Akademik Veri Yönetim Sistemi
7th International Conference on Earthquake Engineering and Seismology-ICEES, Antalya, Türkiye, 6 - 10 Kasım 2023, cilt.401, ss.17-27, (Tam Metin Bildiri)
Fling step (permanent displacement) has been recognized as one of the most damaging earthquake effects on structures located in the vicinity of the fault rupture trace. Nevertheless, ground motion records containing fling step are not identified on account of the fact that standard data processing methods, such as filtering, mask the permanent displacements (PDs). Thus, this effect is commonly ignored during the seismic design of engineering structures. Fling step prediction models has certain limitations since models are mainly derived from the synthetic strong motions of earthquakes. In the last 3 decades, an increase in near fault strong motion recordings made it possible to calculate fling amplitudes with special baseline correction procedures. With this emerging database, it is possible to develop fling amplitude prediction equations or to adjust existing equations. Hence, in this study, performance of the existing fling step predictive models of Kamai et al. (Kamai et al. in Bull Seismol Soc Am 104:1914-1929, 2014), Burks and Baker (Burks and Baker in Soil Dynam Earthq Eng 80:119-126, 2016) and Turkiye adjusted Burks and Baker (Burks and Baker in Soil Dynam Earthq Eng 80:119-126, 2016) are evaluated using the fling inventory of moderate to large magnitude earthquakes in Turkiye including the 2023 Kahramanmaras (Mw7.7) earthquake. Evaluations are performed only with total residual values of fling amplitudes. Burks and Baker (Burks and Baker in Soil Dynam Earthq Eng 80:119-126, 2016) model is found successsful for earthquakes with magnitudes between Mw 6 and Mw 6.9 whereas Kamai et al. (Kamai et al. in Bull Seismol Soc Am 104:1914-1929, 2014) well captured the PDs originated from large Mw7 + earthquakes. In general, estimates of Turkiye adjusted Burks and Baker (Burks and Baker in Soil Dynam Earthq Eng 80:119-126, 2016) model are much closer to observed averaged PDs in the dataset.