International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online), Vo...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online), Vo...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online), Vo...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976
ISSN 0976 – 6316(Online), Volume 5, Issue 4,...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online), Vo...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976
ISSN 0976 – 6316(Online), Volume 5, Issue 4,...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online), Vo...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online), Vo...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online), Vo...
Upcoming SlideShare
Loading in...5
×

20320140504005

60

Published on

Published in: Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
60
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
2
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Transcript of "20320140504005"

  1. 1. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online), Volume 5, Issue 4, April (2014), pp. 48-56 © IAEME 48 CATEGORIZATION AND ANALYSIS OF SURFACE DETERIORATIONS IN STRUCTURES USING REMOTE SENSING TECHNIQUES 1 Ahmad Areeb Anwarul Haque, 2 Chitransh Saxena, 3 Sravan Chitaparthi 1,2 (VIIIth sem, B-tech, Civil Engineering, SRM University, Kattankulathur, India) 3 (Assistant Professor, Civil Engineering, SRM University, Kattankulathur, India) ABSTRACT Visual inspection by human inspectors is one among the most powerful and versatile non destructive tests and it is the first step in the evaluation of any structure. Effectiveness of visual inspection depends on the knowledge and the experience of the investigator. This approach presents some problems. The presence of damage is not quantitatively standardized and depends on the inspector’s qualitative criteria. Productivity is low because the inspectors typically use paper sheets in the field that are digitized afterwards in the office. Sometimes, the inspectors must work at heights and sometimes, the structures are not easily accessible, so it is not possible to perform the correct inspection of the structure. Most often, the inspection results would be subjective which calls for adopting advanced non-contact (non- destructive) surveying techniques along with rigorous scientific analysis methods to obtain complete knowledge of the current state of the structures. For this purpose some latest advancements in this field is already in use, like laser terrestrial scanner, but these devices are not affordable by all. Hence, in this study an attempt has been made for extracting information on the presence of surface deterioration on structures using the combined effect of photogrammetry and remote sensing techniques, using a digital camera. This method is a non-destructive and non-invasive technique that whose use has expanded greatly in recent years in the field of graphic and metric documentation of objects in which no direct contact is involved. Keywords: Surface Deteriorations, Camera, Analysis, Area, Categorization, Structures, Remote Sensing Techniques, ERDAS Imagine. INTERNATIONAL JOURNAL OF CIVIL ENGINEERING AND TECHNOLOGY (IJCIET) ISSN 0976 – 6308 (Print) ISSN 0976 – 6316(Online) Volume 5, Issue 4, April (2014), pp. 48-56 © IAEME: www.iaeme.com/ijciet.asp Journal Impact Factor (2014): 7.9290 (Calculated by GISI) www.jifactor.com IJCIET ©IAEME
  2. 2. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online), Volume 5, Issue 4, April (2014), pp. 48-56 © IAEME 49 1. INTRODUCTION 1.1. GENERAL Tremendous numbers of structures have been built in recent years throughout the world. These structures are important properties to the people as far as they are used by them. Although the number of the structures is tremendous, most of these structures are not always being maintained well by the public. Role of a civil engineer is not completed by just construction of a structure but further periodic maintenance is always needed. For proper maintenance, one should monitor them periodically. Visual inspection is the first step in monitoring of any structure. This is done by human inspectors and is one among the most powerful and versatile non destructive tests. Visual inspection can provide a wealth of information that may lead to positive identification of the cause of observed distress. Effectiveness of visual inspection depends on the knowledge and the experience of the investigator. This approach presents some problems. The presence of damage is not quantitatively standardized and depends on the inspector’s qualitative criteria. Productivity is low because the inspectors typically use paper sheets in the field that are digitized afterwards in the office. Sometimes, the inspectors must work at heights and sometimes, the structures are not easily accessible, so it is not always possible to inspect 100% of all areas in a building within a reasonable period of time and perform a correct inspection of the structure. Most often, the inspection results would be subjective which calls for adopting advanced non- contact (non-destructive) surveying techniques along with rigorous scientific analysis methods to obtain complete knowledge of the current status of the structures which are inaccessible. 