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Contains tools for calculating structural member cross sectional properties and databases of standard cross sectional properties.

Project description


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This package contains tools for calculating structural member cross sectional properties in Python. In addition, it contains a SQLite database of standard cross sectional properties for which data can be acquired via query functions or instantiated directly into a new CrossSection object.

Calculable cross sectional properties include:

  • Cross sectional area
  • Centroid
  • Second moment of the area (moment of inertia)
  • Radius of gyration
  • Section modulus
  • Principal angles
  • Values about the principal axes for the above properties

Composite sections that consist of multiple shapes are also supported, such as those shown below. Here, the shapes shown in blue are added to the cross section, while those shown in red are subtracted cutouts.

Composite Section Example


The package may be installed via pip by running the below command:

pip install xsect

Examples Usages

The following sections outline some possible uses for this package.

Quick Access to Properties

Whether you are performing quick calculations, perhaps through the use of a Jupyter notebook, or a more complex calculation, you can use XSect to reduce the amount of input required for calculations. Rather than turning to references to lookup and manually input properties for members, you can create cross sections simply by passing the name of the member into the appropriate initializer. For example:

>>> xsect.CrossSection.from_aisc('L8x8x1-1/8')
CrossSection(name='L8X8X1-1/8', area=16.8, ...)

If a property is not contained in the database, you can rapidly calculate the properties given a series of (x, y) boundary points or use one of the built-in cross section summary functions to calculate the properties for a specific shape. For example:

>>> odict = xsect.cruciform_summary(8, 8, 1.125)
>>> odict
{'area': 66.9375,
 'x': 0.0,
 'y': 0.0,
 'width': 16.0,
 'height': 16.0,
 'inertia_x': 781.0517578125,
 'inertia_y': 781.0517578125,
 'inertia_j': 1562.103515625,
 'inertia_xy': 0.0,
 'inertia_z': 781.0517578125,
 'gyradius_x': 3.415900115553699,
 'gyradius_y': 3.415900115553699,
 'gyradius_z': 3.415900115553699,
 'elast_sect_mod_x': 97.6314697265625,
 'elast_sect_mod_y': 97.6314697265625,
 'elast_sect_mod_z': 97.6314697265625}

This can be used to quickly generate a cross section by unwrapping the dictionary within the CrossSection initializer:

>>> xsect.CrossSection('4L8x8x1.125', **odict)
CrossSection(name='4L8x8x1.125', area=66.9375, ...)

Design Optimization

If you are creating a Python application for analyzing and optimizing structures, you could use XSect to pull various cross sections from the standard sections database to perform analysis via an iterative scheme. You could also calculate some required properties for the member and use a database filter to acquire the lightest cross section of a particular shape given that criteria. For example, if you were designing a member for a known maximum tensile force, you could calculate its required cross sectional area and perform a filter similar to the below to get the lightest member:

>>> xsect.filter_aisc(["type='L'", 'area>28'], order=['unit_weight'])
  type          name T_F  unit_weight  area     d
0    L  L12X12X1-1/4   F         96.4  28.4  12.0
1    L  L12X12X1-3/8   F        105.0  31.1  12.0

This returns a data frame of all "L" shape sections with areas greater than 28 in ascending order of unit weight. The first row is, naturally, the lightest member available meeting those conditions.

Likewise, if you are designing a brand new cross section, you could use one of the provided shape functions or create your own custom function to generate its boundary points, then calculate the requisite properties for your design.

Database Sources

The properties contained in the SQLite database are acquired from the following sources:

AISC Shapes

The database includes steel shapes from the American Institute of Steel Construction (AISC), which were taken from the below publicly available locations. For variable descriptions, please consult the README included with their data.

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