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Detection of forest road damage using mobile laser profilometry
Computers and Electronics in Agriculture 166 (2019) 105010
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Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag
Detection of forest road damage using mobile laser profilometry a,⁎
b
a
Michal Ferenčík , Miroslav Kardoš , Michal Allman , Zuzana Slatkovská a b
T
b
Department of Forest Harvesting, Logistics, and Ameliorations, Faculty of Forestry, Technical University in Zvolen, Slovakia Department of Forest Resource Planning and Informatics, Faculty of Forestry, Technical University in Zvolen, Slovakia
A R T I C LE I N FO
A B S T R A C T
Keywords: Forest roads Laser profilometry Damage detection Road surface
In this study, we tested the ability of mobile laser profilometry to quantify and compare forest road surface damage. We scanned a 1044 m long road, composed of six sections built from base aggregate layers with top layers constructed from various materials: section stabilized with Portland cement, an aggregate road with the Tensar SS 30 Geogrid, milled asphalt, milled construction waste with lime cover, aggregate section, section covered with sand. We used vehicle-mounted Roadscanner device to measure the road profiles while driving on the road, which took 170 s. The device recorded 4000 points per one profile perpendicular to the driving direction, with measuring frequency 1–5 kHz. The width of the road was 4 m (resolution 1 mm). Individual profiles were connected into a 3D profile with the help of an integrated inertial system. On each road section, the system recorded the number of damages (potholes, cracks), volume of individual damages (dm3), area of individual damages (dm2), and the mean and maximal depth of the individual damages (cm). After the scanning, we measured the same road sections manually with resolution 10 mm, which took 8 h. The total damage volume was 9.75/27.61 m3 (scanning/manually), with a total area of 241.4/1277.8 m2. It represented 5.8/30.6% of the road surface (4176 m2). We found the road sections evaluated as the most damaged in case of manual measurements were different than the most damaged sections evaluated by scanning. We found statistically significant differences between individual road sections only in cases of the depths and volumes of recorded damages.
1. Introduction Properly designed and built network of forest roads is necessary for optimal management of forests. This network usually consists of onestrip forest roads (Klč and Novák, 2006). Management of the forests, forest harvesting, game management, recreational activities, as well as fire protection also require access to forests by roads (Demir, 2007). Forest roads are the most important constructions in forestry and frequently determine whether the management of a particular forest will be sustainable or not (Gumus, 2009). Their optimal spacing depends on various factors such as logging method, costs, timber value, the capacity of the landings, shape, and slope of the terrain, accessibility to machinery, and the possibilities to construct new roads (Najafi et al., 2008). Logging process loads forest roads considerably by the forest harvesting machine traffic, frequently causing various damages, though roads with higher bearing capacity can be used by heavier vehicles without causing further damage. Damages occur when mechanical, physical, or other factors disturb the road surface and negatively affect the operational functions and the load-bearing capacity of the road. In short, well-maintained roads in good condition are essential: uneven ⁎
road surface, ruts, and holes require a slower driving speed, impairing the efficiency of timber haulage. Efficient road maintenance is, therefore, a vital part of forestry, and as such calls for an efficient damage detection system that enables rapid response, thus saving costly, large-scale repairs. Furthermore, detailed road environment information is needed for an increasing number of different applications, such as noise modeling, road safety, road maintenance, location-based services, driver assistance systems, and car and pedestrian navigation (Lehtomäki et al., 2010). Civil engineers take advantage of modern methods, such as mobile laser scanning. The use of this (and similar) method of road condition evaluation will gain further popularity, because mobile mapping that processes the data into 3D models, can provide accurate, intelligent, and up-to-date 3D roadside information that will be needed especially for vehicle and pedestrian navigation and location-based services (Kaartinen et al., 2012). Remote sensing technologies have also been commonly used in many similar applications in the last 15 years. The derived three-dimensional data provided by remote sensing are regularly used for digital terrain and surface modeling (Hrůza et al., 2018). Individual laser-scanning systems may be classified into four categories (Wu et al., 2013): (i) Satellite-based Laser Scanning (SLS); (ii) Airborne
Corresponding author at: T.G. Masaryka 24, 960 53 Zvolen, Slovakia. E-mail address: [email protected] (M. Ferenčík).
https://doi.org/10.1016/j.compag.2019.105010 Received 16 January 2019; Received in revised form 11 September 2019; Accepted 12 September 2019 0168-1699/ © 2019 Elsevier B.V. All rights reserved.
Computers and Electronics in Agriculture 166 (2019) 105010
M. Ferenčík, et al.
The total length of stabilized haulage roads at the UFE is 462 km, density 47.5 m ha−1. We chose a 1044 m long and 4 m wide, continuous part of a forest road, composed of six sections, with various constructions (Table 1) for scanning and field measurements. This road serves as a demonstration object for displaying various types of forest road types to students, however, it is commonly used for transport of timber, material, and people as well. The road selected carried the same load about 560 m3 of timber per year on average. It is situated under the top of the ridge and timber haulage is performed downhill because of steep parts. The composition of the individual road sections (Table 1) was as follows:
Laser Scanning (ALS, namely, airborne LiDAR); (iii) Mobile Laser Scanning (MLS; full name: Vehicle-borne Laser Scanning, VLS); (iv) Terrestrial Laser Scanning (TLS). Other authors base their two-dimensional imaging on systems such as charge-coupled device (CCD) or complementary metal–oxide–semiconductor (CMOS) cameras, producing red, green, blue (RGB), or multispectral images that allow gathering dense information about a scanned surface (Chambon and Moliard, 2011). However, these need specific workflows to process the images and extract data. Three-dimensional imaging technologies (Mathavan et al., 2015) that compute the depth of a given environment include digital photogrammetry and structure from motion (SfM) technique. Cheap devices, consisting of RGB or infrared (IR) cameras and depth sensors (e.g. Microsoft Kinect TM, Google Tango), were also tested for use in applications such as road roughness detection (Marinello et al., 2017). Despite their use in civil engineering, these modern methods of assessing road damages are still largely unused in forestry. Manual surveying, measuring the road damages (length, width, and depth) and profiles are still frequently used for evaluation of the damages in Slovak forestry. The measurements are performed using measuring rods (striped poles), level and measuring tape with an accuracy of one cm (Klč and Králik, 1991). All mentioned methods have their pros and cons that stem from their intended purpose. The SLS has sparse sampling points and is not adequate for road surface scanning (Gong et al., 2011). In contrast, the TLS has the highest accuracy and sampling density, thus it could be useful for road surface scanning. However, the poor mobility of the TLS restricts surveying long forest roads. The ALS has yet another advantage, as it is capable of gathering dense and relatively accurate elevation measurements. Data gathered through the ALS served for example to estimate urban green volume (Huang et al., 2013), or to detect individual trees (Kim et al., 2011). It is also an effective method for assessing the road ditches and quality of the surface with suitable accuracy (Kiss et al., 2015). This method would be limited by dense canopy or vegetation cover (Kiss et al., 2016) which is often the case in the process of scanning of the forest roads. The MLS systems are widely used in urban areas, mostly to scan and evaluate paved roads. Scanning is fast, easy, and allows obtaining very dense and realistic point cloud (Hrůza et al., 2018). In conclusion, the MLS should be optimal technology for measuring and scanning of road profiles. Laser profilometry is a procedure, through which we measure a surface profile so that the roughness and cracks in the scanned material (e.g. a surface of the forest road) can be quantified. The method uses laser triangulation principle based on the measurement of the angle of view of a laser beam, projected onto the object surface (Giesko et al., 2007). Mounted on vehicles, laser scanning provides an effective method for surface detection that can be applied to road quality inspection (Laurent et al., 2012; Tsai et al., 2012). However, there is a lack of information on the large-scale application of the method to detect road damage in the varied forest environment. The main objective of this study was to: i) test the applicability of mobile laser profilometry in field research on the damage of road surface, compared with manual measurements ii) quantify and compare damages on the surface of individual road sections built from various materials.