1.2. LITERATURE REVIEW Sometimes, the inspectors must work at heights and sometimes, the structures are not easily accessible, so it is not possible to perform the correct inspection of the structure. Most often, the inspection results would be subjective which calls for adopting advanced non-contact (non- destructive) surveying techniques along with rigorous scientific analysis methods to obtain complete knowledge of the current state of the structures. Using the methodology of • Gonzalez et al., 2009; Guidia et al., 2004; Lamberts et al., 2007; Sharaf et al., 2009), civil engineering (Gonzalez et al., 2008), geology (Buckley et al., 2008) and geomorphological analysis (Armesto, et al., 2009), (Guirant et al., 2000; Langer et al., 2000; Lichitti et al., 2005; Rodriguez et al., 2010). • RAAJ and Sravan (2013) made an attempt to extract information on the presence of biological crusts on concrete structures using Terrestrial laser scanning (TLS) intensity data. Using the same methodology the project is planned to do this study by taking photographs using high resolution cameras. 2. CATEGORIZING AND ANALYZING DETERIORATION ON SOLID BLOCK WALL 2.1. CONSTRUCTION OF SOLID BLOCK WALL As the project is related to the existing structures, the grade of concrete is assumed to be M35, which is the highest grade of concrete that can be used in the construction of many commonly seen structures, thus the concrete cubes in order to construct the wall for the experiment were made of M35 grade design mix. After the making and curing of the concrete blocks, wall out of these blocks were made of the specification 1.23 m x 0.7 m. After the formation of the wall, certain deteriorations, like cracks and biological crust, were inculcated.
  3. 3. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online), Volume 5, Issue 4, April (2014), pp. 48-56 © IAEME 50 2.2. TAKING THE PHOTOGRAPH The photographs of the wall was taken in such a way that the distance was kept equal to the magnification (photograph at 1m with 1x zoom, 2m at 2x zoom and so on). During the analysis, the results were not obtained because of this redundancy in the values. In order to avoid redundancy in the equation that ought to be procured by the analysis of the photograph, the images were taken at random distance and also a random magnification so that any of the distance or magnification do not repeat. One of the pictures taken for the wall is given in Fig. 2.1. Fig. 2.1: Image at 3m distance at 1x zoom 2.3. CLASSIFICATION OF SURFACE DETERIORATIONS From the image above, the subset of the image, which is shown in fig.2.2, was obtained which included the wall along with the deteriorations. The classification was done on the image along with the change detection using ERDAS Imagine and the results that was obtained by this process is shown in fig.2.3. Fig. 2.2: Subset of the Image taken at 3 m distance, 1x zoom
  4. 4. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 ISSN 0976 – 6316(Online), Volume 5, Issue 4, April (2014), pp. Fig. 2.3: Classification of deterioration for 3 m distance, 1x zoom It has been shown in fig.2.3, the various deteriorations present on the wall at the distance of 3m and the magnification of 1x. 2.4. ANALYSIS OF SURFACE DETERIORATION The analysis of deterioration on solid concrete block wall equation which was obtained using the method of least squares method. Table 2.1 shows the number of pixels in deteriorations at various distance and magnifications. Table 2.1: Number of pixels in deteriorations at various distances and magnifications Magnification (nx) 1 4 6 9 11 14 16 12 8 International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 6316(Online), Volume 5, Issue 4, April (2014), pp. 48-56 © IAEME 51 Classification of deterioration for 3 m distance, 1x zoom , the various deteriorations present on the wall at the distance of 3m and the magnification of 1x. DETERIORATION The analysis of deterioration on solid concrete block wall was done by forming a regression using the method of