بررسی قابلیت دستگاه win area در دانه‌بندی خودکار سنگفرش بیابان

نویسندگان

1 دانشکده کشاورزی و منابع طبیعی دانشگاه اردکان

2 گروه طبیعت دانشکده کشاورزی و منابع طبیعی دانشگاه اردکان

3 گروه مدیریت مناطق خشک و بیابانی دانشکده منابع طبیعی و کویر‌شناسی دانشگاه یزد

‎10.22052/deej.2021.10.33.59

چکیده

دانه‌بندی یکی از ویژگی‌های ذرات رسوبی است که راهنمایی برای تعیین منبع رسوب و زمان حمل می‌باشد. روش‌های گوناگون سنتی و مدرن برای دانه‌بندی ذرات به کار برده می‌شود و با توجه به اینکه روش‌های سنتی، روشی وقت‌گیر است ضرورت استفاده از این روش احساس می‌شود. هدف از این تحقیق بررسی امکان استفاده از دستگاه Win Area در دانه‌بندی ذرات سنگ‌فرش بیابان است. بدین منظور در نقاط مختلف در حوضۀ دشت یزد- اردکان در بازۀ 10 روزه در سال 1399، به‌صورت تصادفی و با استفاده از پلات‌های مربعی شکل 40×40 از سنگ‌فرش نمونه‌برداری شد و نمونه‌ها با استفاده از الک مکانیکی دانه‌بندی شد. همچنین نمونه‌ها به روی دستگاه مذکور قرار داده شد و از آن‌ها عکس‌برداری صورت گرفت و برای تحلیل داده‌های حاصل از نرم‌افزار‌های پردازش تصویر از نرم‌افزارهای GRADISTAT، IBM.SPSS.Statistics-22، Micro soft office Excel 2013 استفاده شد و سپس نمودار دانه‌بندی رسم گردید. نتایج نشان داد که تقریباً تمامی نمودارها با یکدیگر همبستگی دارند، که همبستگی بین نمودار دانه‌بندی الک مکانیکی با عرض ذرات بیشتر می‌باشد و منحنی دانه‌بندی رسم‌شده از نتایج الک مکانیکی با نتایج به‌دست‌آمده از دستگاه Win Area مشابه بوده و منحنی‌های رسم‌شده تقریباً بر هم منطبق است. در سطح اعتماد 95%، ضریب همبستگی بین این دو روش 91/0 به دست آمد. آزمون پیرسون حاصل از وارد‌سازی درصد فراوانی تجمعی طول، عرض و قطر ذرات نشان‌دهندۀ این است که در نمونه‌های شمارۀ 1، 2، 3، 4 و 5 نمودار دانه‌بندی الک مکانیکی تشابه بیشتری با نمودار دانه‌بندی عرض ذرات دارد. در نمونه‌هایی که دارای قطر متوسط بالاتری هستند، بین مقادیر دانه‌بندی از الک مکانیکی با دانه‌بندی عرض و طول ذرات، تشابه وجود دارد. استفاده از دستگاه Win Area با توجه به اینکه سریع‌تر و کم‌هزینه‌تر است می‌تواند جایگزین مناسبی برای روش‌های سنتی دانه‌بندی خاک باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Investigation on the Capability of the Win Area Device in Automatic Granulation of Desert Pavement

نویسندگان [English]

  • Farnaz KHosravi 1
  • Mahdi Tazeh 2
  • Mohammad Ali Saremi Naeini 3
  • Saeideh Kalantari 2
1
2
3
چکیده [English]

Introduction: Pediments are vitally important geomorphological units. Desert pavement feature is used for their classification. The characteristics of desert pavements are a function of geomorphological conditions and have unique properties in terms of different granulation parameters. A variety of methods are employed to determine granulation. The most common method is the sieving test, in which the particle size distribution curve is obtained using the cumulative weight of the grains passing through the sieve. Using image processing methods facilitates the identification, measurement, analysis, and spatial distribution of particles. The present study aims to prove that digital image processing is a viable alternative for traditional methods as the results from both procedures are similar.

