Deep learning techniques have offered innovative and efficient tools for accurate and automated detection of sewer defects by leveraging large-scale sewer data and advanced feature learning algorithms. However, there has been a lack of thorough characterization of the geometric properties of segmented defects, let alone systematically calculate the severity level of sewer defects and quantitatively evaluate their impacts on flood conditions in hydrodynamic models. This study proposed a comprehensive framework and related metrics to accurately and automatically detect, segment, characterize, and evaluate the impacts of sewer defects on flooded nodes and volumes by integrating a DeepLabv3+-based segmentation technique, an automated geometric characterization and severity quantification module, and a GIS and SWMM-based hydrodynamic modeling. The results clearly showed in details where and how much the urban flooding was affected by the different defect types. The segmentation model achieved satisfactory detection performance, with mean pixel accuracy (MPA), mean intersection over union (MIoU), and frequency weighted intersection over union (FWIoU) of 0.99, 0.74 and 0.95, respectively. In terms of severity level quantification, there were 98%, 90%, 90% and 83% of predictions consistent with real conditions for falling off, obstacle, disjoint and leakage. It was shown that the number of surcharging manholes and total flood volume (TFV) were greatly affected by sewer defects, with over 16% increase in TFVs under all investigated rainfall events. The results addressed the impacts of sewer defects on urban flooding and demonstrated the powerful tools provided by the proposed framework for decision-making on sewer defect detection and management.
Keywords: Deep learning; Geometric quantification; SWMM flood modeling; Semantic segmentation; Sewer defect detection.
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