1. A 5 cm layer of aggregate (ø 4–8 mm), stabilized with Portland cement (30 kg m−2) laid on base and rolled. A 15 cm thick base layer was originally composed of aggregate (ø 0–32 mm). This layer was milled to finer aggregate before putting the top layer; 2. A 10 cm top layer of aggregate (ø 0–32 mm) on top of a 10 cm layer (ø 32–63 mm aggregate) and a Tensar SS 30 geogrid (“Geomat,” 2019); 3. A 10 cm top layer of milled asphalt on top of a 10 cm layer of aggregate (ø 0–32 mm), on a soil subgrade; 4. A 15 cm thick top layer consists from milled construction waste (ø 0–32 mm), improved with one cm thick layer of a lime covers the base layer of aggregate (ø 32–63 mm) with added finer aggregate (ø 0–32 mm) to 10 cm thickness. 5. A 10 cm layer of aggregate (ø 0–32 mm) on top of a 10 cm base layer of aggregate (ø 32–63 mm); 6. A 5 cm thick sand layer on top of a 20 cm aggregate (ø 0–32 mm) layer. The first road section was completely reconstructed in 2007 and the rest in 2000. 2.2. Scanning system In October 2016 we scanned the road using a detachable device for mobile, contactless scanning and measuring of road profiles (ROADSCANNER) produced by the KVANT LLC Corporation (“Visionsystems,” 2019). Scanning system worked in 3D space with X-axis perpendicular to the driving direction, Y-axis in direction of driving and Z-axis in the vertical direction. The corporation uses components made by various producers and integrated them into the system with their own original operational software. The device was attached to the boot of a sports utility vehicle (Fig. 1). The speed of the scanning (vehicle speed) was variable and depended on the road surface, slope and directional parameters of the road. The scanning system consists of 8 individual scanning units, inertial measurement unit, GPS unit, wheel encoder, digital video camera, and onboard computer. The black and white industrial camera was synchronized with the scanner, housed in a heated compartment. It recorded surface of the road and close surroundings for review purposes (identification of objects on roads, such as fallen branches, stones, etc.). The camera did not act as part of the triangulation process. As the basic device for measurements, there were eight scanner units installed, with scanning speed from 1 to 5 kHz (700–5000 profiles s−1), a field of view of 350–1000 mm (each unit). Since the system consists of eight scanners, it was capable of recording 40,000 individual “short” profiles s−1. In practice, the software combined profiles from all of the scanning units into one “long” profile, which covered the whole width of the road (1000–5000 such profiles s−1). The system worked with a resolution in X-axis of 1 mm (4000 points per one profile), 0.15 to 0.55 mm for the Z-axis, and a measurement height of 450 mm. The Yaxis resolution depended on the actual speed of the vehicle and was 1.2–6.14 mm. The laser class of the scanner was 3B (< 500 mW),
2. Materials and methods 2.1. Study area and road sections Our research site was located at the University Forest Enterprise (UFE) of the Technical University in Zvolen (Fig. 1), near the town of Zvolen, Slovakia. The UFE is a specialized forest enterprise with the main goal of supporting the university’s educational process. The total area of the UFE is 9724 ha and total annual cut (2016) was 68,236 m3 (16,094 m3 coniferous, 51,942 m3 broadleaved species). The total volume of timber transported from forest landings in 2016 was 67,457 m3. 2
Computers and Electronics in Agriculture 166 (2019) 105010
M. Ferenčík, et al.
Fig. 1. Localization of UFE Zvolen (left) and a scanning device (right) attached on SUV class vehicle. Table 1 Basic data on particular road sections of the scanned road. No. of section
1 2 3 4 5 6
Type
Cement Geogrid Asphalt Lime Aggregate Sand
Length (m)
190 85 73 28 414 254
GPS coordinates – the starting point of the section
GPS coordinates – the end point of the section
N
E
N
E
48°38′34.48504″ 48°38′28.43022″ 48°38′25.72339″ 48°38′23.39712″ 48°38′22.50286″ 48°38′08.85276″
19°02′10.33897″ 19°02′09.36384″ 19°02′08.68863″ 19°02′08.33886″ 19°02′08.39294″ 19°02′11.19001″
48°38′28.42900″ 48°38′25.72339″ 48°38′23.39726″ 48°38′22.50286″ 48°38′08.85276″ 48°38′01.27702″
19°02′09.36160″ 19°02′08.68863″ 19°02′08.34145″ 19°02′08.39294″ 19°02′11.19001″ 19°02′13.89551″
damage rates on individual road sections. We analyzed the data in Statistica 12.0 software, using the Shapiro-Wilk test, descriptive statistics (median, 25 and 75% quartiles), Kruskal-Wallis ANOVA test and Spearman's rank correlation. We performed the Shapiro-Wilk test to confirm whether the data came from a normally distributed population. The test did not confirm the normal distribution of the data set; therefore, we used non-parametric statistical analyses. We used the median, 25 and 75% quartiles for a description of the data and for statistical analyses. We analyzed the values of depths, areas, and volumes, whether they varied between the individual sections using Kruskal-Wallis ANOVA test (significance level of p < 0.05). In case the Kruskal-Wallis test showed significant differences in observed damages between particular sections we identified, which pairs were significantly different through multiple comparisons of mean ranks for this purpose. We analyzed the relationships between the road slope and other parameters of recorded damages (area, average depth, and volume) using the Spearman’s correlation.
808 nm, housing IP67. Scanning system also contained an inertial measuring unit (IMU) which enables connection of individual 2D profiles into the 3D scan with compensation of misalignments caused by vibrations of the vehicle and changes of the speed. The unit consisted of distance measuring indicator (DMI), with an encoder for speed measurement, a mount for the tires, housing IP67. There were two gyroscopes with sensitivity 30 g, 11 ms, vibration level 0.1 g2/Hz, 1 h/os, working temperature −40 °C to + 65 °C, precision ± 100°/s and three accelerometers (x,y,z), acceleration 100 g −11 ms, working temperature from −55 °C to + 95 °C, precision of accelerometer < 20 μg/°C, resolution 5 μg, bandwidth 150 Hz and a GPS antenna. 2.3. Data recording and analyses The measured data were recorded onto a hard drive and were immediately available for analyses. The point cloud was stored for further analysis. The data were processed using the Road Scanner ver.2 software developed by the Kvant corporation. Within each road section, the software recorded the following parameters: (i) the number of damages (potholes, cracks); (ii) the volume of individual damages (dm3); (iii) the area of individual damages (dm2); (iv) the mean and maximal depths of individual damages (cm). The software was set to register the damages which met following: minimal depth 2 mm, minimal diameter 50 mm and minimal area 100 mm2 with regards to the dimension of material used for road construction and to the time of data processing. We also calculated the total and median values (depth, area, and volume) of recorded damages in individual road sections. In case of the depth, the software calculated the mean depth of every individual damage (pothole/crack) and subsequently, we calculated median depths for particular road sections. We used Klč, Králik classification (Klč and Králik, 1991) of road damage to classify the state of each road section. This classification is standard in Slovakia and the Czech Republic (Table 2). Because the individual road sections were not equally long, we also calculated the damage values per 100 m. Thus, we could compare the
2.4. Comparing the results and test of the accuracy In March 2019, we performed the field measurements on the same part of the road for comparing the results with the data recorded by the scanning system. It was two and a half years later, but there was no maintenance performed on the road and the surface of the road did not change significantly. The measurements took place on sample plots, which were 10 m long and 4 m wide and we placed in randomly on individual road sections. We established 19 sample plots in total, in accordance of the length of the sections (3 for Cement, 3 for Geogrid, 2 for Asphalt and also for Lime, 5 for Aggregate, and 4 for Sand). We measured dimensions of individual damages, such as potholes, ruts, and cracks at the plots. Measurements were performed using a level and measuring tape. We identified the damages, measured their dimensions with an accuracy of one cm and depth with an accuracy of 0.5 cm. These data allowed us to estimate the area and volume of damages per plot and for the whole section. Volume and area per 100 m were also calculated in every section to compare them with the data recorded by the scanner. We also measured the average slope of the plot and 3
