2019-06-032019-06-032019HOUNSOU, Israël Sèwanou. Estudo e aplicação de redes neurais recorrentes para a imputação de dados em monitoramento da integridade de estruturas civis. Orientador: Claudomiro de Souza de Sales Junior. 2019. 59 f. Trabalho de Curso (Bacharelado em Ciência da Computação) – Faculdade de Computação, Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, 2019. Disponível em: http://bdm.ufpa.br/jspui/handle/prefix/1368. Acesso em:.http://bdm.ufpa.br/jspui/handle/prefix/1368In contemporary times, new developments and technological methods are being used as part of a process called Strutural Health Monitoring (SHM). SHM is the development of strategies for detection, prevention and characterization of undesirable damages in civil and mechanical structures of static behavior (i.e., bridges, railways) and dynamics (i.e., satellites, vehicles, industrial equipment). A large number of sensors collect information over a period of time, which can generate a high amount of data that needs to be transmitted and stored. However, failure or other malfunctions can cause data loss, which directly impacts analysis and decision making. To work around this problem, a new technique appears: A Data Imputation. An imputation process basically replaces lost data with substituted values and “fills” the missing application data with plausible values. This imputation is a practice of filling in missing data and avoids the complexity generated by the missing data. For this, this work will proceed to a comparative study of several imputation techniques referring to imputation by means, fashion, regression, knn and recurrent neural networks. Based on this, this work proposes an evaluation method that compares the error rate generated in the detection of damages. The methods were tested using data sets from a monitoring system installed on the Z-24 bridge (Switzerland), which was subjected to conditions of varying variability as well as progressive damage trials. The occurrence of missing data was done artificially. The results show that recurrent neural networks imputation provides the best results.Acesso AbertoImputação de dadosMonitoramento de integridade estruturalSensoresDados faltantesRedes neurais recorrentesCNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::MATEMATICA DA COMPUTACAO::MODELOS ANALITICOS E DE SIMULACAOTrabalho de Curso - Graduação - Monografia