The Converter software is plug-in for Autodesk Revit. It works equally well for 64-bit and 32-bit platforms, and it requires Revit API, .Net framework 3.5 and Microsoft Excel preinstalled. (If you don't have MS Excel but you are interested in our software we can create personal assembly for your business, please contact us for more information)
Converter can transfer 3D model from Autodesk Revit (2009, 2010, 2011) to CSI SAFE 12.x.x.
Elements which only belong to adjacent floors can be converted at the same time. The following instances are converted:
Beams
Columns (with local axis rotation parameter)
Slabs and curved edge slabs (drop panels and slab differ) with openings
Walls and curved walls
Grids
Groups of elements
There is possibility to adjust boundary elevations for columns and walls.
Revit 3D model can be transfered to Excel spreadsheet or SAFE *.f2k file.
Default design parameters can be assign during conversion:
Concrete and rebar materials (Materials database can be easily reset or adjusted with using Microsoft Excel).
Cover layers for slabs and beams
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Author: Laura Kong, Ian Robertson, Harry Yeh | Size: 6 MB | Format:PDF | Quality:Unspecified | Publisher: - | Year: - | pages: 28 | ISBN: -
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Pilot Study on current code tsunami design
Lessons from Indian Ocean Tsunami
FEMA ATC-64 Project
NEESR-SG Proposal - Performance Based Tsunami Engineering, PBTE
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Rainfall and sampling uncertainties: A rain gauge perspective
Author: Gabriele Villarini, Pradeep V. Mandapaka, Witold F. Krajewski, and Robert J. Moore | Size: 733 KB | Format:PDF | Quality:Unspecified | Publisher: Wiley | Year: 2008 | pages: 12
Rain gauge networks provide rainfall measurements with a high degree of accuracy at
specific locations but, in most cases, the instruments are too sparsely distributed to
accurately capture the high spatial and temporal variability of precipitation systems. Radar
and satellite remote sensing of rainfall has become a viable approach to address this
problem effectively. However, among other sources of uncertainties, the remote-sensing
based rainfall products are unavoidably affected by sampling errors that need to be
evaluated and characterized. Using a large data set (more than six years) of rainfall
measurements from a dense network of 50 rain gauges deployed over an area of about
135 km2 in the Brue catchment (south-western England), this study sheds some light on
the temporal and spatial sampling uncertainties: the former are defined as the errors
resulting from temporal gaps in rainfall observations, while the latter as the uncertainties
due to the approximation of an areal estimate using point measurements. It is shown that
the temporal sampling uncertainties increase with the sampling interval according to a
scaling law and decrease with increasing averaging area with no strong dependence on
local orography. On the other hand, the spatial sampling uncertainties tend to decrease for
increasing accumulation time, with no strong dependence on location of the gauge
within the pixel or on the gauge elevation. For the evaluation of high resolution satellite
rainfall products, a simple rule is proposed for the number of rain gauges required to
estimate areal rainfall with a prescribed accuracy. Additionally, a description is given of
the characteristics of the rainfall process in the area in terms of spatial correlation
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Spatial uncertainty to determine reliable daily precipitation maps
Author: Adrian Chappell, Luigi Renzullo, and Malcolm Haylock | Size: 6.5 MB | Format:PDF | Quality:Unspecified | Publisher: Wiley | Year: 2012 | pages: 14
Daily precipitation observations are commonly used with related variables to make
estimates at unsampled locations to provide maps and gridded data for hydrological and
climate model applications. Uncertainty in the way gridded data (maps) are prepared, given
the available information, is rarely considered. Over a study period of one year, we used
conditional simulations to produce multiple equally likely realizations of Australian
precipitation per day. Together those realizations represented an ensemble measure of
spatial uncertainty for rainfall for a given day. An independent gauge data set had values
within the 5th–95th percentile uncertainty range 94% of the time. Combined with other
measures they confirmed the reliability of the ensemble spatial uncertainty ranges. We
compared several established mapping techniques to an independent gauge data set using
local error statistics and to the spatial uncertainty maps. Those statistics showed little
difference between the mapping techniques and overall assessment of performance was
largely dependent on skill scores. However, the mapping techniques were different when
compared to the spatial uncertainty ranges. These findings support the assertion that
assessment of mapping techniques using local error statistics is insensitive to the
uncertainty in producing the maps as a whole. We conclude that uncertainty information in
precipitation estimates should not be overlooked when comparing precipitation estimation
techniques. The focus of performance assessment is traditionally on local error estimates,
and this tradition diverts attention away from the issues of uncertainty and reliability.
Reliable uncertainty characterization is necessary for the rigorous detection of spatial
patterns and longer time series trends in precipitation
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A method for delineating and estimating rainfall fields
Author: C. BARANCOURT AND J. D. CREUTIN | Size: 1.1 MB | Format:PDF | Quality:Unspecified | Publisher: Wiley | Year: 1992 | pages: 12
A geostatisticaml odelh as beend efinedt o deal with intermittentr ainfall fields. The intermittencyi s
representedb y a binary randomf unction,w hile, insidet he rainy areas, the rainfall variability is
rendered by an intrinsic random function. A rain gage data set from the Cevennes region (France) is
usedt o infer the model.T he inner variabilityi s independenot f the intermittency,t hereby allowing a
simplee stimationre lyingo n the separatek rigingo f theset wo componentsC. omparisono f the rainfall
assessmentms adeb y a classicg lobalk riginga nd the proposedm ethods howsi ts clear superiorityf or
delineating and estimating rainfall fields.
