Navegando por Assunto "Estruturas geotécnicas"

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Acesso aberto (Open Access)
Análise dos efeitos da detonação na estabilidade de talude em mina de ferro no Quadrilátero Ferrífero em Minas Gerais
(Universidade Federal do Pará, 2022-08-03) AZEVEDO, Daniel Prado; MARQUES, Eduardo Antonio Gomes; http://lattes.cnpq.br/6725413897416818; ALENCAR JÚNIOR, Júlio Augusto de; http://lattes.cnpq.br/3663658632717465
The vibrations induced by blasting in mining are finite waves that can disrupt geotechnical structures. For the present research, seismographs were installed in various positions along a slope with approximately two hundred meters of difference in height from the bottom of the pit to the top, in order to analyze the speed and acceleration of the particles at the moment of the wave transmission and their consequences to the stability of rocky mass. It is known that the course of these vibratory oscillations causes the particles to move and then return to the equilibrium state. Therefore, it is important to study which waves were generated in the detonation and their behavior in the lithology covered. A slope in the selected mine, Vale S.A. property, was analyzed, which is composed mostly of phyllite and quartzite, sometimes continuous, sometimes discontinuous, as there is a fractured zone of N / S orientation that extends from the top of the slope to its base. In this context, there are at least 3 fracture directions that act as an escape zone for groundwater, constituting a geotechnical problem of material disaggregation, resulting in great erosion. The vibrations induced by the detonations of explosives in the mine can increase the erosion condition when they propagate through the fractured area. The highest seismographic reading obtained among the seismographs was used in this study and the evaluation of the reduction in the safety factor of selected sections was carried out. Later, the maximum value for vibration in which the slope remains stable is also calculated, on Slide2 software. The evaluation of the results on the slope Allowed the interpretation of the effects of vibrations on the slopes reduced between 4.1% and 4.8% the values of the safety factors and shows that the lower the slope safety factor, the greater this interference from the vibration in the stability of the structure. A difference equal to 8% was observed when comparing the section with the highest Safety Factor and the one with the lowest Safety Factor.
Acesso aberto (Open Access)
Detecção de danos em superfícies geotécnicas com redes neurais convolucionais de baixa complexidade
(Universidade Federal do Pará, 2024-05-29) ARAÚJO, Thabatta Moreira Alves de; FRANCÊS, Carlos Renato Lisboa; ttp://lattes.cnpq.br/7458287841862567
Most natural disasters result from geodynamic events, such as landslides and collapse of geotechnical structures. These failures are catastrophic that directly impact the environment and cause financial and human losses. Visual inspection is the main method for detecting surface flaws in geotechnical structures. However, visits to the site can be risky due to the possibility of soil’s instability. Furthermore, the terrain design, hostile environment and remote installation conditions make access to these structures impractical. When a quick and safe assessment is necessary, computer vision analysis becomes a potential alternative. However, studies on computer vision techniques still need to be explored in this field due to the particularities of geotechnical engineering, such as limited, redundant and scarce public data sets. In this context, this thesis presents a redes neurais convolucionais, do inglês Convolutional Neural Network (CNN) approach for identifying defects on the surface of geotechnical structures to reduce dependence on human-led on-site inspections. To this end, images of surface failure indicators were collected on slopes on the banks of a Brazilian highway, with the help of UAVs and mobile devices. Next, low-complexity CNN architectures were explored to build a binary classifier capable of detecting flaws apparent to the naked human eye in images. The architecture composed of three convolutional layers, each with 32 filters, followed by two fully connected layers, each composed of 128 neurons and output with one neuron, showed an accuracy of 94.26%. The performance evaluation of the model with the test set obtained AUC metrics of 0.99, confusion matrix, and a AUPRC curve that indicates robust performance of the classifier in detecting damage, while maintaining a low computational complexity, making it suitable for applications field practices. The contributions of the thesis include the provision of an image database, the obtaining of a classification model suitable for scarce data and limited computational resources, and the exploration of strategies for remote inspection and detection of signs of failure in geotechnical structures.