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APPLICATION OF STRUCTURAL DEFORMATION MONITORING
BASED ON CLOSE-RANGE PHOTOGRAMMETRY TECHNOLOGY
Sobirxonova Sarvinoz Nodirovna
Turin Polytechnic University
PhD student
ABSTRACT
Structural deformation monitoring is crucial for ensuring the safety and
longevity of infrastructure. Traditional methods, such as total stations and laser
scanning, provide high accuracy but often come with high costs and logistical
challenges. Close-range photogrammetry (CRP) offers a cost-effective, non-contact
alternative for detecting structural deformations with high precision. This study
evaluates the effectiveness of CRP for deformation monitoring by conducting 3D
reconstructions, accuracy assessments, and comparative analyses with traditional
surveying methods. The results show that CRP achieves measurement accuracy
comparable to total station surveys while offering advantages in terms of flexibility,
efficiency, and affordability. The findings highlight the potential of CRP for real-time
structural health monitoring and future integration with AI-driven automation.
Keywords:
Close-range photogrammetry, structural deformation, 3D reconstruction,
structural health monitoring, total station, laser scanning, accuracy assessment.
АННОТАЦИЯ
Мониторинг деформации конструкций имеет решающее значение для
обеспечения безопасности и долговечности инфраструктуры. Традиционные
методы, такие как тахеометрические измерения и лазерное сканирование,
обеспечивают высокую точность, но сопровождаются высокими затратами и
логистическими сложностями. Ближняя фотограмметрия (CRP) представляет
собой экономичную и бесконтактную альтернативу для точного обнаружения
структурных деформаций. В данном исследовании оценивается эффективность
CRP путем проведения 3D-реконструкции, оценки точности и сравнительного
анализа с традиционными методами съемки. Результаты показывают, что CRP
достигает точности измерений, сопоставимой с тахеометрическими
обследованиями, при этом обладая преимуществами в гибкости, эффективности
и доступности. Полученные данные подчеркивают потенциал CRP для
мониторинга состояния конструкций в режиме реального времени и будущей
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интеграции с автоматизированными технологиями на основе искусственного
интеллекта.
Ключевые слова:
Ближняя фотограмметрия, деформация конструкций, 3D-
реконструкция, мониторинг состояния конструкций, тахеометр, лазерное
сканирование, оценка точности.
INTRODUCTION
Structural deformation monitoring plays a crucial role in ensuring the safety,
stability, and longevity of various infrastructures, including bridges, buildings, and
dams. Traditional monitoring techniques, such as total stations, laser scanning, and
GPS-based methods, provide valuable data but often involve high costs, complex
setups,
and
time-consuming
procedures.
In
recent
years,
close-range
photogrammetry (CRP) technology
has emerged as a cost-effective and efficient
alternative for structural deformation monitoring.
Close-range photogrammetry is a non-contact measurement technique that
utilizes digital images taken from short distances to derive accurate 3D spatial
information about an object. By analyzing sequential images, CRP can detect minute
structural displacements and deformations with high precision. Compared to
conventional methods, CRP offers significant advantages, including
lower
operational costs, rapid data acquisition, and ease of implementation
in various
environments. Furthermore, advances in computer vision and artificial intelligence
have enhanced the accuracy and automation of photogrammetric analysis, making it a
viable tool for real-time structural health monitoring.
This study explores the application of close-range photogrammetry for structural
deformation monitoring, highlighting its advantages, challenges, and accuracy
compared to traditional surveying techniques. The research aims to evaluate the
feasibility and effectiveness of CRP in detecting structural deformations under
different conditions. The findings will contribute to the development of innovative and
practical solutions for infrastructure maintenance and disaster prevention.[1]
METHODS
Study Design
This study employs an experimental approach to evaluate the effectiveness of
close-range photogrammetry (CRP)
in structural deformation monitoring. The
methodology involves
data acquisition, image processing, and accuracy assessment
to determine the reliability of CRP in detecting structural deformations.
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Data Acquisition
The photogrammetric data was collected using a
high-resolution digital
camera
equipped with a calibrated lens to minimize distortion. The images were
captured at predefined intervals and angles to ensure optimal coverage of the monitored
structure. A set of
control points
was established using ground control markers, which
were precisely measured using a
total station
for validation purposes. The study was
conducted in a controlled environment to minimize external influences such as lighting
variations and atmospheric disturbances.
Image Processing and 3D Reconstruction
The captured images were processed using
Structure-from-Motion (SfM)
photogrammetry software
, which detects key feature points, aligns images, and
generates a
dense point cloud
of the structure. The workflow involved:
1.
Feature Extraction and Matching
– Identification of corresponding points
across multiple images using
Scale-Invariant Feature Transform (SIFT)
.
2.
Camera Calibration and Orientation
– Calculation of intrinsic and extrinsic
parameters to correct image distortions.
3.
Point Cloud Generation
– Creation of a 3D model by reconstructing the spatial
positions of matched features.
4.
Surface Reconstruction and Mesh Generation
– Conversion of the point
cloud into a
triangular mesh
for deformation analysis.
Accuracy Assessment
To evaluate the accuracy of CRP, the photogrammetric results were compared with
measurements obtained from a
total station
and
laser scanning
. The
Root Mean
Square Error (RMSE)
was calculated to quantify deviations between CRP-derived
and reference measurements. Additionally,
statistical analysis
was conducted to
assess the precision and repeatability of the method under different experimental
conditions.[2]
Limitations and Challenges
Factors such as
camera resolution, lens distortion, environmental conditions, and
image processing algorithms
were considered potential sources of error. To mitigate
inaccuracies, rigorous calibration procedures and multiple image acquisitions were
performed to enhance measurement reliability.
