Title:
Response Simulation, Data Cleansing and Restoration of Dynamic and Static Measurements Based on Deep Learning Algorithms
Author(s):
Seok‑Jae Heo , Zhang Chunwei and Eunjong Yu
Publication:
IJCSM
Volume:
13
Issue:
Appears on pages(s):
Keywords:
artificial intelligence, deep learning, adaptive neuro-fuzzy inference system, response simulation, data cleansing, noise reduction, data restoration
DOI:
10.1186/s40069-018-0316-x
Date:
2/28/2019
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.