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Spatial interpolation of precipitation in a dense gauge network for monsoon storm events in the southwestern United States
Author: Matthew Garcia,Christa D. Peters-Lidard, and David C. Goodrich | Size: 900 KB | Format:PDF | Quality:Unspecified | Publisher: Wiley | Year: 2008 | pages: 14
Inaccuracy in spatially distributed precipitation fields can contribute significantly to
the uncertainty of hydrological states and fluxes estimated from land surface models. This
paper examines the results of selected interpolation methods for both convective and
mixed/stratiform events that occurred during the North American monsoon season over a
dense gauge network at the U.S. Department of Agriculture Agricultural Research Service
Walnut Gulch Experimental Watershed in the southwestern United States. The spatial
coefficient of variation for the precipitation field is employed as an indicator of event
morphology, and a gauge clustering factor CF is formulated as a new, scale-independent
measure of network organization. We consider that CF < 0 (a more distributed gauge
network) will produce interpolation errors by reduced resolution of the precipitation field
and that CF > 0 (clustering in the gauge network) will produce errors because of
reduced areal representation of the precipitation field. Spatial interpolation is performed
using both inverse-distance-weighted (IDW) and multiquadric-biharmonic (MQB)
methods. We employ ensembles of randomly selected network subsets for the statistical
evaluation of interpolation errors in comparison with the observed precipitation. The
magnitude of interpolation errors and differences in accuracy between interpolation
methods depend on both the density and the geometrical organization of the gauge
network. Generally, MQB methods outperform IDW methods in terms of interpolation
accuracy under all conditions, but it is found that the order of the IDW method is
important to the results and may, under some conditions, be just as accurate as the
MQB method. In almost all results it is demonstrated that the inverse-distance-squared
method for spatial interpolation, commonly employed in operational analyses and for
engineering assessments, is inferior to the ID-cubed method, which is also more
computationally efficient than the MQB method in studies of large networks.
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Multicriteria design of rain gauge networks for flash flood prediction in semiarid catchments with complex terrain
Author: Till H. M. Volkmann, Steve W. Lyon, Hoshin V. Gupta, and Peter A. Troch | Size: 1.9 MB | Format:PDF | Quality:Unspecified | Publisher: Wiley | Year: 2010 | pages: 16
Despite the availability of weather radar data at high spatial (1 km2) and temporal
(5–15 min) resolution, ground‐based rain gauges continue to be necessary for accurate
estimation of storm rainfall input to catchments during flash flood events, especially in
mountainous catchments. Given economical considerations, a long‐standing problem in
catchment hydrology is to establish optimal placement of a small number of rain gauges to
acquire data on both rainfall depth and spatiotemporal variability of intensity during
extreme storm events. Using weather radar observations and a dense network of 40 tipping
bucket rain gauges, this study examines whether it is possible to determine a reliable “best”
set of rain gauge locations for the Sabino Canyon catchment near Tucson, Arizona, USA,
given its complex topography and dominant storm track pattern. High‐quality rainfall
data are used to evaluate all possible configurations of a “practical” network having from
one to four rain gauges. A multicriteria design strategy is used to guide rain gauge
placement, by simultaneously minimizing the residual percent bias and maximizing the
coefficient of correlation between the estimated and true mean areal rainfall and
minimizing the normalized spatial mean squared error between the estimated and true
spatiotemporal rainfall distribution. The performance of the optimized rain gauge network
was then compared against randomly designed network ensembles by evaluating the
quality of streamflows predicted using the Kinematic Runoff and Erosion (KINEROS2)
event‐based rainfall‐runoff model. Our results indicate that the multicriteria strategy
provided a robust design by which a sparse but accurate network of rain gauges could
be implemented for semiarid basins such as the one studied.
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Geostatistical Analysis of Spatial Variability of Rainfall and Optimal Design of a Rain Gauge Network
Author: DIMITRIS M. PAPAMICHAIL and IRINI G. METAXA | Size: 1.13 MB | Format:PDF | Quality:Unspecified | Publisher: Kluwer Academic Publishers | Year: 1996 | pages: 21
Kriging is a geostatistical estimation technique for regionalized variables that exhibit an
autocorrelation structure. Such a structure can be described by a semivariogram of the observed data.
The punctual&Aging estimate at any point is a weighted average of the data, where the weights are
determined by using the semivariogram and an assumed drift, or lack of drift, in the data. The kriging
algorithm, based on unbiased and minimum-variance estimates, involves a linear system of equations
to calculate the weights. Kriging is applied in an attempt to describe the spatial variability of rainfall
data over a geographical region in northern Greece. Monthly rainfall data of January and June 1987
have been taken from 20 measurement stations throughout the above area. The rainfall data are used
to compute semivariograms for each month. The resulting semivariograms are anisotropic and fitted
by linear and spherical models. Kriging estimates of rainfall and standard deviation were made at
90 locations covering the study area in a rectangular grid and the results used to plot contour maps
of rainfall and contour maps of kriging standard deviation. Verification of the kriging estimates of
rainfall are made by removing known data points and kriging an estimate at the same location. This
verification is known as the jacknifing technique. Kriging errors, a by-product of the calculations,
can then be used to give confidence intervals of the resulting estimates. The acceptable results of the
verification procedure demonstrated that geostatistics can be used to describe the spatial variability
of rainfall. Finally, it is shown how the property of kriging variance depends on the structure and
the geometric configuration of the data points and the point to be estimated can also be used for the
optimal design of the rain gauge network in an area
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