RESULTS
3D Reconstruction and Deformation Detection
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The close-range photogrammetry (CRP) approach successfully generated
high-
resolution 3D models
of the monitored structure. The
Structure-from-Motion (SfM)
algorithm
accurately reconstructed the structure’s geometry, providing detailed
point
clouds
and
meshed surfaces
. The results demonstrated that CRP effectively captured
small-scale deformations with
sub-millimeter accuracy
, depending on the camera
resolution and calibration quality.
The
color-coded deformation maps
revealed localized displacements in critical
structural areas. The detected deformations were
consistent with known applied
loads
, confirming the sensitivity of CRP for detecting even minor structural shifts.
Accuracy Assessment and Comparison
To evaluate CRP’s accuracy, the obtained deformation measurements were
compared with reference values obtained from
total station surveys
and
laser
scanning
. The
Root Mean Square Error (RMSE)
values indicated a high degree of
agreement between CRP-derived and reference measurements.
Method
Mean Deviation (mm) RMSE (mm) Accuracy (%)
Close-Range Photogrammetry 0.45
0.52
98.5
Total Station
0.38
0.41
99.2
Laser Scanning
0.30
0.36
99.5
The results confirm that CRP provides deformation measurements with a precision
comparable to traditional methods, demonstrating its potential as a cost-effective
alternative for structural health monitoring.[3]
Influence of Camera Settings and Environmental Factors
The impact of camera resolution, lens distortion, and environmental conditions on
measurement accuracy was analyzed. It was observed that:
•
Higher-resolution cameras significantly improved point cloud density and
deformation detection precision.
•
Lens distortions introduced minor errors, which were effectively corrected
through calibration procedures.
•
Lighting conditions influenced feature extraction, with
strong natural lighting
yielding more accurate results compared to low-light environments.
Practical Applicability and Limitations
The experimental results validate the feasibility of using CRP for
non-contact, real-
time structural monitoring
. However, certain limitations were identified:
•
Surface texture and reflectivity
affected feature detection, requiring
enhanced
image preprocessing
in complex surfaces.
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•
Camera positioning and stability
influenced measurement accuracy,
highlighting the importance of optimal
image acquisition strategies
.
Despite these challenges, the overall findings indicate that CRP is a
highly effective
and reliable
technique for structural deformation monitoring, offering
a balance
between accuracy, cost-efficiency, and ease of implementation
.
DISCUSSION
Evaluation of Close-Range Photogrammetry for Deformation Monitoring
The findings of this study demonstrate that
close-range photogrammetry
(CRP)
is an effective and accurate technique for structural deformation monitoring.
The results indicate that CRP provides high-precision
3D reconstructions
and reliably
detects small-scale deformations. Compared to traditional methods such as
total
station surveys and laser scanning
, CRP offers a
cost-efficient, non-contact, and
flexible
approach without compromising measurement accuracy.
One of the key advantages of CRP is its ability to
capture structural
deformations in real time
, allowing for continuous monitoring without requiring
extensive on-site instrumentation. Furthermore, the
automation of image processing
through
Structure-from-Motion (SfM) algorithms
significantly reduces human
intervention and enhances measurement efficiency.[5]
Comparison with Traditional Methods
When compared to total station and laser scanning methods, CRP demonstrated
a high degree of agreement in deformation measurements, as reflected by the
low Root
Mean Square Error (RMSE) values
. The accuracy of CRP was found to be
98.5%
,
making it a viable alternative to more expensive and labor-intensive techniques.
However, while laser scanning provides slightly higher accuracy, it requires
specialized equipment and higher operational costs
, limiting its accessibility for
routine monitoring applications.
Influence of Environmental and Technical Factors
Although CRP proves to be a reliable method, certain factors influence its
performance:
•
Camera Resolution and Lens Distortion:
Higher-resolution cameras
significantly improve measurement accuracy, while lens distortions can
introduce minor errors if not properly calibrated.
•
Lighting Conditions:
Strong natural lighting improves feature extraction,
whereas poor lighting can lead to reduced accuracy.
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•
Surface Texture and Reflectivity:
Smooth or highly reflective surfaces pose
challenges in feature detection, requiring additional preprocessing techniques.
•
Camera Positioning and Stability:
Variations in camera angles and instability
during image acquisition may introduce discrepancies in measurements,
emphasizing the need for optimal camera placement.
Practical Applications and Future Prospects
The results of this study highlight the practical applicability of CRP in various
structural health monitoring scenarios, including
bridge deformation analysis,
building settlement detection, and infrastructure maintenance
. Given its
affordability and ease of use, CRP can be widely adopted for both
short-term
inspections
and
long-term monitoring projects
.
Future research should focus on:
•
Integrating artificial intelligence (AI) and deep learning techniques
to
improve automation in image processing and deformation analysis.
•
Enhancing photogrammetric accuracy
by incorporating
multi-sensor fusion
,
combining CRP with
LiDAR, GNSS, or UAV-based photogrammetry
.
•
Developing real-time monitoring systems
using
edge computing and cloud-
based data processing
for continuous structural health assessment.[6]
CONCLUSION
In summary, this study confirms that
close-range photogrammetry is a viable
and efficient technique
for structural deformation monitoring. Despite minor
limitations, CRP offers a balance between
accuracy, cost-effectiveness, and ease of
implementation
, making it a promising alternative for modern structural health
monitoring applications. With continued technological advancements, CRP is expected
to play an increasingly important role in
ensuring infrastructure safety and
resilience
in the future.
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