#Din 1055 wind load windows

#Din 1055 wind load windows

Factors of change maps, referred to subsequent time windows are presented considering RCP4.5 and RCP8.5 emission trajectories, corresponding to medium and maximum greenhouse gas concentration scenarios. Referring to the historical period, the obtained maps are critically compared with the current European maps based on observations. Maps of representative values of snow loads adopted for structural design, associated with an annual probability of exceedance p = 2%, are elaborated for Europe. In the paper, past and future trends of ground snow load in Europe are assessed for the period 1950–2100, starting from high-resolution climate simulations, recently issued by the CORDEX program. As climate change impacts are becoming more and more evident over time, that hypothesis is becoming controversial, so that suitable adaptation strategies aiming to define climate resilient design loads need to be implemented. Like other climatic actions, representative values of snow loads provided in structural codes are usually derived by means of suitable elaborations of extreme statistics, assuming climate stationarity over time. Obviously, the problem is further emphasized in warmer climatic areas, where low design values are generally foreseen for snow loads. Lightweight roofs are extremely sensitive to extreme snow loads, as confirmed by recently occurring failures all over Europe. Subsequently, future daily snow depth data (2016–2065) are retrieved from the NEX-GDPP dataset (Washington, DC/USA: the National Aeronautics and Space Administration(NASA)Earth Exchange/Global Daily Downscaled Projections data), revealing that the simulation data error is highly correlated with that of the input data thus, a validation method for gridded meteorological data is proposed to verify the accuracy of gridded meteorological data within snowfall periods and the reasonability of hydrothermal coupling for gridded meteorological data. Validation results show that the temporal correlations of the observed and the model iterative simulated values are 0.94 for monthly cumulative snow cover duration and 0.88 for monthly cumulative snow depth.

The model is trained and validated based on data from the National Climate Reference Station over a baseline period of 1986–2005. The input layer of the BPANNSIM comprises the current day’s maximum temperature, minimum temperature, snow depth, and precipitation data, and the target layer comprises snow depth data of the following day. In this study, a backpropagation artificial neural network snow simulation model (BPANNSIM) is built using data collected from the National Climate Reference Station to obtain simulation data of China’s future daily snow depth in terms of representative concentration pathways (RCP4.5 and RCP8.5).