TY  - JOUR
A1  - Achenbach, Marcus
A1  - Lahmer, Tom
A1  - Morgenthal, Guido
T1  - Identification of the thermal properties of concrete for the temperature calculation of concrete slabs and columns subjected to a standard fire—Methodology and proposal for simplified formulations
JF  - Fire Safety Journal 87
N2  - The fire resistance of concrete members is controlled by the temperature distribution of the considered cross section. The thermal analysis can be performed with the advanced temperature dependent physical properties provided by 5EN6 1992-1-2. But the recalculation of laboratory tests on columns from 5TU6 Braunschweig shows, that there are deviations between the calculated and measured temperatures. Therefore it can be assumed, that the mathematical formulation of these thermal properties could be improved. A sensitivity analysis is performed to identify the governing parameters of the temperature calculation and a nonlinear optimization method is used to enhance the formulation of the thermal properties. The proposed simplified properties are partly validated by the recalculation of measured temperatures of concrete columns. These first results show, that the scatter of the differences from the calculated to the measured temperatures can be reduced by the proposed simple model for the thermal analysis of concrete.
KW  - Sensitivitätsanalyse
KW  - Thermodynamische Eigenschaft
KW  - Fire resistance; Parameter optimization; Sensitivity analysis; Thermal properties
Y1  - 2017
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170331-30929
UR  - http://www.sciencedirect.com/science/article/pii/S0379711216301965
SP  - 80
EP  - 86
ER  - 
TY  - JOUR
A1  - Harirchian, Ehsan
A1  - Lahmer, Tom
A1  - Kumari, Vandana
A1  - Jadhav, Kirti
T1  - Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings
JF  - Energies
N2  - The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning.
KW  - Erdbeben
KW  - Maschinelles Lernen
KW  - earthquake vulnerability assessment
KW  - rapid visual screening
KW  - machine learning
KW  - support vector machine
KW  - buildings
KW  - OA-Publikationsfonds2020
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200707-41915
UR  - https://www.mdpi.com/1996-1073/13/13/3340
VL  - 2020
IS  - volume 13, issue 13, 3340
PB  - MDPI
CY  - Basel
ER  -