Building seismic optimization using tuned viscous mass dampers

This study presents a novel approach for enhancing structural safety and effectiveness through a technique called DBNAWM-WPPT (DAWPT) that offers a full seismic optimization framework. It attempts to enhance parameters for multifaceted structures under extreme seismic conditions through better multi-objective coordination. Firstly, the technique employs Wavelet Transform (WT) for the decomposition of seismic ground motions by multiple levels to acquire the features of predominant frequency bands. Next, a predictive surrogate model of nonlinear damping performance is created via Support Vector Machine (SVM). Furthermore, a Deep Belief Network-Adaptive Weighting Mechanism (DBN-AWM) is incorporated to control seismic features that are high-dimensional from multi-dimensional monitored data, where the fitness weights for multiple objectives

are dynamically changed and adjusted. Thereafter, Particle Swarm Optimization (PSO) uses the control parameters of Tuned Viscous Mass Dampers (TVMDs) as decision variables to execute collaborative iterative optimization under modeled seismic conditions. Ultimately, this process is aimed at providing the best possible combination of parameters designed to address and balance the contradicting demands of an appropriate level of seismic activity suppression, economic cost, and structural stability.

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