Computers and Electronics in Agriculture 166 (2019) 105010
M. Ferenčík, et al.
Table 2 Classification of road damages for 1 km of road 3.5 m wide (Klč and Králik, 1991). Damage category
Status
Damaged area (%)
Total damage volume (m3)
Necessary treatment
I II III IV V
Very good Good Moderate damage Bad Severe
0–10% 11–30% 31–50% 51–70% 71% +
0–50 51–99 100–149 150–200 201+
Preventive care Periodical maintenance Maintenance/repair Repair Reconstruction
recorded the time consumption for measurements. We tested the accuracy of the Roadscanner software detection of individual damages, comparing the volume of the scanned transversal ditch (2 m clear section of 4 m total length). The ditch was steel, Ushaped, open-top prefabricate, easily identifiable both in data from road scanner and in terrain, close to measured road sections. We used the Roadscanner polygon measurement tool for manual selection the top edges of the ditch and the software calculated the characteristics of the selected area (2 m section of the ditch). We repeated these measurements 10 times to compute basic statistics (average volume and standard deviation). In order to obtain accurate and reliable reference data, we used point cloud created by close-range photogrammetry with ground control points measured in local coordinate system by universal total station Topcon GPT 3002 LN (Fig. 2).
Table 3 Distribution of the damages on individual road sections calculated per 100 m (Sc – data from scanner, Man – data from field measurements). Section
Cement Geogrid Asphalt Lime Aggregate Sand
Mean slope (%)
Total number of damages per 100 m
Total volume of damages per 100 m (m3)
Total area of damages (m2) per 100 m
Sc
Man
Sc
Man
Sc
Man
Sc
Man
9.0 10.7 8.2 7.7 15.4 10.2
10.8 9.8 8.3 8 14.3 10.6
139 452 231 161 327 146
23 20 20 20 24 28
0.49 2.41 0.96 0.34 1.14 0.49
3.27 2.27 1.14 4.87 2.84 2.17
13.78 58.51 22.12 11.71 26.41 14.44
148.5 142.9 94 168.4 127.8 90.3
3. Results The total volume of the damages on the selected part of the road was 9.75/27.61 m3 (scanning/manually), with a total area of 241.4/ 1277.8 m2. It represented 5.8/30.6% of the road surface (4176 m2). We found the geogrid and aggregate were the most damaged sections according to the scanned data and the lime and cement were the most damaged sections according to the manual measurements. In contrary, the lime and cement sections were the least damaged in case of the scanned data and the asphalt and sand were the least damaged in case of the manual measurements (Table 3). We noted significant differences in the recorded number of the damages between manual method and scanning.
Scanning the 1044 m long part of the road took 170 s, preparation of the system for measurements took half an hour. Automated data processing on the computer took 5 h. We found potholes were the most frequent type of recorded damage. Manual measurements of the same part of the road took 8 h, manual processing of the data took 1.5 h. Times for calculations of volumes and areas per 100 m and total volumes and areas were comparable in both methods. Costs per 1 km of the scanning were approximately ten times higher compared to manual measurements. We recorded ruts as the most frequent type of damage during manual measurements.
Fig. 2. Photogrammetric RGB colored point cloud with control points. 4
Computers and Electronics in Agriculture 166 (2019) 105010
M. Ferenčík, et al.
Fig. 3. The median, 25–75% quantiles of the damage area, mean depth of damage and damage volume.
(Table 5). We analyzed the relationships between the road slope and other parameters of recorded damages (area, average depth, and volume) using the Spearman’s correlation. We found only weak correlations between the slope and damage area and between the slope and the average depth of the damage (Table 6). The average volume of the ditch computed from manually measured polygon in the Roadscanner software achieved a value of 9.698 ± 0.292 dm3, while reference data provided a volume of 10.369 dm3. The difference was 0.671 dm3 (−6.4%).
We used the data recorded by the scanning system for comparison of individual road sections. The median, 25 and 75% quartiles are presented in the picture (Fig. 3). We recorded the largest median area of individual damage on the cement (3.98 dm2) and sand (3.79 dm2) road sections. The lowest median values were recorded on lime (3.22 dm2) and milled asphalt (3.21 dm2) sections. The Kruskal-Wallis ANOVA did not confirm significant differences between the damaged areas (dm2) on individual road sections (p = 0.0555). We used the same tests for the depths of the individual damages on all road sections. The data did not fit the normal distribution according to the Shapiro-Wilk test. We observed the highest median depths of the potholes in case of the aggregate (3.66 cm) and the geogrid (3.53 cm) road sections. The lowest median values were recorded in the case of sand (1.65 cm) and lime (2.00 cm) sections (Fig. 3). There were significant differences in the depth of damages between the individual road sections (p = 0.000). We found the differences in median depth of damages between the individual road sections were statistically significant in almost all cases (Table 4). The deepest damages were found on the aggregate section (20.03 cm), milled asphalt (19.03 cm), followed by the cement (18.5 cm), geogrid (17.35 cm), sand (17.2 cm), and lime (9.16 cm). We observed the highest median volume (dm3) of potholes (Fig. 3) on the aggregate (1.25 dm3) and geogrid (1.25 dm3) sections. The lowest median values we recorded in the case of the sand (0.70 dm3) and the lime (0.74 dm3). The Kruskal-Wallis analysis showed significant (p = 0.000) differences between the damage volumes recorded on individual road sections. We found significant differences mostly in the case of sand against all other sections, with the exception of the lime section
4. Discussion Laser profilometry allowed us to compare the damage caused by haulage trucks to various types of road constructions. Aerial photography, made by unmanned aerial systems (Hrůza et al., 2016) allowed the authors to calculate the volume of the damages in the test section of an asphalt paved road. The total volume of material needed to repair a 500 m long road section was 40.46 m3 (i.e. 80.92 m3 per 1 km). The drone was moving at a speed of 1 m s−1 and the measurement of the road section took about 9 min (18 min per 1 km). We found the scanning method of data collection as more efficient (less than 3 min per 1 km). Automated data processing took 5 h, but it needed only manual setting of the threshold dimensions and it runs independently (for example during the night). Manual measurements of 1 km took one whole shift per 2 persons. In our case, both methods, based on the total volume of damages classified the road to the first category; i. e. the road was in good shape and needed only preventive maintenance (Klč and Králik, 1991). Total amounts of the damages calculated per 100 m of road sections showed significant differences among the sections and also between the 5
Computers and Electronics in Agriculture 166 (2019) 105010
M. Ferenčík, et al.
Table 4 Multiple comparisons of mean ranks (average depth of individual damage) for all road sections (significant differences are boldfaced). Average depth
Multiple comparisons of p levels (average depth cm) Kruskal-Wallis test: H (5, N = 2591) = 195.35p = 0.000 Cement R:1133.2
Cement Geogrid Asphalt Lime Aggregate Sand
0.00 0.11 0.21 0.00 0.00
Geogrid R:1422.3
Asphalt R:1330.0
Lime R:835.91
Aggregate R:1418.6
Sand R:874.61
0.00
0.11 1.00
0.21 0.00 0.00
0.00 1.00 1.00 0.00
0.00 0.00 0.00 1.00 0.00
1.00 0.00 1.00 0.00
0.00 1.00 0.00
methods used for data collection (table 3). Scanning method recorded a huge number of smaller damages (potholes) and manually we recorded only a few damages, but with large dimensions (ruts) and no smaller damages. Roads with closed crown, improved with lime and/or cement were less damaged, compared to the sections crowned with coarse-grained material according to the scanned data. This finding is in accordance with the study by Marinello et al., (2017). But according to manual measurements, there were ruts driven through some sections. The largest ruts were present in the lime and cement sections, where we calculated the highest volume of damage (Table 3). We think these differences in results were caused by too low threshold values for damage registration which we set in the Kvant software. Then the software was not able to find huge damages with smooth changes of surface - typically the ruts. These ruts are visible in the 3D picture from the point cloud recorded by the scanner (Fig. 4). We estimated the damage incurred by the road sections through several factors and found no significant differences between the damaged areas of the sections. The differences in depths and volumes of damages between the individual road sections were statistically significant in almost all cases (tables 4 and 5). The maximal depths are important in case of potholes, because even one pothole deeper than the ground clearance of the vehicle used may completely block the road. We recorded such depths on the milled asphalt and aggregate sections. Furthermore, we found only weak correlations between the slope and the damaged area and between the slope and the mean depth of the damage on the observed road sections (Table 6). We consider the result of the comparison between two measurement methods on the transversal ditch as sufficient for forestry practice, however, the accuracy could be improved using standard geometrical objects (sphere or block) for analyses. Precision forestry, as defined by Taylor et al., (2002) requires an application of modern and efficient methods to determine the state of the forests and their parts (like the forest road network) to enable efficient forest management. Traditional terrestrial profiling methods require a lot of time and resources of qualified personnel to render
0.00 1.00
0.00
Table 6 Spearman's rank correlation between particular variables (boldfaced correlations are significant at the p < 0,05 confidence level).
