<p>Sewer pipelines are an essential
component of wastewater infrastructure and serve as the primary means for
transporting wastewater to treatment plants. In the face of increasing demands
and declining budgets, municipalities across the US face unprecedented
challenges in maintaining current service levels of the 800,000 miles of public
sewer pipes. Inadequate maintenance of sewer pipes leads to inflow and
infiltration, sanitary sewer overflows, and sinkholes, which threaten human
health and are expensive to correct. Accurate condition information from sewers
is essential for planning maintenance, repair, and rehabilitation activities
and ensuring the longevity of sewer systems. Currently, this information is
obtained through visual closed-circuit television (CCTV) inspections and
deterioration modeling of sewer pipelines. CCTV inspection facilitates the
identification of defects in pipe walls whereas deterioration modeling
estimates the remaining service life of pipes based on their current condition.
However, both methods have drawbacks that limit their effective usage for sewer
condition assessment. For instance, CCTV inspections tend to be labor
intensive, costly, and time consuming, with the accuracy of collected data
depending on the operator’s experience and skill level. Current deterioration
modeling approaches are unable to incorporate spatial information about pipe
deterioration, such as the relative locations, densities, and clustering of
defects, which play a crucial role in pipe failure. This study attempts to
leverage recent advances in deep learning and data mining to address these
limitations of CCTV inspection and deterioration modeling and consists of three
objectives. </p>
<p> </p>
<p>The first objective of this study seeks to develop
algorithms for automated defect interpretation, to improve the speed and
consistency of sewer CCTV inspections. The development, calibration, and
testing of the algorithms in this study followed an iterative approach that
began with the development of a defect classification system using a 5-layer
convolutional neural network (CNN) and evolved into a two-step defect
classification and localization framework, which combines a the ResNet34 CNN
and Faster R-CNN object detection model. This study also demonstrates the use
of a feature visualization technique, called class activation mapping (CAM), as
a diagnostic tool to improve the accuracy of CNNs in defect classification
tasks—thereby representing a crucial first step in using CNN interpretation
techniques to develop improved models for sewer defect identification. </p>
<p> </p>
<p>Extending upon the development of automated defect
interpretation algorithms, the second objective of this study attempts to
facilitate autonomous navigation of sewer CCTV robots. To overcome Global
Positioning System (GPS) signal unavailability inside underground pipes, this
study developed a vision-based algorithm that combines deep learning-based
object detection with optical flow for estimating the orientation of sewer CCTV
cameras. This algorithm can enable inspection robots to estimate their
trajectories and make corrective actions while autonomously traversing pipes.
Hence, considered together, the first two objectives of this study pave the way for future
inspection technologies that combine automated defect interpretation with
autonomous navigation of sewer CCTV robots.</p>
<p> </p>
<p>The third and final objective of this study seeks to develop
a novel methodology that incorporates spatial information about defects (such
as their locations, densities, and co-occurrence characteristics) when
assessing sewer deterioration. A methodology called Defect Cluster Analysis
(DCA) was developed in order to mine sewer inspection reports and identify pipe
segments that contain clusters of defects (i.e., multiple defects in
proximity). Additionally, an approach to mine co-occurrence characteristics
among defects is also introduced (i.e., identification of defects which occur
frequently together). Together the two approaches (i.e., DCA and co-occurrence
mining) address a key limitation of existing deterioration modeling approaches
(i.e., the lack of consideration to spatial information about defects)—thereby
leading to the generation of new insights into pipeline rehabilitation
decision-making. </p>
<p> </p>
<p>The algorithms and approaches presented in this dissertation
have the potential to improve the speed, accuracy, and consistency of assessing
sewer pipeline deterioration, leading to better prioritization strategies for
maintenance, repair, and rehabilitation. The automated defect interpretation
algorithms proposed in this study can be used to assign the subjective and
error-prone task of defect identification to computer processes, thereby
enabling human operators to focus on decision-making aspects, such as deciding
whether to repair or rehabilitate a pipe. Automated interpretation of sewer
CCTV videos could also facilitate re-evaluation of historical sewer inspection
videos, which would be infeasible if performed manually. The information
gleaned from re-evaluating these videos could generate insights into pipe
deterioration, leading to improved deterioration models. The algorithms for
autonomous navigation could enable the development of completely autonomous
inspection platforms that utilize unmanned aerial vehicles (UAVs) or similar
technologies to facilitate rapid assessment of sewers. Furthermore, these
technologies could be integrated into wireless sensor networks, paving the way
for real-time condition monitoring of sewer infrastructure. The DCA approach
could be used as a diagnostic tool to identify specific sections in a pipeline
system that have a high propensity for failure due to the existence of multiple
defects in proximity. When combined with contextual information (e.g., soil
properties, water table levels, and presence of large trees), DCA could provide
insights about the likelihood of void formation due to sand infiltration. The
DCA approach could also be used to periodically determine how the distribution
of defects and their clustering progresses with time and when examined
alongside contextual data (e.g., soil properties, water table levels, presence
of trees) could reveal trends in pipeline deterioration. </p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12218432 |
| Date | 30 April 2020 |
| Creators | Srinath Shiv Kumar (8782508) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Leveraging_Big_Data_and_Deep_Learning_for_Economical_Condition_Assessment_of_Wastewater_Pipelines/12218432 |
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