https://repositorio.ifgoiano.edu.br/handle/prefix/252 2026-09-05T16:18:34Z https://repositorio.ifgoiano.edu.br/handle/prefix/6906 Título: ESTUDO DE VIABILIDADE ECONÔMICA PARA IMPLANTAÇÃO DE CARPORT FOTOVOLTAICO NO ESTACIONAMENTO DO HOSPITAL MUNICIPAL DE MONTIVIDIU-GO Autor(es): Pascoal, João Vitor Morais Primeiro Orientador: Barbosa Junior, João Areis Ferreira Abstract: This study analyzed the economic feasibility of implementing a photovoltaic carport at the Municipal Hospital of Montividiu, Goiás, Brazil, based on the estimation of the required investment and the assessment of its financial returns. To this end, the hospital’s average monthly electricity consumption was surveyed, the parking spaces were dimensioned, and quotations were obtained from specialized companies. The economic analysis was conducted using the Net Present Value (NPV), Internal Rate of Return (IRR), and discounted Payback Period indicators. The results demonstrated that all analyzed proposals were economically feasible, presenting positive Net Present Value, an Internal Rate of Return higher than the minimum attractiveness rate, and a maximum investment payback period of four years and one month. Based on these results, it is concluded that the implementation of a photovoltaic carport constitutes a technically and economically feasible alternative, contributing to the reduction of electricity expenditures, improvement of hospital infrastructure, and more efficient allocation of public resources. Editor: Instituto Federal Goiano Tipo: Trabalho de Conclusão de Curso 2026-08-17T00:00:00Z https://repositorio.ifgoiano.edu.br/handle/prefix/6773 Título: ESTUDO COMPARATIVO DA ESTABILIDADE DE EDIFÍCIOS DE MÚLTIPLOS PAVIMENTO ENTRE LIGAÇÕES RIGÍDAS E SEMIRRÍGIDAS Autor(es): Oliveira, Guilherme Honorato Primeiro Orientador: Bernardes, Heitor Cardoso Abstract: Analyzing global instability in multi-story buildings is crucial for precast concrete structures, which may exhibit varying degrees of stiffness or sway depending on the beam-column connections. These connection stiffness characteristics can reduce bending moment transmission, thereby increasing horizontal displacements and influencing foundation forces— particularly under wind loads. This study aims to compare the structural behavior of two-dimensional precast plane frame models under two connection conditions: rigid and semi-rigid. The analysis evaluates horizontal displacements, global stability, and foundation moments using the γz coefficient method and P-Δ analysis to account for second-order effects. The results indicate that adopting semi-rigid connections reduces the structure's overall stiffness, leading to greater lateral displacements and increased foundation forces compared to a rigid model. This highlights the importance of accurately modeling connection stiffness to realistically predict structural behavior. It is concluded that accounting for moment redistribution effects associated with connection stiffness is essential for assessing global stability in precast structures, thereby contributing to greater safety and reliability in structural design. Editor: Instituto Federal Goiano Tipo: Trabalho de Conclusão de Curso 2026-06-25T00:00:00Z https://repositorio.ifgoiano.edu.br/handle/prefix/6591 Título: DIAGNÓSTICO DAS CONDIÇÕES DE SEGURANÇA CONTRA INCÊNDIO EM EDIFICAÇÕES HISTÓRICAS: ESTUDO DE CASO CASARÃO FEDERICO GONZAGA JAYME EM RIO VERDE – GO Autor(es): Ferreira, Pedro Vitor de Paula Primeiro Orientador: Campos, Bruna Abstract: This study presents a diagnosis of the fire safety conditions of the Frederico Gonzaga Jayme Mansion, an important historic landmark in Rio Verde – GO. Built in 1888, the building is currently in a state of abandonment and structural degradation, worsened by legal disputes. Fire risk assessment involves several factors, including fire load, building classification, emergency access conditions, and other elements related to the building's construction characteristics and use. In this context, this study aimed to analyze the fire risk in a historic building through a case study of the Frederico Gonzaga Jayme Mansion, located in the municipality of Rio Verde – GO. For the assessment, the fire risk analysis method proposed by Professor Antônio Maria Claret Gouveia was adopted — a methodology designed exclusively for analyzing fire risk in such sites, which differs from modern standards that require structural modifications in listed buildings. The work on the historic landmark protected by the municipality of Rio Verde, known as "O Casarão," is under imminent risk of total loss, making immediate intervention by public authorities and preservation agencies imperative. Technical restoration and the implementation of adequate safety measures are the only viable paths to ensuring the safeguarding of historical memory and the integrity of the municipal architectural heritage. Editor: Instituto Federal Goiano Tipo: Trabalho de Conclusão de Curso 2026-04-28T00:00:00Z https://repositorio.ifgoiano.edu.br/handle/prefix/6503 Título: APLICAÇÃO DE ALGORITMOS DE APRENDIZADO DE MÁQUINA NA ESTIMATIVA DO FATOR DE SEGURANÇA DE TALUDES COM DADOS SIMULADOS PARA DIFERENTES MÉTODOS DE EQUILÍBRIO LIMITE Autor(es): Costa, Lucas Soares Primeiro Orientador: Chaves, Charles Pereira Primeiro Membro da Banca: Silva, Philippe Barbosa Segundo Membro da Banca: Cavalcante, André Luis Brasil Abstract: Slope stability is a fundamental aspect of geotechnical engineering and plays a crucial role in ensuring the safety and performance of civil and environmental engineering projects. Traditional slope stability assessments rely on limit equilibrium methods, such as those developed by Fellenius, Bishop, Janbu, and others, which evaluate the safety factor based on simplified assumptions about soil behavior. With the evolution of computational tools, geotechnical software has made it possible to conduct more realistic and complex simulations, enhancing the reliability of engineering designs. Recently, artificial intelligence techniques, especially machine learning, have emerged as promising tools for improving slope stability analysis by better addressing uncertainties and reducing the limitations of conventional methods. This study proposes the development of a predictive model using machine learning algorithms trained on simulated data generated from geotechnical software. The objective is to evaluate the performance of different algorithms in estimating the safety factor of slopes and to compare their accuracy and efficiency with traditional methods. The research aims to contribute to the advancement of AI applications in geotechnical engineering and to promote safer, more efficient design practices. Editor: Instituto Federal Goiano Tipo: Trabalho de Conclusão de Curso 2026-03-23T00:00:00Z