Enhancing geotechnical damage detection with deep learning: a convolutional neural network approach.

ABSTRACT: Most natural disasters result from geodynamic events such as landslides and slope collapse. These failures cause catastrophes that directly impact the environment and cause financial and human losses. Visual inspection is the primary method for detecting failures in geotechnical structures, but on-site visits can be risky due to unstable soil. In addition, the body design and hostile and remote installation conditions make monitoring these structures inviable. When a fast and secure evaluation is required, analysis by computational methods becomes feasible. In this study, a convolutional neural network (CNN) approach to computer vision is applied to identify defects in the surface of geotechnical structures aided by unmanned aerial vehicle (UAV) and mobile devices, aiming to reduce the rel

SUBMITTER: de Araujo TMA 

PROVIDER: S-EPMC11419631 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature