Studies into Computational Intelligence and Evolutionary Approaches for Model‐Free Identification of Hysteretic Systems

Abstract

This article introduces a robust hybrid computational method for the data‐driven model‐free identification of nonlinear systems that exhibit hysteretic behavior. The proposed approach combines Genetic Programming, which incorporates discontinuous basis functions, for discovering the structure of the governing differential equations, Genetic Algorithms for optimizing the parameters of the differential equations, and Computer Algebra for simplifying mathematical expressions symbolically, and consequently, controlling bloat through condensing dependent terms. A similar technique has been previously proposed by the authors for the identification of nonhysteretic Single Degree of Freedom (SDOF) systems. That technique is extended in this article and is utilized to provide parsimonious differential equations that represent the Bouc–Wen model and the bilinear hysteretic oscillator—both exhibit abrupt change in their memory‐dependent response. The representative models are subjected to validation excitations that are substantially different from the probing signals to confirm the generalizability of the models for different dynamical phenomena. The results verify the effectiveness of the approach and the accuracy of the subsequent differential operators that characterize the behavior of the studied hysteretic systems even under new dynamical conditions. Beside the presented application of this approach, the introduced methodology is more general and can be employed across different disciplines, specifically in data analytics for mathematical modeling of various complex systems.

Identification of Wind Loads and Estimation of Structural Responses of Super‐Tall Buildings by an Inverse Method

“…Recently, structural health monitoring (SHM) of structural systems has received considerable attention in civil engineering community and some useful results for evaluating structural integrity, durability, and reliability throughout the lifecycle of structures have been obtained (Park et al., ; Qarib and Adeli, ; Park et al., ; Amezquita‐Sanchez and Adeli, , ). In practical applications of SHM, system identification exhibits a critical component and extensive research has been conducted in this field (Jiang and Adeli, ; Adeli and Jiang, ; Amezquita‐Sanchez and Adeli, ; Qarib and Adeli, ; Sun et al., ; An et al., ; Yuen and Mu, ; Zhang et al., ; Oh et al., ; Sun and Betti, ; Boscato et al., ; Bolourchi et al., ; Cha and Buyukozturk, ; Shabbir and Omenzetter, ; Vincenzi and Savoia, ). To evaluate the performance of Taipei 101 Tower under typhoon and earthquake actions, a monitoring system was installed in the super‐tall building.…”

Physics‐based search and optimization: Inspirations from nature

“…where x i ( t ) represents a candidate solution at iteration t , f i is the fitness function value of x i ( t ), and N is the population size. This operator is different from selection operator used in many other optimization algorithms such as genetic algorithms (GA); (Lee, & Arditi, ; Paris, Pedrino, & Nicoletti, ; Bolourchi, Masri, & Aldraihem, ; Park, Oh, & Park, ), genetic programming (Rashidi & Ranjitkar, ; Mesejo et al, ), or particle swarm optimization (PSO) (Zeng, Xu, Wu, & Shen, ; Shabbir & Omenzetter, ).…”

Seismic Data‐Driven Identification of Linear Models for Building Structures Using Performance and Stabilizing Objectives

“…Recently, representative St‐Id methods are iterative optimization dependent (García‐Palencia and Santini‐Bell, ; Sun et al., ; Xu et al., ) and further utilize computational intelligence (Bolourchi et al., ; Vincenzi and Savoia, ). In the broad area of health monitoring, the latest studies on model updating (Oh et al., ; Sun and Betti, ), signal processing (Qarib and Adeli, ), and damage detection (Cha and Buyukozturk, ) share the same characteristic of using intelligent optimization.…”