Methodology: Yazd-Ardakan plain is located in the range of 15 53 to 50-54 easts and in 31-15 to 45 45 north. In this study, ten points were randomly selected in 40 40 40 square plots in plain areas in Yazd-Ardakan plain basin. Sediment granulation was done by the mechanical sieving method. In order to granulate sediments, the samples were taken and transferred to the laboratory, and placed in a shaker to separate the particles according to their large diameter size by the sieves in the machine. After performing the calculations via Excel software, a graph related to the granulation of each point was achieved. WinArea-UT-11 can measure most physical coefficients of products such as perimeter, area, and the largest and smallest particle diameters. The parameters measured by the device were particles’ length and width.

Results and discussion: In this section, the granulation results by mechanical sieving as well as the results obtained from the Win Area machine are presented. The following table, Table 1, is an example of calculating the weight percent and the percentage of cumulative frequency of particles in a mechanical sieve.

Table (1): Weight percentage and cumulative frequency percentage of particle diameter in mechanical sieving




Particle diameter (mm)
Particle weight percentage
Cumulative percentage of particles


2
14.97
14.97


4.76
31.75
46.73


9.525
12.46
59.19


12.7
30.35
89.54


19.05
10.45
100





Sizing by mechanical sieving was followed by placing the collected samples on the Win Area device, and photography was performed. The information obtained from the pebbles was saved in Excel software after shooting. A rectangular shape for each pebble is assumed. According to the length and width of the pebble device, the Pythagorean equation was used to calculate the particle diameter.

Table (2): Weight and cumulative percentage of length, width and diameter of the machine using sieve diameter




Diameter (mm)
Frequency percentage
Cumulative frequency percentage
Frequency percentage of device length
Percent cumulative frequency of length
Frequency percentage of device width

Percent cumulative frequency of width

Percentage of frequency of device diameter
Percentage of cumulative frequency of device diameter


2
14.97
14.97
0
0
0
0
0
0


4.76
31.75
46.73
0
0
2.33
2.33
0
0


9.525
12.46
59.19
9.62
9.62
53.35
55.68
1.16
1.16


12.7
30.35
89.54
27.11
63.73
32.36
88.04
20.99
22.15


19.05
10.45
100
32.65
69.38
8.16
96.2
37.9
60.05


25.4
0
100
18.07
87.46
2.91
99.19
20.69
80.75


38.1
0
100
11.37
98.83
0.58
99.7
17.92
98.68


50.8
0
100
0.58
99.41
0.29
100
1.16
99.85


63.5
0
100
0.29
99.7
0
100
0.29
100


80
0
100
0.29
100
0
100
0
100




According to the results obtained from the Pearson test in SPSS software, the correlation between the results of the cumulative frequency percentage of the parameters of length, width, and diameter of particles with the percentage of cumulative frequency calculated by the particles’ diameter in the mechanical sieve is above 70%. Additionally, the mean level particle content in all three parameters of particles’ length, width, and diameter is less than 0.05.
Conclusion: The results of the granulation diagram of each of the parameters of particles’ length, width, and diameter visually show that the graphs are almost correlated and coordinated with each other. Among these, the correlation between the mechanical sieve granulation diagram and the particles’ width is more consistent than the particles’ length and diameter granulation diagram. According to the grading results of each parameter in comparison with the mechanical sieving method, if a few number of grading diagrams of length, width, and diameter are shifted, the diagrams will completely match. Pearson test, obtained by importing the percentage of cumulative frequency of particles’ length, width, and diameter, shows that in samples 1, 2, 3, 4, and 5, which are related to the pediments, the grading diagram of a mechanical sieve is more similar to Particles’ width grading diagram. In samples with higher mean diameters, the similarities between the grading values of the mechanical sieve are approximately the same as the granulation of particles’ width and length.

Table (3): Pearson correlation results at 95% confidence level Granulation of particle length, width and diameter by mechanical sieving




pediment
points
Results of length correlation with sieve
Results of width correlation with sieve
Results of diameter correlation with sieve


epandage
1
0.88
0.96
0.83


2
0.81
0.91
0.74


3
0.84
0.95
0.76


4
0.85
0.79
0.76


5
0.98
0.94
0.96


mean
0.87
0.91
0.81


bare
6
0.98
0.94
0.96


7
0.99
0.96
0.97


8
0.89
0.73
0.92


9
0.95
0.96
0.93


10
0.95
0.97
0.91


mean
0.95
0.91
0.93




This study illustrates the similarity between the grading curve drawn from the mechanical sieving and the results obtained with the win area machine, and the drawn curves are visually matched with some shifts. Also, according to the results of comparing image processing methods with traditional methods, it is found that there is a significant correlation between the two methods, meaning that the results of digital image processing methods are similar to conventional methods. Therefore, the digital image processing method can perfectly replace the traditional methods.

