Non-probabilistic method to consider uncertainties in structural damage identification based on Hybrid Jaya and Tree Seeds Algorithm

• Published in: Engineering structures Vol. 220; p. 110925
• Main Authors: Ding, Zhenghao, Li, Jun, Hao, Hong
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.10.2020; Elsevier BV
• Subjects: Algorithms; Cluster analysis; Clustering; Computer simulation; Damage detection; Finite element method; Hybrid systems; Hybridization; Intelligence; Modal data; Non-probabilistic; Objective function; Optimization; Probabilistic methods; Reduction; Regularization; Robustness (mathematics); Seeds; Sparse regularization technique; Stiffness; Structural damage; Swarm intelligence; Swarm intelligence method; Uncertainties; Uncertainty; Upper bounds; Vector quantization; Vibration-based damage identification; Sparse regularization technique; Non-probabilistic; Swarm intelligence method; Hybridization; Uncertainties; Vibration-based damage identification
• ISSN: 0141-0296; 1873-7323
• DOI: 10.1016/j.engstruct.2020.110925

•A novel non-probabilistic structural damage identification approach is proposed.•Uncertainties in finite element modelling and measurement errors are considered.•Developed a hybrid swarm intelligence technique based on Jaya and TSA.•Numerical and experimental investigations are conducted to verify the approach.•Reliable results are obtained with probability of damage existence. This paper proposes a novel non-probabilistic structural damage identification approach by developing a hybrid swarm intelligence technique based on Jaya and Tree Seeds Algorithm (TSA), taking into account the high-level uncertainties in the measurements and finite element modelling. The damage in structure is simulated as reduction of elemental stiffness, and structural damage identification is formulated as an optimization problem. To overcome the challenge for structural damage identification with a limited number of measurement data, an objective function based on the modal data and sparse regularization technique is defined. To make the optimization algorithm more powerful and robust, a hybridization of the K-means clustering based Jaya and TSA is proposed. Jaya algorithm is taken as the core in the hybridization. The clustering strategy is employed to replace solutions with low-quality objective values in the Jaya algorithm. Then the search strategy of the TSA is introduced into the best-so-far solution of each cycle. The proposed hybridization algorithm is termed as “ C-Jaya-TSA”. To enhance the capacity of the proposed algorithm to consider uncertainties, a non-probabilistic method is also integrated to calculate the interval bound (lower and upper bounds) of the elemental stiffness changes by using the interval analysis method. To better quantify the structural damage extents, Damage Measure Index (DMI) values are introduced for representing structural damage states. The DMI value can be viewed as a combination of deterministic stiffness reduction and the Possibility of Damage Existence (PoDE). Numerical benchmark functions, numerical studies and experimental investigations are conducted to verify the accuracy and performance of the proposed method. The identification results show that the developed C-Jaya-TSA integrated with the non-probabilistic interval analysis method is a promising tool to accurately identify the structural damage, even high-level uncertainties exist.