“…In addition to parametric modelling, there are two non-parametric approaches that have been used in the development of MRF damper models: mathematical functions and the computational intelligence paradigms. The mathematical functions approach was used to obtain accurate hysteresis behaviour, such as hyperbolic tangent (Baxter and Caicedo, 2014;Jiang and Christenson, 2012;Tian et al, 2015), polynomial (Choi et al, 2001;Pokaad et al, 2011Pokaad et al, , 2013Ubaidillah et al, 2011), and sigmoid function (Liao and Wang, 2003;Ma et al, 2006), but its accuracy in predicting MRF damper behaviour yielded a relatively large error compared to experimental characterisation data. On the other hand, the second approach of computational intelligence paradigms using neural networks (Khalid et al, 2014;Wang and Liao, 2005;Zeinali et al, 2014), adaptive neuro-fuzzy inference systems (ANFIS) Imaduddin et al (2017); Rahmat et al (2019a, b); Zeinali et al (2013), and genetic algorithms (Giuclea et al, 2004) has been investigated by many researchers.…”