A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment

Structural health monitoring is an important research field being investigated around the globe. In recent years, meta-heuristics are being used to solve the complex inverse problem of struc-tural damage assessment. In this work, a novel approach depending on a new meta-heuristic and effective objective function formulation is proposed. Firstly, by considering some research short-comings, a triple modal-based objective function combination is employed to improve the precision of damage identification. Secondly, a new self-adaptive algorithm which combines the powerful fea-tures of the stochastic fractal search with improved mechanisms into one framework, is developed. Moreover, the concept of quasi-oppositional learning is utilized to improve the overall exploration in both initial and executive stages. The new algorithm, called the self-adaptive quasi-oppositional stochastic fractal search (SA-QSFS), is benchmarked using well-known benchmark functions and applied on the IASC-ASCE FE model for damage assessment. Various damage scenarios are stud-ied using partial modal data and noisy conditions. The proposed technique demonstrates outstand-ing performance and can be recommended to solve continuous optimization problems. (c) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/ 4.0/).