This document summarizes research on using remote sensing techniques like photogrammetry and digital cameras to categorize and analyze surface deteriorations on structures. Key points: - Visual inspections are subjective and limited by access. Remote sensing allows comprehensive inspections. - A concrete block wall was constructed with cracks and biological crusts added. Photos were taken at varying distances and magnifications. - Image analysis software classified deteriorations in photos. Regression analysis calculated relationships between deterioration area and distance/magnification. - Calculations determine the field of view and image scale at different camera settings to allow calculating deterioration sizes from pixel counts.

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)
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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. 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
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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. 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. 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. 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
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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. 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
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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. 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
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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. 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
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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