Deprem Mühendisliği

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Browsing Deprem Mühendisliği by Subject "Building -- Earthquake effects."

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    Experimental study on effects of geogrid reinforced zone on seismic performance of low-to-medium rise buildings
    (Thesis (M.S.)-Bogazici University. Kandilli Observatory and Earthquake Research Institute, 2020., 2020.) Küçükakyüz, Okan.; Erdinçliler, Ayşe.
    This study aims to investigate the effectiveness and reliability of geogrids as a soil reinforcing system. In order to prevent or minimize the earthquake impact on structures, this study focuses on reducing the effect of earthquake loads by creating the geogrid reinforced zones. This system is composed of various layers of geogrid configurations to be able to create a reinforced geogrid foundation under the structure. To present reliable results through observing the soil-structure interaction and structural behaviour and digital comparisons via data outputs; an experimental setup was established. With this purpose in mind, the seismic behavior of the two 1:10 scaled structure models without and with the different geogrid reinforcement configurations under different earthquake conditions were studied. A series of shaking table tests were performed to evaluate the seismic response of the building models depending on the selected performance criteria. The effects of geogrid reinforced zone which is dependent on the number of geogrid layers on the seismic behaviour of the low-rise and medium rise buildings were discussed with comparing test results of the unreinforced and reinforced cases. Comparison results of the tests revealed that the inclusion of the geogrid reinforcement to the sand can reduce earthquake impacts by decreasing the transmitted seismic energy from soil to structure via the interlocking mechanism between geogrid layers and soil. Significant improvements in reducing forces of strong ground motions are able to make geogrid reinforced soil systems an option to improve seismic performance of the structures.
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    The use of machine learning algorithms to derive fragility curves for MID-RISE reinforced concrete buildings
    (Thesis (M.S.)-Bogazici University. Kandilli Observatory and Earthquake Research Institute, 2023., 2023) Ülkü, Onur.; Hancılar, Ufuk.
    The occurrence of an earthquake does not necessarily indicate that there is a seismic risk. The existence of risk depends on having three components, which are hazards, exposures, and fragility together. Assessing the risk of existing buildings is a building-specific task that may be tremendously time- consuming and computationally burdensome. Moreover, determining the risk of each structure can be complicated when investigating a portfolio or a group of buildings. Developing fragility curves for buildings provides a possible and undemanding method to estimate damage likelihood. Machine learning algorithms are one of the novel approaches that are implemented for estimating potential structural damage. Well- trained machine learning algorithms provide to speed up processing, cut down the cost of computation, and produce reliable fragility curves. This thesis focuses on developing fragility curves for generic building inventory spread over the Marmara region by predicting the probability of maximum inter- story drift ratio intervals via five various machine learning algorithms, which are Random Forest, Stochastic Gradient Boosting, Na¨ıve Bayes, Decision Tree, K-Nearest Neighbors. Information on the characteristics of pre-dominant building typologies in the Marmara region available in the literature is utilized for creating an artificial inventory dataset of mid- rise RC buildings. The data for the machine learning was gathered by designing and analyzing 2-D frame systems under non-linear time history analysis with OpenSeesPy. The machine learning algorithms are trained considering different intensity measures and buildings’ characteristics. Machine learning algorithms are evaluated when generating fragility functions by comparing those produced by fitting log-normal distribution with the maximum likelihood estimation method.