Macro-scale vulnerability assessment of cities using Association Rule Learning

International audienceIn this paper, a datamining method based on Association Rule Learning (ARL) is applied to define a vulnerability proxy between the elementary characteristics of buildings and the vulnerability classes of the European Macroseismic Scale EMS98 (Grunthal, 1998). The method was applied to the Grenoble city test-bed described in the first part of this paper. The ARL method is then presented and a vulnerability proxy was derived for a Grenoble city-like environment. The vulnerability proxy is tested in Nice in the third part, a city that has been the subject of a vulnerability study (Spence and Lebrun, 2006). Finally, the damage produced by historic earthquakes was computed, considering the (equivalent) earthquake-era and the present-day urbanization for simulating seismic damage

Blind photovoltaic modeling intercomparison: A multidimensional data analysis and lessons learned

The Photovoltaic (PV) Performance Modeling Collaborative (PVPMC) organized a blind PV performance modeling intercomparison to allow PV modelers to blindly test their models and modeling ability against real system data. Measured weather and irradiance data were provided along with detailed descriptions of PV systems from two locations (Albuquerque, New Mexico, USA, and Roskilde, Denmark). Participants were asked to simulate the plane-of-array irradiance, module temperature, and DC power output from six systems and submit their results to Sandia for processing. The results showed overall median mean bias (i.e., the average error per participant) of 0.6% in annual irradiation and −3.3% in annual energy yield. While most PV performance modeling results seem to exhibit higher precision and accuracy as compared to an earlier blind PV modeling study in 2010, human errors, modeling skills, and derates were found to still cause significant errors in the estimates

Seismic vulnerability assessment of urban environments in moderate-to-low seismic hazard regions using association rule learning and support vector machine methods

International audienc

Grain-scale characterization of water retention behaviour of sand using X-ray CT

International audienc