Hybrid neural network method for damage localization in structural health monitoring.

The detection of cracks in large structures is of critical importance, as such damage can result not only in significant financial costs but also pose serious risks to public safety. Many existing methods for crack detection rely on deep learning algorithms or traditional approaches that typically use image data. In this study, however, we explore an innovative approach based on numerical data, which is characterized by greater cost efficiency and offers intriguing research implications.

Unveiling soil thermal behavior under ultra-high voltage power cable operations.

The optimal operation of high-voltage underground power cables is crucial for powering our communities, and it hinges on the intricate dynamics of insulation temperature around the conductor, primarily influenced by joule heating. This temperature responsiveness is further molded by seasonal and diurnal fluctuations in power demand, as well as the moisture content in the surrounding soil. Past research concentrated on theoretical analyses and experiments under dry conditions, but our study expands this scope.

Deep neural networks for crack detection inside structures.

Crack detection is a long-standing topic in structural health monitoring. Conventional damage detection techniques rely on intensive, time-consuming, resource-intensive intervention. The current trend of crack detection emphasizes using deep neural networks to build an automated pipeline from measured signals to damaged areas.