least squares and bilinear quadratic equation Table 2.1 shows the number of pixels in deteriorations at various distance and ls in deteriorations at various distances and magnifications Distance between the wall and observer (m) Number of pixels in deteriorations 2 42186 5 46002 7 53364 10 44961 13 47663 15 62107 17 54756 20 20575 9 54618 International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), Classification of deterioration for 3 m distance, 1x zoom , the various deteriorations present on the wall at the distance of was done by forming a regression and bilinear quadratic equation Table 2.1 shows the number of pixels in deteriorations at various distance and ls in deteriorations at various distances and magnifications
  5. 5. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online), Volume 5, Issue 4, April (2014), pp. 48-56 © IAEME 52 2.4.1. Method of least squares Solving for the regression equation by the method of least squares for surface deterioration of solid block wall, we obtain the following equation ‫ݖ‬ ൌ 4592.07‫ݔ‬ െ 3740.756‫ݕ‬ ൅ 46661.028 (2.1) Checking the equation by substituting x = 16, y = 17 ‫ݖ‬ ൌ 56541.246 When we analyzed with software value for 17 m, 16x zoom ‫ݖ‬ ൌ 54756 Percentage difference of surface deterioration = ሺ ହ଺ହସଵ.ଶସ଺ିହସ଻ହ଺ ହସ଻ହ଺ ሻ ൈ 100 = 3.2 % 2.4.2. Bilinear quadratic equation method Solving for the regression equation by the bilinear quadratic equation method for surface deterioration of solid block wall, we obtain the following equation ‫ݖ‬ ൌ െ186.8624‫ݔ‬ଶ ൅ 461.6557‫ݕݔ‬ െ 236.8800‫ݕ‬ଶ ൅ 45691.1652 (2.9) Checking the equation by substituting x = 16, y = 17 ‫ݖ‬ ൌ 54966.4 When we analyzed with software value for 17 m, 16x zoom ‫ݖ‬ ൌ 54756 Percentage difference of surface deterioration = ሺ ହସଽ଺଺.ସିହସ଻ହ଺ ହସ଻ହ଺ ሻ ൈ 100 = 0.38 % As the percentage difference for surface deterioration is minimum for the bilinear quadratic equation method, this method is preferred for the analysis of surface deteriorations. 3. CALCULATING THE AREA OF SURFACE DETERIORATIONS The calculation of size through the image is shown in Fig. 3.1. This equation was obtained by the Smithsonian Astrophysics Observatory in Harvard University for the calculation of distance from the observing point to the moon, using a telescope, with the known size of the moon. The same methodology is used for determining the size of the wall, with the known distance of the wall from the observer.
  6. 6. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 ISSN 0976 – 6316(Online), Volume 5, Issue 4, April (2014), pp. Fig. 3.1: Equation for obtaining distance with known size, Smithsonian Astrophysics Observatory, Harvard University The field of view is illustrated as shown in Fig. 3 Fig. 3.2: Ray Diagram for Image Formation in a Camera Where, F= focal length d= dimension of image frame S2= distance from the image to the camera lens S1= distance from lens to the α= angle of view/field of view International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 6316(Online), Volume 5, Issue 4, April (2014), pp. 48-56 © IAEME 53 Equation for obtaining distance with known size, through a telescope, according to Smithsonian Astrophysics Observatory, Harvard University s illustrated as shown in Fig. 3.2. Ray Diagram for Image Formation in a Camera d= dimension of image frame = distance from the image to the camera lens = distance from lens to the object = angle of view/field of view International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), through a telescope, according to
  7. 7. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online), Volume 5, Issue 4, April (2014), pp. 48-56 © IAEME 54 Derivation of formula tan ቀ ఈ ଶ ቁ ൌ ௗ ଶ ‫ݏ‬ଶ ൘ (3.1) ߙ ൌ 2 tanିଵ ݀/2‫ݏ‬ଶ (3.2) In order to get a sharp image, S2 should be equal to F Therefore, ߙ ൌ 2 tanିଵ ݀ 2‫ܨ‬ൗ (3.3) Camera Specification Camera magnification upto 16x Focal length → 24-384 mm Image frame= 36 mm ൈ 24 mm Increase in focal length per magnification = ሺ384 െ 24ሻ 16ൗ = 22.5 ݉݉ For, 1x → F= 24 ݉݉ 2x → F= 46.5 ݉݉ 3x → F= 69 ݉݉ Substituting the values in the above formula we get the field of view value as shown in Table 2.2. Table 2.2: Field of view values Focal Length (mm) 24 46.5 69 Horizontal (deg.) 73.7 42.7 29.24 Vertical (deg.) 53.1 28.9 19.7 Now, Angular Size = No. of pixels ൈ Image scale (degrees per pixel) In order to acquire image scale, the technical team of the Camera “Sony Dsc hx9v” and also the Smithsonian Astrophysics observatory (Harvard University) was contacted. Comparing the two responses with the available data, the image scale in degrees per pixel for different distances at 1x are found as shown in Table 2.3.