کلیدواژه‌ها [English]

  • Granulation
  • Mechanical Sieving
  • Geomorphology
  • Particle Size Distribution
  • Win Area Device
  1. Afrasiabi, S., Tazeh, M., Taghizadeh, R., Ghaneei, MJ. and Kalantari, S., 2019. Performance of two measurement methods of pin meter and laser disto meter in the measurement of microtopography Created by desert pavement. Desert Ecosystem Engineering, 8, 1-14. (in Persian)
  2. Azad, MR., Kalantari, , Shirmardi, M. and Tazeh, M., 2021. Investigation of Land use and Physico-chemical properties of soil on wind erosion threshold velocities using data mining. DesertEcosystemEngineering, 9, 1-14. (in Persian)
  3. Al-Farraj, A. and Harvey, A.M., 2000. Desert pavement characteristics on wadi terrace and alluvial fan surfaces. Wadi Al-Bih, 1 1.
  4. Beggan, C. and Hamilton, C.W., 2010. New image processing software for analyzing object size-frequency distributions, geometry, orientation, and spatial distribution. Computers & Geosciences, 36(4), 539-549.
  5. Chang, F.J. and Chung. Ch.H., 2012. Estimation of riverbed grain-size distribution using image-processing techniques. Journal of Hydrology, 440-441, 102-112.
  6. Cheng, Z. and Liu, H., 2015. Digital grain-size analysis based on autocorrelation algorithm. Sedimentary Geology, 327, 21–31
  7. Chung, Ch.H. and Chang, F.J., 2013. A refined automated grain sizing method for estimating river-bed grain size distribution of digital images. Hydrology, 486, 224-233.
  8. Fathizad, H., Tazeh, M. and Kalantari, S., 2016. Assessment of pixel-based classification (Artmap Fuzzy Neural Networks and Decision Tree) and object-oriented methods for land use mapping (Case Study: Meymeh, Ilam Province). Arid Biome, 5, 69-81. (in Persian)
  9. Fathizad, H., Tazeh, M., Kalantari, S. and Shojaei, S., 2017. The investigation of spatiotemporal variations of land surface temperature based on land use changes using NDVI in southwest of Iran. Journal of African Earth Sciences, 134, 249-256. (in Persian)
  10. Han, j., Wang, k., Wangc, X. and Paulo, J.M., 2016. 2D image analysis method for evaluating coarse aggregate characteristic and distribution in concrete. Construction and Building Materials,127, 30–42.
  11. Hesp, P., 2002. Fore dunes and blowouts: Initiation, geomorphology and dynamics. Geomorphology, 48, 245–268.
  12. Kargaran, F., Kalantari, S., Ghaneei, MJ. and Tazeh, M., 2017. The Compare of grading criteria in Coarse ripple Mark on the windward and leeward slopes (Case Study: Hassan Abad erg in Bafg), quantitative geomorphological research, 4, 134-144. (in Persian)
  13. Khosravi, F., Tazeh, M., Saremi naeini, Ma. and Kalantari, S., 2020. Evaluation and comparison of Image J and GIAS softwares with mechanical sieving in automatic particle-size distributions. Arid Biome, 9(2), 29-42. (in Persian)
  14. Nourzadeh Haddad, M. and Bahrami, H., 2015. Investigation of the relationship between fine dust concentration and surface moisture and soil particle size distribution using a mobile wind erosion simulator in the desert areas of western Khuzestan province. Geographical explorations of desert areas, 3(1), 60-72. (in Persian)
  15. Penders, C.A., 2010. Determining mean grain-size in high gradient streams with autocorrelative digital image processing. Master of Science Thesis, Appalachian State University, Boone, North Carolina, United States.
  16. Storm, K.B., Kuhns, R.D. and Lucas, H.J., 2010. Comparison of automated image-based grain sizing to standard pebble-count methods. Journal of Hydraulic Engineering, 136(8), 461–473.