Slope
Area
Average depth
Pothole volume
−0.06
0.14
0.04
acceptable results. Traditional means used for road surface measurements such as level bars, measuring tapes and other manual tools do not provide a level of accuracy and data density comparable to vehicle laser profilometry. Manual measurements also require a considerable time and restriction of traffic on the assessed road part (Knyaz and Chibunichev, 2016). On the other hand, Vehicle laser profilometry, such as we used, required only a single operator (who also drives the vehicle, on which the device is mounted) during the measurement phase and the data evaluation process was automated to a certain degree through the software. The method was also time-efficient, because the device recorded data rapidly, compared to other methods. The time consumption depended on the vehicle speed, whereas the accuracy of scanning did not suffer at all. Vehicle laser profilometry used in our study provided a high-density point cloud of the transversal profiles (up to 9600 points/profile). Crack and pothole detection rate then depended on the software settings (e.g. the minimal area and/or depth of damage to be considered a pothole, crack, etc.). As a modern method of data collection, it was suitable for a precise evaluation of the road surface parameters. Tomiyama et al., (2012) consider surface roughness of a pavement an important parameter, vital for determining the maintenance and rehabilitation needs of a road (or a whole road network). A reliable roughness survey method requires an objective and repeatable profile measurement, often called “profiling”. Another advantage of the method was the contactless measurement, independent of weather conditions (except for snow and ice directly on the road) and processing of the data in the office. Parameter settings (the smallest recorded depth, area, and volume of what will be considered damage) allows increasing effectiveness and objectivity of the measurement and is the. The method was sensitive to various objects on the road surface (fallen branches, leaves, snow, etc.). In these cases, it
Table 5 Multiple comparisons of mean ranks (volume of individual damage) for all road sections (significant differences are boldfaced). Damage volume
Multiple comparisons of p levels (damage volume dm3) Kruskal-Wallis test: H (5, N = 2591) = 57.25230p = 0.0000 Cement R:1289.3
Cement Geogrid Asphalt Lime Aggregate Sand
1.00 1.00 0.16 1.00 0.00
Geogrid R:1391.1
Asphalt R:1301.4
Lime R:980.11
Aggregate R:1344.9
Sand R:1059.6
1.00
1.00 1.00
0.16 0.01 0.16
1.00 1.00 1.00 0.02
0.00 0.00 0.01 1.00 0.00
1.00 0.01 1.00 0.00
0.16 1.00 0.01
6
0.02 1.00
0.00
Computers and Electronics in Agriculture 166 (2019) 105010
M. Ferenčík, et al.
Fig. 4. 3D visualization of point cloud recorded at 5 m long part of the asphalt section.
was necessary to check the road surface from the synchronized recording of the vehicle-mounted camera. Profilometry scanning technology allows optimizing the road maintenance, to measure the volume of material necessary to fill the potholes and the calculation of the cost. Choi et al., (2016) presented this method as sufficiently precise (86.4% detection rate) for detecting surface cracks in paved roads. In the case of scanning the roads with an uneven surface, the key factor is the correct setting of threshold parameters for minimal registered damage as we mentioned before. There is also the benefit of having a permanent record of the damage, which can be retrieved and compared with future scanned data to quantify the rate of deterioration - temporal effects (Chen et al., 2013).
Geomat [WWW Document], 2019. URL https://www.geomat.eu/en/geogrids/tensar-ss (Accessed 1.10.19). Giesko, T., Zbrowski, A., Czajka, P., 2007. Laser profilometers for surface inspection and profile measurement. Probl. Eksploat. 97–108. Gong, P., Li, Z., Huang, H., Sun, G., Wang, L., 2011. ICESat GLAS data for urban environment monitoring. IEEE Trans. Geosci. Remote Sens. 49, 1158–1172. https://doi. org/10.1109/TGRS.2010.2070514. Gumus, S., 2009. Constitution of the forest road evaluation form for Turkish forestry. African J. Biotechnol. 8, 5389–5394. Hrůza, P., Mikita, T., Janata, P., 2016. Monitoring of forest hauling roads wearing course damage using unmanned aerial systems. Acta Univ. Agric. Silvic. Mendelianae Brun. 64, 1537–1546. https://doi.org/10.11118/actaun201664051537. Hrůza, P., Mikita, T., Tyagur, N., Krejza, Z., Cibulka, M., Procházková, A., Patočka, Z., 2018. Detecting forest road wearing course damage using different methods of remote sensing. Remote Sens. 10, 1–17. https://doi.org/10.3390/rs10040492. Huang, Y., Yu, B., Zhou, J., Hu, C., Tan, W., Hu, Z., Wu, J., 2013. Toward automatic estimation of urban green volume using airborne LiDAR data and high resolution Remote Sensing images. Front. Earth Sci. 7, 43–54. https://doi.org/10.1007/s11707012-0339-6. Kaartinen, H., Hyyppä, J., Kukko, A., Jaakkola, A., Hyyppä, H., 2012. Benchmarking the performance of mobile laser scanning systems using a permanent test field. Sensors (Switzerland) 12, 12814–12835. https://doi.org/10.3390/s120912814. Kim, S., Hinckley, T., Briggs, D., 2011. Classifying individual tree genera using stepwise cluster analysis based on height and intensity metrics derived from airborne laser scanner data. Remote Sens. Environ. 115, 3329–3342. https://doi.org/10.1016/j.rse. 2011.07.016. Kiss, K., Malinen, J., Tokola, T., 2016. Comparison of high and low density airborne lidar data for forest road quality assessment. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 3, 167–172. https://doi.org/10.5194/isprs-annals-III-8-167-2016. Kiss, K., Malinen, J., Tokola, T., 2015. Forest road quality control using ALS data. Can. J. For. Res. 45, 1636–1642. https://doi.org/10.1139/cjfr-2015-0067. P. Klč A. Králik Klč, P., Králik, A., 1991. Katalóg porušení a závad na lesných cestách: Odborná lesnícka aktualita č. 26. Príroda, Bratislava. Klč, P., Novák, J., 2006. Analysis of making access to forests in a model area.[Analýza sprístupnenia lesov v modelovom území]. For. J. 52, 209–221. Knyaz, V.A., Chibunichev, A.G., 2016. Photogrammetric techniques for road surface analysis. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.-ISPRS Arch. 41, 515–520. https://doi.org/10.5194/isprsarchives-XLI-B5-515-2016. Laurent, J., Hébert, J.F., Lefebvre, D., Savard, Y., 2012. Using 3D laser profiling sensors for the automated measurement of road surface conditions. In: Scarpas, A., Kringos, N., Al-Qadi, I., Loizos, A. (Eds.), International Conference on Cracking in Pavements. Springer Netherlands, Dordrecht, pp. 157–167. Lehtomäki, M., Jaakkola, A., Hyyppä, J., Kukko, A., Kaartinen, H., 2010. Detection of vertical pole-like objects in a road environment using vehicle-based laser scanning data. Remote Sens. 2, 641–664. https://doi.org/10.3390/rs2030641. Marinello, F., Proto, A.R., Zimbalatti, G., Pezzuolo, A., Cavalli, R., Grigolato, S., 2017. Determination of forest road surface roughness by kinect depth imaging. Ann. For. Res. 60, 217–226. https://doi.org/10.15287/afr.2017.893. Mathavan, S., Kamal, K., Rahman, M., 2015. A Review of Three-Dimensional Imaging Technologies for Pavement Distress Detection and Measurements. IEEE Trans. Intell. Transp. Syst. 16, 2353–2362. https://doi.org/10.1109/TITS.2015.2428655. Najafi, A., Sobhani, H., Saeed, A., Makhdom, M., Mohajer, M.M., 2008. Planning and assessment of alternative forest road and skidding networks | Planiranje i procjena kakvoće različitih inačica mreže šumskih prometnica. Croat. J. For. Eng. 29, 63–73. Taylor, S.E., McDonald, T.P., Veal, M.W., Corley, F.W., Grift, T.E., 2002. Precision
Funding This research was funded by projects APVV-18-0305, VEGA 1/ 0471/17 and VEGA 1/0868/18. Acknowledgments We gratefully thank the KVANT s. r. o. company for providing us with the scanning device and access to their software for data processing. Declaration of Competing Interest The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. References Chambon, S., Moliard, J.M., 2011. Automatic road pavement assessment with image processing: review and comparison. Int. J. Geophys. 2011. https://doi.org/10.1155/ 2011/989354. Chen, S.-E., Wanqiu, L., Bian, H., Smith, B., 2013. 3D LiDAR scans for bridge damage evaluation. Forensic Eng. 487–495. https://doi.org/10.1061/9780784412640.052. Choi, J., Zhu, L., Kurosu, H., 2016. Detection of cracks in paved road surface using laser scan image data. In: International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences. Prague, pp. 559–562. https://doi.org/10.5194/ isprsarchives-XLI-B1-559-2016. Demir, M., 2007. Impacts, management and functional planning criterion of forest road network system in Turkey. Transp. Res. Part A Policy Pract. 41, 56–68. https://doi. org/10.1016/j.tra.2006.05.006.
