Leveraging Big Data and Deep Learning for Economical Condition Assessment of Wastewater Pipelines
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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.
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.
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.
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.
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.