Detecção de erosão em taludes baseada em deep learning

Abstract

The recent catastrophes triggered by the rupture of the Fundão and Córrego do Feijão dams caused around 300 deaths and countless irreparable socio-environmental damages. Since the use of more accurate monitoring systems and the proper execution of preventive and corrective maintenance would allow identifying, and even mitigating, the damage caused to society, it can be stated that there is a need for greater investment and incentive to create solutions of Structural Health Monitoring (SHM) capable of diagnosing occurrences that compromise the most crucial civil structures, such as bridges, buildings, dams and slopes. High-performance Artificial Intelligence (AI) techniques have been able to solve these structural analysis problems and presented superior results to previous solutions, their use has increased dramatically in the most diverse (SHM) scenarios. When it comes to image analysis and classification solutions, Convolutional Neural Network (CNN) is the type of neural network that delivers the best results. Therefore, this dissertation will describe the development process of a CNN with three convolutional layers that combines the use of the most consolidated technologies in the current scenario of computer vision, such as the Adam optimizer and batch normalization. The proposed CNN was trained with a database set up specifically for this dissertation, consisting of images of public work reports made by the Brazilian government, portfolios of companies that work with construction and maintenance of slopes and reports on landslides and/or catastrophes. These images were labeled, according to the context of each one of them, as stable or instable slopes. The results obtained were quite satisfactory, presenting an accuracy of 96.67% and proving that this solution is capable of identifying in a precise and improved way the instability indicators presented by the analyzed slopes, allowing a more adequate planning of the maintenance for each case, in the prevention of possible disasters, more efficient manpower management, cost reduction, greater safety and structural health to ensure its long-term integrity.