###### Abstract  
Damage detection of civil and mechanical structures based on measured modal parameters using model updating schemes has received increasing attention in recent years. In this study, for uncertainty-oriented damage identification, a non-probabilistic structural damage identification (NSDI) technique based on an optimization algorithm and interval mathematics is proposed. In order to take into account the uncertainty quantification, the elastic modulus is described as unknown-but-bounded interval values and the proposed new scheme determines the upper and lower bounds of the damage index. In this method, the interval bounds can provide supports for structural health diagnosis under uncertain conditions by considering the uncertainties in the variables of optimization algorithm. The model updating scheme is subsequently used to predict the interval-bound of the Elemental Stiffness Parameter (ESP). The slime mold algorithm (SMA) is used as the main algorithm for model updating. In addition, in this study, an enhanced variant of SMA (ESMA) is developed, which removes unchanged variables after a defined number of iterations. The method is implemented on three well-known numerical examples in the domain of structural health monitoring under single damage and multi-damage scenarios with different degrees of uncertainty. The results show that the proposed NSDI methodology has reduced computation time, by at least 30%, in comparison with the probabilistic methods. Furthermore, ESMA has the capability to detect damaged elements with higher certainty and lower computation cost in comparison with the original SMA. s 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 2202 2022 22022 2022 2022 2022 2022 2022 22022 2022 2022 22022 22022 22022 22022 2022 22022 22022 2022 22022 22022 22022 22022 22022 2202 220 Modal parameters of a structure are usually used to detect structural damages [2; 3; 4; 5; 6; 7; 8; 9; 10], since modal parameters (modal frequencies and mode shapes) are functions of the physical parameters (mass and stiffness). As a result, the presence of damage causes changes in the structure's modal characteristics. Modal parameters can also be monitored conveniently and precisely. The experimentally obtained modal parameters are considered accurate and deterministic in conventional applications of model updating for damage detection [11; 12; 13; 14; 15; 16; 17; 18]. However, there are always uncertainties in the recorded modal parameters in real applications, which can lead to unreliable and inaccurate structural damage assessments. As a result, uncertainties in structural damage detection must be considered [19; 20]. In the domain of damage detection, research addressing uncertainty has been conducted utilizing probability analysis and fuzzy theory [21]. The randomness of the structural finite element (FE) model and the test data were employed by [PERSON] et al. [22]. The probability distribution characteristics of the FE model were determined using the Monte Carlo technique and data perturbation. The structural damage probability was then calculated using the interval estimation approach. Despite extensive studies that have been performed, some problems have been identified by other scholars in the study of uncertain damage identification. One major difficulty is that the existing uncertain damage detection methods based on probability analysis and fuzzy theory typically require a large quantity of sample data to explain the source of uncertainty [23]. However, the probability model is commonly used to follow specific distributions in engineering practice because of insufficient experimental information and limited cost [24]. As a result, the accuracy and reliability of approaches based on probability and fuzzy theory cannot be guaranteed [25]. In interval analysis, uncertain parameters are represented as interval numbers that only require the uncertainty parameters' boundaries [26].