Localized identification of seepage and ponding in earthen embankment using infrared thermography assimilated with different deep learning frameworks

Earthen embankments are built to prevent flooding and protect communities from the dangers
of floods and high water levels. However, these geotechnical structures may not always remain
serviceable and can fail due to long-term seepage and ponding. For instance, erosion causes the
earthen structure to weaken and eventually fail, which may be due to several factors, including the
velocity of the water, soil water characteristics, fine content, and gradation of the soil. The presented
research explores an advanced approach to address the critical issue of identifying the seepage and
ponding through the embankment by assimilating the passive infrared thermographic imageries
with Deep Learning (DL) algorithms. To facilitate the development and validation of developed DL
frameworks, a physical experimentation setup at the model scale is developed. This platform enabled
the generation of a comprehensive dataset of thermal images across various environmental scenarios,
including vegetation coverage and rainfall. Multiple DL frameworks were initially explored within the
framework and the models were designed to process sequences of thermal images and predict the
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extent of seepage and ponding. This research builds upon effectively transforming the complex task
of embankment leakage identification into an image classification problem. Moreover, the developed
framework demonstrates that mapping of seepage and ponding can be achieved with great accuracy
and is vital in enhancing embankment safety and disaster prevention strategies in flood-prone areas.