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A. (Eds.), 7th RILEM International Conference on Cracking in Pavements. Springer Netherlands, Dordrecht, pp. 169–178. Visionsystems [WWW Document], 2019. URL http://www.visionsystems.sk/solutions/ dynamic-roadscanner/(Accessed 1.10.19). Wu, B., Yu, B., Yue, W., Shu, S., Tan, W., Hu, C., Huang, Y., Wu, J., Liu, H., 2013. A voxelbased method for automated identification and morphological parameters estimation of individual street trees from mobile laser scanning data. Remote Sens. 5, 584–611. https://doi.org/10.3390/rs5020584.
forestry: operational tactics for today and tomorrow. In: 25th Annual Meeting of the Council of Forest Engineers, pp. 6. https://doi.org/10.1017/S0021849903030228. Tomiyama, K., Kawamura, A., Nakajima, S., Ishida, T., Jomoto, M., 2012. A mobile profilometer for road surface monitoring by use of accelerometers. 7th Symposium on Pavement Surface Characteristics: SURF 2012. Virginia Tech. Virginia Tech Transportation Institute. Tsai, Y.(James), Jiang, C., Wang, Z., 2012. pavement crack detection using high-resolution 3D line laser imaging technology. In: Scarpas, A., Kringos, N., Al-Qadi, I., Loizos,
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Contents lists available at ScienceDirect
Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag
Detection of forest road damage using mobile laser profilometry a,⁎
b
a
Michal Ferenčík , Miroslav Kardoš , Michal Allman , Zuzana Slatkovská a b
T
b
Department of Forest Harvesting, Logistics, and Ameliorations, Faculty of Forestry, Technical University in Zvolen, Slovakia Department of Forest Resource Planning and Informatics, Faculty of Forestry, Technical University in Zvolen, Slovakia
A R T I C LE I N FO
A B S T R A C T
Keywords: Forest roads Laser profilometry Damage detection Road surface
In this study, we tested the ability of mobile laser profilometry to quantify and compare forest road surface damage. We scanned a 1044 m long road, composed of six sections built from base aggregate layers with top layers constructed from various materials: section stabilized with Portland cement, an aggregate road with the Tensar SS 30 Geogrid, milled asphalt, milled construction waste with lime cover, aggregate section, section covered with sand. We used vehicle-mounted Roadscanner device to measure the road profiles while driving on the road, which took 170 s. The device recorded 4000 points per one profile perpendicular to the driving direction, with measuring frequency 1–5 kHz. The width of the road was 4 m (resolution 1 mm). Individual profiles were connected into a 3D profile with the help of an integrated inertial system. On each road section, the system recorded the number of damages (potholes, cracks), volume of individual damages (dm3), area of individual damages (dm2), and the mean and maximal depth of the individual damages (cm). After the scanning, we measured the same road sections manually with resolution 10 mm, which took 8 h. The total damage volume was 9.75/27.61 m3 (scanning/manually), with a total area of 241.4/1277.8 m2. It represented 5.8/30.6% of the road surface (4176 m2). We found the road sections evaluated as the most damaged in case of manual measurements were different than the most damaged sections evaluated by scanning. We found statistically significant differences between individual road sections only in cases of the depths and volumes of recorded damages.
1. Introduction Properly designed and built network of forest roads is necessary for optimal management of forests. This network usually consists of onestrip forest roads (Klč and Novák, 2006). Management of the forests, forest harvesting, game management, recreational activities, as well as fire protection also require access to forests by roads (Demir, 2007). Forest roads are the most important constructions in forestry and frequently determine whether the management of a particular forest will be sustainable or not (Gumus, 2009). Their optimal spacing depends on various factors such as logging method, costs, timber value, the capacity of the landings, shape, and slope of the terrain, accessibility to machinery, and the possibilities to construct new roads (Najafi et al., 2008). Logging process loads forest roads considerably by the forest harvesting machine traffic, frequently causing various damages, though roads with higher bearing capacity can be used by heavier vehicles without causing further damage. Damages occur when mechanical, physical, or other factors disturb the road surface and negatively affect the operational functions and the load-bearing capacity of the road. In short, well-maintained roads in good condition are essential: uneven ⁎
road surface, ruts, and holes require a slower driving speed, impairing the efficiency of timber haulage. Efficient road maintenance is, therefore, a vital part of forestry, and as such calls for an efficient damage detection system that enables rapid response, thus saving costly, large-scale repairs. Furthermore, detailed road environment information is needed for an increasing number of different applications, such as noise modeling, road safety, road maintenance, location-based services, driver assistance systems, and car and pedestrian navigation (Lehtomäki et al., 2010). Civil engineers take advantage of modern methods, such as mobile laser scanning. The use of this (and similar) method of road condition evaluation will gain further popularity, because mobile mapping that processes the data into 3D models, can provide accurate, intelligent, and up-to-date 3D roadside information that will be needed especially for vehicle and pedestrian navigation and location-based services (Kaartinen et al., 2012). Remote sensing technologies have also been commonly used in many similar applications in the last 15 years. The derived three-dimensional data provided by remote sensing are regularly used for digital terrain and surface modeling (Hrůza et al., 2018). Individual laser-scanning systems may be classified into four categories (Wu et al., 2013): (i) Satellite-based Laser Scanning (SLS); (ii) Airborne
Corresponding author at: T.G. Masaryka 24, 960 53 Zvolen, Slovakia. E-mail address: [email protected] (M. Ferenčík).
https://doi.org/10.1016/j.compag.2019.105010 Received 16 January 2019; Received in revised form 11 September 2019; Accepted 12 September 2019 0168-1699/ © 2019 Elsevier B.V. All rights reserved.
