High Damping Rubber Bearings: Optimal models evaluation considering production variability

Seismic isolation has become an effective technique for seismic protection, significantly reducing lateral forces and horizontal accelerations in buildings. Among various isolation devices, High Damping Rubber Bearings are widely used due to their cost-effectiveness, ease of application, and clear mechanical behavior during the design phase. A precise mechanical behavior model is essential for the efficient use of High Damping Rubber Bearings in practice, as it realistically mitigates seismic actions on protected structures. The design of these systems mandates a permissible displacement limit under seismic conditions, beyond which the mechanical performance and operational functionality of the bearings maybe compromised. This paper focuses on the optimal selection of mechanical behavior models for High Damping Rubber Bearings, constrained by the maximum design displacement to prevent phenomena such as pinching. The primary innovation lies in evaluating not only the accuracy but also the stability of mechanical parameters derived from standard experimental tests, as specified by construction guidelines. The research investigates the mechanical behavior within the limits of maximum design displacement, ensuring alignment with practical design scenarios and addressing relevant mechanical responses under typical service conditions. Moreover, it provides a reliable assessment approach for the assessment of the real industrial production variability of such devices, emphasizing the effect of uncertainties induced in manufacturing, and the need for robust, adaptable design models to accommodate such variability. Six established High Damping Rubber Bearings models were evaluated through experimental tests on ten samples. The analysis revealed that models with higher accuracy in reproducing the experimental behavior of a specific device might fail to represent the variability found in industrial production. This highlights the importance of balancing accuracy and robustness in the model's selection for practical applications.