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This work evaluates the Salp Swarm Algorithms' (SSA) performance for truss system optimization problems and presents a novel method called the Modified Salp Swarm Algorithm (MSSA). Five truss structures, previously optimized by metaheuristics and containing discrete & continuous variables, were used for the evaluations. Size and size-shape optimization types have been considered.Although the SSA performs poorly and has convergence issues in initial random solutions, it reaches comparable solutions to previously published results, particularly in continuous problems. Contrarily, the MSSA achieves the best solutions for discrete problems and is relatively close to the best results in the reference literature on continuous problems. Moreover, the MSSA convergence curves exhibit a modest increase in convergence rates, especially for discrete problems. It is envisaged that the findings will contribute to improving solution performance, convergence speed, and security for future real-world applications.

This chapter presents the results of an urban damage assessment after a moderate seismic event, the Mw 5.8 Silivri earthquake, which is the most significant earthquake to have struck the region since two major catastrophic earthquakes, the Mw 7.6 Kocaeli and the Mw 7.1 Düzce earthquakes. First, distribution maps for earthquake parameters and building damages using an appropriate ground motion prediction equation are created for İstanbul. Then, near-real-time hazard and damage distribution maps are generated using the data recorded during the event by the ground motion network established in Istanbul. Comparing the results of the two analyses reveals that the ground motion and damage distributions generated by the selected ground motion prediction equations (GMPEs) are more conservative than those generated by the network, and this is because the actual station data surpass the GMPE’s projections. This research concludes by emphasizing the significance of both GMPEs and densely installed ground motion station networks that capture real-time data during earthquakes and providing motivations for constructing or expanding such systems.