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The total length of stabilized haulage roads at the UFE is 462 km, density 47.5 m ha−1. We chose a 1044 m long and 4 m wide, continuous part of a forest road, composed of six sections, with various constructions (Table 1) for scanning and field measurements. This road serves as a demonstration object for displaying various types of forest road types to students, however, it is commonly used for transport of timber, material, and people as well. The road selected carried the same load about 560 m3 of timber per year on average. It is situated under the top of the ridge and timber haulage is performed downhill because of steep parts. The composition of the individual road sections (Table 1) was as follows:
Laser Scanning (ALS, namely, airborne LiDAR); (iii) Mobile Laser Scanning (MLS; full name: Vehicle-borne Laser Scanning, VLS); (iv) Terrestrial Laser Scanning (TLS). Other authors base their two-dimensional imaging on systems such as charge-coupled device (CCD) or complementary metal–oxide–semiconductor (CMOS) cameras, producing red, green, blue (RGB), or multispectral images that allow gathering dense information about a scanned surface (Chambon and Moliard, 2011). However, these need specific workflows to process the images and extract data. Three-dimensional imaging technologies (Mathavan et al., 2015) that compute the depth of a given environment include digital photogrammetry and structure from motion (SfM) technique. Cheap devices, consisting of RGB or infrared (IR) cameras and depth sensors (e.g. Microsoft Kinect TM, Google Tango), were also tested for use in applications such as road roughness detection (Marinello et al., 2017). Despite their use in civil engineering, these modern methods of assessing road damages are still largely unused in forestry. Manual surveying, measuring the road damages (length, width, and depth) and profiles are still frequently used for evaluation of the damages in Slovak forestry. The measurements are performed using measuring rods (striped poles), level and measuring tape with an accuracy of one cm (Klč and Králik, 1991). All mentioned methods have their pros and cons that stem from their intended purpose. The SLS has sparse sampling points and is not adequate for road surface scanning (Gong et al., 2011). In contrast, the TLS has the highest accuracy and sampling density, thus it could be useful for road surface scanning. However, the poor mobility of the TLS restricts surveying long forest roads. The ALS has yet another advantage, as it is capable of gathering dense and relatively accurate elevation measurements. Data gathered through the ALS served for example to estimate urban green volume (Huang et al., 2013), or to detect individual trees (Kim et al., 2011). It is also an effective method for assessing the road ditches and quality of the surface with suitable accuracy (Kiss et al., 2015). This method would be limited by dense canopy or vegetation cover (Kiss et al., 2016) which is often the case in the process of scanning of the forest roads. The MLS systems are widely used in urban areas, mostly to scan and evaluate paved roads. Scanning is fast, easy, and allows obtaining very dense and realistic point cloud (Hrůza et al., 2018). In conclusion, the MLS should be optimal technology for measuring and scanning of road profiles. Laser profilometry is a procedure, through which we measure a surface profile so that the roughness and cracks in the scanned material (e.g. a surface of the forest road) can be quantified. The method uses laser triangulation principle based on the measurement of the angle of view of a laser beam, projected onto the object surface (Giesko et al., 2007). Mounted on vehicles, laser scanning provides an effective method for surface detection that can be applied to road quality inspection (Laurent et al., 2012; Tsai et al., 2012). However, there is a lack of information on the large-scale application of the method to detect road damage in the varied forest environment. The main objective of this study was to: i) test the applicability of mobile laser profilometry in field research on the damage of road surface, compared with manual measurements ii) quantify and compare damages on the surface of individual road sections built from various materials.
1. A 5 cm layer of aggregate (ø 4–8 mm), stabilized with Portland cement (30 kg m−2) laid on base and rolled. A 15 cm thick base layer was originally composed of aggregate (ø 0–32 mm). This layer was milled to finer aggregate before putting the top layer; 2. A 10 cm top layer of aggregate (ø 0–32 mm) on top of a 10 cm layer (ø 32–63 mm aggregate) and a Tensar SS 30 geogrid (“Geomat,” 2019); 3. A 10 cm top layer of milled asphalt on top of a 10 cm layer of aggregate (ø 0–32 mm), on a soil subgrade; 4. A 15 cm thick top layer consists from milled construction waste (ø 0–32 mm), improved with one cm thick layer of a lime covers the base layer of aggregate (ø 32–63 mm) with added finer aggregate (ø 0–32 mm) to 10 cm thickness. 5. A 10 cm layer of aggregate (ø 0–32 mm) on top of a 10 cm base layer of aggregate (ø 32–63 mm); 6. A 5 cm thick sand layer on top of a 20 cm aggregate (ø 0–32 mm) layer. The first road section was completely reconstructed in 2007 and the rest in 2000. 2.2. Scanning system In October 2016 we scanned the road using a detachable device for mobile, contactless scanning and measuring of road profiles (ROADSCANNER) produced by the KVANT LLC Corporation (“Visionsystems,” 2019). Scanning system worked in 3D space with X-axis perpendicular to the driving direction, Y-axis in direction of driving and Z-axis in the vertical direction. The corporation uses components made by various producers and integrated them into the system with their own original operational software. The device was attached to the boot of a sports utility vehicle (Fig. 1). The speed of the scanning (vehicle speed) was variable and depended on the road surface, slope and directional parameters of the road. The scanning system consists of 8 individual scanning units, inertial measurement unit, GPS unit, wheel encoder, digital video camera, and onboard computer. The black and white industrial camera was synchronized with the scanner, housed in a heated compartment. It recorded surface of the road and close surroundings for review purposes (identification of objects on roads, such as fallen branches, stones, etc.). The camera did not act as part of the triangulation process. As the basic device for measurements, there were eight scanner units installed, with scanning speed from 1 to 5 kHz (700–5000 profiles s−1), a field of view of 350–1000 mm (each unit). Since the system consists of eight scanners, it was capable of recording 40,000 individual “short” profiles s−1. In practice, the software combined profiles from all of the scanning units into one “long” profile, which covered the whole width of the road (1000–5000 such profiles s−1). The system worked with a resolution in X-axis of 1 mm (4000 points per one profile), 0.15 to 0.55 mm for the Z-axis, and a measurement height of 450 mm. The Yaxis resolution depended on the actual speed of the vehicle and was 1.2–6.14 mm. The laser class of the scanner was 3B (< 500 mW),
2. Materials and methods 2.1. Study area and road sections Our research site was located at the University Forest Enterprise (UFE) of the Technical University in Zvolen (Fig. 1), near the town of Zvolen, Slovakia. The UFE is a specialized forest enterprise with the main goal of supporting the university’s educational process. The total area of the UFE is 9724 ha and total annual cut (2016) was 68,236 m3 (16,094 m3 coniferous, 51,942 m3 broadleaved species). The total volume of timber transported from forest landings in 2016 was 67,457 m3. 2
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Fig. 1. Localization of UFE Zvolen (left) and a scanning device (right) attached on SUV class vehicle. Table 1 Basic data on particular road sections of the scanned road. No. of section
1 2 3 4 5 6
Type
Cement Geogrid Asphalt Lime Aggregate Sand
Length (m)
190 85 73 28 414 254
GPS coordinates – the starting point of the section
GPS coordinates – the end point of the section
N
E
N
E
48°38′34.48504″ 48°38′28.43022″ 48°38′25.72339″ 48°38′23.39712″ 48°38′22.50286″ 48°38′08.85276″
19°02′10.33897″ 19°02′09.36384″ 19°02′08.68863″ 19°02′08.33886″ 19°02′08.39294″ 19°02′11.19001″
48°38′28.42900″ 48°38′25.72339″ 48°38′23.39726″ 48°38′22.50286″ 48°38′08.85276″ 48°38′01.27702″
19°02′09.36160″ 19°02′08.68863″ 19°02′08.34145″ 19°02′08.39294″ 19°02′11.19001″ 19°02′13.89551″
damage rates on individual road sections. We analyzed the data in Statistica 12.0 software, using the Shapiro-Wilk test, descriptive statistics (median, 25 and 75% quartiles), Kruskal-Wallis ANOVA test and Spearman's rank correlation. We performed the Shapiro-Wilk test to confirm whether the data came from a normally distributed population. The test did not confirm the normal distribution of the data set; therefore, we used non-parametric statistical analyses. We used the median, 25 and 75% quartiles for a description of the data and for statistical analyses. We analyzed the values of depths, areas, and volumes, whether they varied between the individual sections using Kruskal-Wallis ANOVA test (significance level of p < 0.05). In case the Kruskal-Wallis test showed significant differences in observed damages between particular sections we identified, which pairs were significantly different through multiple comparisons of mean ranks for this purpose. We analyzed the relationships between the road slope and other parameters of recorded damages (area, average depth, and volume) using the Spearman’s correlation.