  8. 8. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online), Volume 5, Issue 4, April (2014), pp. 48-56 © IAEME 55 Table 2.3: Image Scale Value Distance (metre) Horizontal (degrees) Vertical (degrees) 3 m 0.045 0.040 5 m 0.120 0.100 6 m 0.038 0.030 8 m 0.070 0.060 Area calculations, General Equation. ஽ ௅೓ ௅ೡ⁄ ൌ ହ଻ ஺ (3.4) Where, D = Distance of observer from the structure ‫ܮ‬௩ = Vertical length of the wall ‫ܮ‬௛ = Horizontal length of the wall A = Angular size of the camera At 3m, Horizontal distance = ଷ ௅೓ ൌ ହ଻ ହଵଵൈ଴.଴ସହ = 1.21 m (3.5) Vertical distance = ଷ ௅ೡ ൌ ହ଻ ଶ଻଻ൈ଴.଴ସ଴ = 0.60 m (3.6) The Area of the solid block wall = 1.21 x 0.60 = 0.705 m2 At 6m, Horizontal distance = ଺ ௅೓ ൌ ହ଻ ଷ଴଺ൈ଴.଴ଷ଼ = 1.22 m (3.7) Vertical distance = ଺ ௅ೡ ൌ ହ଻ ଵ଻଴ൈ଴.଴ଷ଴ = 0.58 m (3.8) The Area of the solid block wall = 0.707 m2 Comparing the Area and the percentage, Total deteriorations as 0.16 m2 , Biological crust was found to be in 0.062 m2 and other deteriorations as 0.099 m2
  9. 9. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online), Volume 5, Issue 4, April (2014), pp. 48-56 © IAEME 56 4. CONCLUSION After taking the photographs of the constructed walls from different distances at different resolutions and analyzing through ERDAS IMAGINE it is possible to categorize the surface deteriorations on structures. It is also used to determine the exact areas of surface deteriorations on structures quantitatively. This calculation of area can aid structural engineers in designing appropriate rehabilitation techniques. The area of surface deteriorations on both brick and solid block wall was calculated for 1x zoom level at different distances using the methodology developed by Smithsonian Astrophysics observatory, Harvard University, the results obtained were accurate (99.3%). By using bilinear quadratic model it is found out that the error caused due to redundancy is eliminated (0.38%) when compared with linear regression equation. 5. REFERENCES Journal Papers [1] González-Jorge, H., Gonzalez-Aguilera, D., Rodriguez-Gonzalvez, P., Arias, P., (2012) Monitoring biological crusts in civil engineering structures using intensity data from terrestrial laser scanners, Construction and Building Materials, 31:119–128. [2] González-Aguilera, D., Gómez-Lahoz, J., Muñoz-Nieto, A., HerreroPascual, J., (2009) Monitoring the health of an emblematic monument from terrestrial laser scanner.Non-destruct Test Eval, 23:301–15. [3] Guidi, G., Beraldin, A., Atzeni, C., (2004) High-accuracy 3D modeling of cultural heritage: the digitizing of Donatello’s Maddalena. IEEE Trans Image Process, 13:370 80. [4] Ramsankaran, R., Sravan, C., (2013) Recognizing biological crusts in civil engineering structures using intensity data from terrestrial laser scanner, Indian Concrete Institute. [5] Peg Herlihy, (2009) From the Ground Up! , Smithsonian Astrophysics Observatory, Harvard University. IS Codes [6] IS: 1905 (1997), Code of practice for structural use of un-reinforced masonry. [7] IS: 10262 (2009), Recommended guidelines for concrete mix design. Web-page [8] http://www.cfa.harvard.edu/webscope/activities/pdfs/measureSize.PDF

×