Abstract
In this study, an output-based neuro controller was built based on the idea of the adaptive neuro-fuzzy inference system (ANFIS) and its capabilities in response simulation, data cleansing and restoration capability were verified using measurement data from actual structural testing. The ANFIS is a family of the deep learning algorithm, which incorporates the benefits of adaptive control technique, artificial neural network, and the fuzzy inference system. Thus, it is expected to produce very accurate predictions even for the highly nonlinear system. Forced vibration responses of a five-story steel building were simulated by ANFIS and its accuracy was compared with the results of Recurrent Neural Network (RNN), which is a type of traditional artificial neural networks. Simulations by ANFIS were very accurate with a much lower root means square error (RMSE) than RNN. Simulated data by ANFIS showed an almost perfect match with the original. Even the small ripples in the power spectrum plot outside the dominant frequency were successfully reproduced. In addition, the ANFIS was used to increase the sampling rate of dynamic data. It was shown that missing high-frequency contents could be successfully reproduced when the ANFIS was properly trained. Lastly, The ANFIS was applied to remove the noise in the measured data from RC column cyclic load tests. The outliers were corrected effectively, but the tendency of flattening the peak values was observed.
Highlights
Performance data collected from structural testing have been the fundamental resource for the development of structural engineering
An adaptive neuro-fuzzy inference system (ANFIS) is a kind of deep learning algorithm that is a combination of the adaptive control technique, artificial neural network, and the fuzzy inference system
Adding the adaptive control technique minimizes the errors between the original data and the estimate by the neuro-fuzzy system
Summary
Performance data collected from structural testing have been the fundamental resource for the development of structural engineering. According to Fu (1970) and Gupta and Saridis (1977), the intelligent intelligence can be defined as a combination of control theory and information technology to imitate human behavior such as ‘learning’ and ‘problem solving’ It involves interpretation of input data, Heo et al Int J Concr Struct Mater (2018) 12:82 treatment of the ambiguity in human perception for transformation to machine knowledge (Gupta and Saridis 1977; Saridis and Lee 1979; Fu and Gonzalez 1987; Fu 1970). An adaptive neuro-fuzzy inference system (ANFIS) is a kind of deep learning algorithm that is a combination of the adaptive control technique, artificial neural network, and the fuzzy inference system.
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