808 nm, housing IP67. Scanning system also contained an inertial measuring unit (IMU) which enables connection of individual 2D profiles into the 3D scan with compensation of misalignments caused by vibrations of the vehicle and changes of the speed. The unit consisted of distance measuring indicator (DMI), with an encoder for speed measurement, a mount for the tires, housing IP67. There were two gyroscopes with sensitivity 30 g, 11 ms, vibration level 0.1 g2/Hz, 1 h/os, working temperature −40 °C to + 65 °C, precision ± 100°/s and three accelerometers (x,y,z), acceleration 100 g −11 ms, working temperature from −55 °C to + 95 °C, precision of accelerometer < 20 μg/°C, resolution 5 μg, bandwidth 150 Hz and a GPS antenna. 2.3. Data recording and analyses The measured data were recorded onto a hard drive and were immediately available for analyses. The point cloud was stored for further analysis. The data were processed using the Road Scanner ver.2 software developed by the Kvant corporation. Within each road section, the software recorded the following parameters: (i) the number of damages (potholes, cracks); (ii) the volume of individual damages (dm3); (iii) the area of individual damages (dm2); (iv) the mean and maximal depths of individual damages (cm). The software was set to register the damages which met following: minimal depth 2 mm, minimal diameter 50 mm and minimal area 100 mm2 with regards to the dimension of material used for road construction and to the time of data processing. We also calculated the total and median values (depth, area, and volume) of recorded damages in individual road sections. In case of the depth, the software calculated the mean depth of every individual damage (pothole/crack) and subsequently, we calculated median depths for particular road sections. We used Klč, Králik classification (Klč and Králik, 1991) of road damage to classify the state of each road section. This classification is standard in Slovakia and the Czech Republic (Table 2). Because the individual road sections were not equally long, we also calculated the damage values per 100 m. Thus, we could compare the
2.4. Comparing the results and test of the accuracy In March 2019, we performed the field measurements on the same part of the road for comparing the results with the data recorded by the scanning system. It was two and a half years later, but there was no maintenance performed on the road and the surface of the road did not change significantly. The measurements took place on sample plots, which were 10 m long and 4 m wide and we placed in randomly on individual road sections. We established 19 sample plots in total, in accordance of the length of the sections (3 for Cement, 3 for Geogrid, 2 for Asphalt and also for Lime, 5 for Aggregate, and 4 for Sand). We measured dimensions of individual damages, such as potholes, ruts, and cracks at the plots. Measurements were performed using a level and measuring tape. We identified the damages, measured their dimensions with an accuracy of one cm and depth with an accuracy of 0.5 cm. These data allowed us to estimate the area and volume of damages per plot and for the whole section. Volume and area per 100 m were also calculated in every section to compare them with the data recorded by the scanner. We also measured the average slope of the plot and 3
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Table 2 Classification of road damages for 1 km of road 3.5 m wide (Klč and Králik, 1991). Damage category
Status
Damaged area (%)
Total damage volume (m3)
Necessary treatment
I II III IV V
Very good Good Moderate damage Bad Severe
0–10% 11–30% 31–50% 51–70% 71% +
0–50 51–99 100–149 150–200 201+
Preventive care Periodical maintenance Maintenance/repair Repair Reconstruction
recorded the time consumption for measurements. We tested the accuracy of the Roadscanner software detection of individual damages, comparing the volume of the scanned transversal ditch (2 m clear section of 4 m total length). The ditch was steel, Ushaped, open-top prefabricate, easily identifiable both in data from road scanner and in terrain, close to measured road sections. We used the Roadscanner polygon measurement tool for manual selection the top edges of the ditch and the software calculated the characteristics of the selected area (2 m section of the ditch). We repeated these measurements 10 times to compute basic statistics (average volume and standard deviation). In order to obtain accurate and reliable reference data, we used point cloud created by close-range photogrammetry with ground control points measured in local coordinate system by universal total station Topcon GPT 3002 LN (Fig. 2).
Table 3 Distribution of the damages on individual road sections calculated per 100 m (Sc – data from scanner, Man – data from field measurements). Section
Cement Geogrid Asphalt Lime Aggregate Sand
Mean slope (%)
Total number of damages per 100 m
Total volume of damages per 100 m (m3)
Total area of damages (m2) per 100 m
Sc
Man
Sc
Man
Sc
Man
Sc
Man
9.0 10.7 8.2 7.7 15.4 10.2
10.8 9.8 8.3 8 14.3 10.6
139 452 231 161 327 146
23 20 20 20 24 28
0.49 2.41 0.96 0.34 1.14 0.49
3.27 2.27 1.14 4.87 2.84 2.17
13.78 58.51 22.12 11.71 26.41 14.44
148.5 142.9 94 168.4 127.8 90.3
3. Results The total volume of the damages on the selected part of the road was 9.75/27.61 m3 (scanning/manually), with a total area of 241.4/ 1277.8 m2. It represented 5.8/30.6% of the road surface (4176 m2). We found the geogrid and aggregate were the most damaged sections according to the scanned data and the lime and cement were the most damaged sections according to the manual measurements. In contrary, the lime and cement sections were the least damaged in case of the scanned data and the asphalt and sand were the least damaged in case of the manual measurements (Table 3). We noted significant differences in the recorded number of the damages between manual method and scanning.
Scanning the 1044 m long part of the road took 170 s, preparation of the system for measurements took half an hour. Automated data processing on the computer took 5 h. We found potholes were the most frequent type of recorded damage. Manual measurements of the same part of the road took 8 h, manual processing of the data took 1.5 h. Times for calculations of volumes and areas per 100 m and total volumes and areas were comparable in both methods. Costs per 1 km of the scanning were approximately ten times higher compared to manual measurements. We recorded ruts as the most frequent type of damage during manual measurements.
Fig. 2. Photogrammetric RGB colored point cloud with control points. 4
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Fig. 3. The median, 25–75% quantiles of the damage area, mean depth of damage and damage volume.
(Table 5). We analyzed the relationships between the road slope and other parameters of recorded damages (area, average depth, and volume) using the Spearman’s correlation. We found only weak correlations between the slope and damage area and between the slope and the average depth of the damage (Table 6). The average volume of the ditch computed from manually measured polygon in the Roadscanner software achieved a value of 9.698 ± 0.292 dm3, while reference data provided a volume of 10.369 dm3. The difference was 0.671 dm3 (−6.4%).
