165 research outputs found
Comparison between seismic vulnerability models and experimental dynamic properties of existing buildings in France
Elastic fundamental frequency is a key-parameter of simplified seismic design and vulnerability assessment methods. Empirical relationships exist in codes to estimate this frequency but they miss experimental data to validate them accounting for national feature of building design and, above all, corresponding uncertainties. Even if resonance frequency extracted from ambient vibrations may be larger than the elastic frequency (at yield) generally used in earthquake engineering, ambient vibration recordings may provide a large set of data for statistical analysis of periods versus building characteristics relationships. We recorded ambient vibrations and estimated the fundamental frequency of about 60 buildings of various types (RC and masonry) in Grenoble City (France). These data complete the set existing yet, made of 26 RC-buildings of Grenoble (Farsi and Bard 2004) and 28 buildings in Nice (France) (Dunand 2005). Statistical analysis of these experimental data was performed for fundamental frequencies of RC shear wall structures and the results are compared with existing relationships. Only building height or number of stories has a statistical relevancy to estimate the resonance frequency but the variability associated to the proposed relationships is large. Moreover, we compared the elastic part of capacity curves of RC and masonry buildings used in the European Risk-UE method for vulnerability assessment with the experimental frequencies. The variability is also large and the curves may not be consistent with French existing building
Assessment of the seismic non-linear behavior of ductile wall structures due to synthetic earthquakes
Displacement ductility and energy assessement from shaking table tests on RC structural walls
Impact of displacement demand reliability for seismic vulnerability assessment at an urban scale
This paper addresses seismic vulnerability assessment at an urban scale by focusing on the displacement demand determination for building damage prediction. The study is based on the comparison of urban seismic damage distributions obtained by the displacement demand computed using non-linear time-history analysis (NLTHA) with three simplified methods. These methods include the N2 method, the Lin & Miranda proposal and an optimized version of the N2 method. Comparing the different damage distributions from the three simplified methods with the one obtained by time-history analysis helps understanding the reliability of displacement demand determination. The study is carried out on Sion and Martigny, two typical Swiss cities. For the case of Sion, results clearly show that using N2 method may lead to significant overestimation of damage grade distribution. The use of Lin & Miranda method and optimized version of N2 improves the damage prediction in both cases. For the other studied case of Martigny, N2 method and Lin & Miranda proposal are not accurate. The optimized version of N2 method provides stable and reliable results
Seismic response of nonstructural components in case of nonlinear structures based on floor response spectra method
Building typological classification in Switzerland using deep learning methods for seismic assessment
Natural disasters, such as earthquakes, have always represented a danger to human life. Seismic risk assessment consists of the evaluation of existing buildings and their expected response in case of an earthquake; the exposure model of buildings plays a key role in risk calculations. With this respect, in recent years, advanced techniques have been developed to speed up and automatize the processes of data acquisition to data interpretation, although it is worth mentioning that the visual survey is essential to train and validate Machine Learning (ML) methods. In the present study, the identification of building types is conducted by exploiting the traditional visual survey to implement a Deep Learning (DL) classification model. As a first step, city mapping schemes are obtained by classifying buildings according to the main features (i.e., construction period and height classes). Then, Random Forest (RF), a supervised learning algorithm, is applied to classify different building types by exploiting all their attributes. The RF model is trained and tested on the cities of Neuchatel and Yverdon-Les-Bains. The decent accuracy of the results encourages the application of the method to different cities, with proper adjustments in datasets, features and algorithms
Non-linear seismic behavior of structures with limited hysteretic energy dissipation capacity
ISSN:1570-761XISSN:1573-145
Extension of adrenocortical carcinoma into the right atrium – echocardiographic diagnosis: A case report
BACKGROUND: Adrenocortical carcinoma is a rare, highly malignant tumor. Cardiac involvement of the tumor is very rare. Echocardiography facilitates the evaluation of the cardiac involvement of the tumor. CASE PRESENTATION: We describe a patient with an adrenal tumor. Transthoracic echo showed its extension into the right atrium. Accordingly, a combined abdominal and cardiac operation was performed, monitored by transesophageal echocardiography. CONCLUSION: This case highlights the importance of echocardiography in revealing the cardiac involvement by this tumor and in planning the operative procedure
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