Akademik Veri Yönetim Sistemi
European Geosciences Union (EGU) - Geophysical Research Abstract, cilt.19, sa.1, ss.1, 2017 (Düzenli olarak gerçekleştirilen hakemli kongrenin bildiri kitabı)
Recent advances in sensors have helped the growth of local networks. In recent years, many Micro Electro
Mechanical System (MEMS)-based accelerometers have been successfully used in seismology and earthquake
engineering projects. This is basically due to the increased precision obtained in these downsized instruments.
Moreover, they are cheaper alternatives to force-balance type accelerometers.
In Turkey, though MEMS-based accelerometers have been used in various individual applications such as
magnitude and location determination of earthquakes, structural health monitoring, earthquake early warning
systems, MEMS-based strong motion networks are not currently available in other populated areas of the country.
Motivation of this study comes from the fact that, if MEMS sensors are qualified to record strong motion
parameters of large earthquakes, a dense network can be formed in an affordable price at highly populated areas.
The goals of this study are 1) to test the performance of MEMS sensors, which are available in the inventory of the
Institute through shake table tests, and 2) to setup a small scale network for observing online data transfer speed
to a trusted in-house routine.
In order to evaluate the suitability of sensors in strong motion related studies, MEMS sensors and a reference
sensor are tested under excitations of sweeping waves as well as scaled earthquake recordings. Amplitude response
and correlation coefficients versus frequencies are compared. As for earthquake recordings, comparisons are
carried out in terms of strong motion(SM) parameters (PGA, PGV, AI, CAV) and elastic response of structures
(Sa). Furthermore, this paper also focuses on sensitivity and selectivity for sensor performances in time-frequency
domain to compare different sensing characteristics and analyzes the basic strong motion parameters that influence
the design majors.
Results show that the cheapest MEMS sensors under investigation are able to record the mid-frequency
dominant SM parameters PGV and CAV with high correlation. PGA and AI, the high frequency components of
the ground motion, are underestimated. Such a difference, on the other hand, does not manifest itself on intensity
estimations. PGV and CAV values from the reference and MEMS sensors converge to the same seismic intensity
level. Hence a strong motion network with MEMS sensors could be a modest option to produce PGV-based
damage impact of an urban area under large magnitude earthquake threats in the immediate vicinity.