We used the data recorded by the scanning system for comparison of individual road sections. The median, 25 and 75% quartiles are presented in the picture (Fig. 3). We recorded the largest median area of individual damage on the cement (3.98 dm2) and sand (3.79 dm2) road sections. The lowest median values were recorded on lime (3.22 dm2) and milled asphalt (3.21 dm2) sections. The Kruskal-Wallis ANOVA did not confirm significant differences between the damaged areas (dm2) on individual road sections (p = 0.0555). We used the same tests for the depths of the individual damages on all road sections. The data did not fit the normal distribution according to the Shapiro-Wilk test. We observed the highest median depths of the potholes in case of the aggregate (3.66 cm) and the geogrid (3.53 cm) road sections. The lowest median values were recorded in the case of sand (1.65 cm) and lime (2.00 cm) sections (Fig. 3). There were significant differences in the depth of damages between the individual road sections (p = 0.000). We found the differences in median depth of damages between the individual road sections were statistically significant in almost all cases (Table 4). The deepest damages were found on the aggregate section (20.03 cm), milled asphalt (19.03 cm), followed by the cement (18.5 cm), geogrid (17.35 cm), sand (17.2 cm), and lime (9.16 cm). We observed the highest median volume (dm3) of potholes (Fig. 3) on the aggregate (1.25 dm3) and geogrid (1.25 dm3) sections. The lowest median values we recorded in the case of the sand (0.70 dm3) and the lime (0.74 dm3). The Kruskal-Wallis analysis showed significant (p = 0.000) differences between the damage volumes recorded on individual road sections. We found significant differences mostly in the case of sand against all other sections, with the exception of the lime section
4. Discussion Laser profilometry allowed us to compare the damage caused by haulage trucks to various types of road constructions. Aerial photography, made by unmanned aerial systems (Hrůza et al., 2016) allowed the authors to calculate the volume of the damages in the test section of an asphalt paved road. The total volume of material needed to repair a 500 m long road section was 40.46 m3 (i.e. 80.92 m3 per 1 km). The drone was moving at a speed of 1 m s−1 and the measurement of the road section took about 9 min (18 min per 1 km). We found the scanning method of data collection as more efficient (less than 3 min per 1 km). Automated data processing took 5 h, but it needed only manual setting of the threshold dimensions and it runs independently (for example during the night). Manual measurements of 1 km took one whole shift per 2 persons. In our case, both methods, based on the total volume of damages classified the road to the first category; i. e. the road was in good shape and needed only preventive maintenance (Klč and Králik, 1991). Total amounts of the damages calculated per 100 m of road sections showed significant differences among the sections and also between the 5
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Table 4 Multiple comparisons of mean ranks (average depth of individual damage) for all road sections (significant differences are boldfaced). Average depth
Multiple comparisons of p levels (average depth cm) Kruskal-Wallis test: H (5, N = 2591) = 195.35p = 0.000 Cement R:1133.2
Cement Geogrid Asphalt Lime Aggregate Sand
0.00 0.11 0.21 0.00 0.00
Geogrid R:1422.3
Asphalt R:1330.0
Lime R:835.91
Aggregate R:1418.6
Sand R:874.61
0.00
0.11 1.00
0.21 0.00 0.00
0.00 1.00 1.00 0.00
0.00 0.00 0.00 1.00 0.00
1.00 0.00 1.00 0.00
0.00 1.00 0.00
methods used for data collection (table 3). Scanning method recorded a huge number of smaller damages (potholes) and manually we recorded only a few damages, but with large dimensions (ruts) and no smaller damages. Roads with closed crown, improved with lime and/or cement were less damaged, compared to the sections crowned with coarse-grained material according to the scanned data. This finding is in accordance with the study by Marinello et al., (2017). But according to manual measurements, there were ruts driven through some sections. The largest ruts were present in the lime and cement sections, where we calculated the highest volume of damage (Table 3). We think these differences in results were caused by too low threshold values for damage registration which we set in the Kvant software. Then the software was not able to find huge damages with smooth changes of surface - typically the ruts. These ruts are visible in the 3D picture from the point cloud recorded by the scanner (Fig. 4). We estimated the damage incurred by the road sections through several factors and found no significant differences between the damaged areas of the sections. The differences in depths and volumes of damages between the individual road sections were statistically significant in almost all cases (tables 4 and 5). The maximal depths are important in case of potholes, because even one pothole deeper than the ground clearance of the vehicle used may completely block the road. We recorded such depths on the milled asphalt and aggregate sections. Furthermore, we found only weak correlations between the slope and the damaged area and between the slope and the mean depth of the damage on the observed road sections (Table 6). We consider the result of the comparison between two measurement methods on the transversal ditch as sufficient for forestry practice, however, the accuracy could be improved using standard geometrical objects (sphere or block) for analyses. Precision forestry, as defined by Taylor et al., (2002) requires an application of modern and efficient methods to determine the state of the forests and their parts (like the forest road network) to enable efficient forest management. Traditional terrestrial profiling methods require a lot of time and resources of qualified personnel to render
0.00 1.00
0.00
Table 6 Spearman's rank correlation between particular variables (boldfaced correlations are significant at the p < 0,05 confidence level).
Slope
Area
Average depth
Pothole volume
−0.06
0.14
0.04
acceptable results. Traditional means used for road surface measurements such as level bars, measuring tapes and other manual tools do not provide a level of accuracy and data density comparable to vehicle laser profilometry. Manual measurements also require a considerable time and restriction of traffic on the assessed road part (Knyaz and Chibunichev, 2016). On the other hand, Vehicle laser profilometry, such as we used, required only a single operator (who also drives the vehicle, on which the device is mounted) during the measurement phase and the data evaluation process was automated to a certain degree through the software. The method was also time-efficient, because the device recorded data rapidly, compared to other methods. The time consumption depended on the vehicle speed, whereas the accuracy of scanning did not suffer at all. Vehicle laser profilometry used in our study provided a high-density point cloud of the transversal profiles (up to 9600 points/profile). Crack and pothole detection rate then depended on the software settings (e.g. the minimal area and/or depth of damage to be considered a pothole, crack, etc.). As a modern method of data collection, it was suitable for a precise evaluation of the road surface parameters. Tomiyama et al., (2012) consider surface roughness of a pavement an important parameter, vital for determining the maintenance and rehabilitation needs of a road (or a whole road network). A reliable roughness survey method requires an objective and repeatable profile measurement, often called “profiling”. Another advantage of the method was the contactless measurement, independent of weather conditions (except for snow and ice directly on the road) and processing of the data in the office. Parameter settings (the smallest recorded depth, area, and volume of what will be considered damage) allows increasing effectiveness and objectivity of the measurement and is the. The method was sensitive to various objects on the road surface (fallen branches, leaves, snow, etc.). In these cases, it
Table 5 Multiple comparisons of mean ranks (volume of individual damage) for all road sections (significant differences are boldfaced). Damage volume
Multiple comparisons of p levels (damage volume dm3) Kruskal-Wallis test: H (5, N = 2591) = 57.25230p = 0.0000 Cement R:1289.3
Cement Geogrid Asphalt Lime Aggregate Sand
1.00 1.00 0.16 1.00 0.00
Geogrid R:1391.1
Asphalt R:1301.4
Lime R:980.11
Aggregate R:1344.9
Sand R:1059.6
1.00
1.00 1.00
0.16 0.01 0.16
1.00 1.00 1.00 0.02
0.00 0.00 0.01 1.00 0.00
1.00 0.01 1.00 0.00
0.16 1.00 0.01
6
0.02 1.00
0.00
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Fig. 4. 3D visualization of point cloud recorded at 5 m long part of the asphalt section.
was necessary to check the road surface from the synchronized recording of the vehicle-mounted camera. Profilometry scanning technology allows optimizing the road maintenance, to measure the volume of material necessary to fill the potholes and the calculation of the cost. Choi et al., (2016) presented this method as sufficiently precise (86.4% detection rate) for detecting surface cracks in paved roads. In the case of scanning the roads with an uneven surface, the key factor is the correct setting of threshold parameters for minimal registered damage as we mentioned before. There is also the benefit of having a permanent record of the damage, which can be retrieved and compared with future scanned data to quantify the rate of deterioration - temporal effects (Chen et al., 2013).
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Funding This research was funded by projects APVV-18-0305, VEGA 1/ 0471/17 and VEGA 1/0868/18. Acknowledgments We gratefully thank the KVANT s. r. o. company for providing us with the scanning device and access to their software for data processing. Declaration of Competing Interest The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. References Chambon, S., Moliard, J.M., 2011. Automatic road pavement assessment with image processing: review and comparison. Int. J. Geophys. 2011. https://doi.org/10.1155/ 2011/989354. Chen, S.-E., Wanqiu, L., Bian, H., Smith, B., 2013. 3D LiDAR scans for bridge damage evaluation. Forensic Eng. 487–495. https://doi.org/10.1061/9780784412640.052. Choi, J., Zhu, L., Kurosu, H., 2016. Detection of cracks in paved road surface using laser scan image data. In: International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences. Prague, pp. 559–562. https://doi.org/10.5194/ isprsarchives-XLI-B1-559-2016. Demir, M., 2007. Impacts, management and functional planning criterion of forest road network system in Turkey. Transp. Res. Part A Policy Pract. 41, 56–68. https://doi. org/10.1016/j.tra.2006.05.006.
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