded area. The X axis is represented in Fig.4. Figure8. Color map of the deflection basin and seven measuring lines at step 16. Generated with GOM Inspect Pro (Aramis 3D v2019, GOM GmbH, Braunschweig, Germany). 9 Vol.:(0123456789) Scientific Reports | (2022) 12:9491 | https://doi.org/10.1038/s41598-022-13176-3 www.nature.com/scientificreports/ A fairly uniform deformation was measured in the fields of view of all the cameras along 180mm in the wheel motion direction and in the orthogonal direction. Those deflection curves were interpolated to build a 3D surface, which is shown in Fig.10. From a qualitative point of view, the DIC system performed a realistic capture of the deformation on the pavement. The assessment of the DIC was performed from the deformation detected by DIC on two sets of elements. The first set of elements is the tie points (see Fig.3). In particular, the difference was calculated between the mobile points and the fixed points of the sensors (ΔZ DIC). The second set of elements is the points of the pavement that were in contact with the sensors. Only the two sensors closest to the wheel pass area (Ext. 1 and Ext. 2) were used in the analysis, as the more distant ones did not register significant variations above their noise-floor during the test. Since the sensors themselves block the visibility of the points of contact from the view of the Figure9. Vertical displacement of the pavement along the sampled lines at step 16. Figure10. Deformed surface at step 16.