simdesign.rcmrf.bcim.randomization

This module provides samplers for randomization in the building portfolio generation process.

class simdesign.rcmrf.bcim.randomization.Sampler(num_sample, seed=None)[source]

Bases: object

Sampler for regular moment resisting frame structures.

Variables:

  • sample_size (int) – Number of samples to be generated.

  • staircase_span_length_x (np.ndarray) – Staircase span length in X direction.

  • typical_span_length_x (np.ndarray) – Typical span length in X direction.

  • typical_span_length_y (np.ndarray) – Typical span length in Y direction.

  • layout (np.ndarray) – Building floor layouts.

  • typical_storey_height (np.ndarray) – Typical storey heights.

  • ground_storey_height (np.ndarray) – Ground storey heights.

  • slab_type (np.ndarray) – Slab typology. 1: Two-way solid slab. 2: One-way solid slab. 3: One-way composite slab with ceramic blocks and RC joists or pre-stressed beams.

  • beam_type (np.ndarray) – Beam typology. 1: Wide beams. 2: Emergent beams.

  • column_section (np.ndarray) – Column cross-section. 1: Square solid section. 2: Rectangular solid section.

  • steel_grade (np.ndarray) – Steel material class ID, e.g., ‘S400’.

  • concrete_grade (np.ndarray) – Concrete material class ID, e.g., ‘C20/25’.

  • quality (np.ndarray) – Construction quality. 1: High quality. 2: Moderate quality. 3: Low quality.

  • ext_infill_type (np.ndarray) – Exterior masonry infill typology in terms of strength. 1: Weak. 2: Medium. 3: Strong.

  • int_infill_type (np.ndarray) – Interior masonry infill typology in terms of strength. 1: Weak. 2: Medium. 3: Strong.

  • infill_conf (np.ndarray) – Masonry infill wall configuration IDs. 1: Exterior only, Regular over the height, XX + YY. 2: Exterior only, Pilotis, XX + YY. 3: Exterior only, Pilotis, XX. 4: Exterior only, Pilotis, YY. 5: Exterior + Interior, Regular over the height, XX + YY. 6: Exterior + Interior, Pilotis, XX + YY. 7: Exterior + Interior, Pilotis, XX. 8: Exterior + Interior, Pilotis, YY. 9: Interior only, Regular over the height, XX + YY. 10: Interior only, Pilotis, XX + YY. 11: Interior only, Pilotis, XX. 12: Interior only, Pilotis, YY.

Parameters:

  • num_sample (int)

  • seed (int | None)

staircase_span_length_x: ndarray
typical_span_length_x: ndarray
typical_span_length_y: ndarray
layout: ndarray
typical_storey_height: ndarray
ground_storey_height: ndarray
slab_type: ndarray
beam_type: ndarray
column_section: ndarray
steel_grade: ndarray
concrete_grade: ndarray
quality: ndarray
ext_infill_type: ndarray
int_infill_type: ndarray
infill_conf: ndarray
sample_size: int
set_stair_span_length_x(lower_bound, upper_bound)[source]

Set staircase span lengths.

Staircase span lengths are represented by uniform distribution.

Parameters:

  • lower_bound (float) – Lower boundary of the output interval. All values generated will be greater than or equal to lower bound.

  • upper_bound (float) – Upper boundary of the output interval. All values generated will be less than or equal to upper bound.

Returns:

Staircase span lengths.

Return type:

List[float]

Examples

>>> lower_bound = 2.8
>>> upper_bound = 3.2
set_typical_span_length(corr_coeff, lower_bound, upper_bound, theta, std_ln)[source]

Set typical span lengths in x and Y directions.

Typical span lengths are represented with truncated log-normal distribution. It is assumed that span lengths are correlated. The samples are generated from multivariate truncated lognormal distribution.

Parameters:

  • corr_coeff (float) – Correlation coefficient (between span lengths x and y).

  • lower_bound (Tuple[float]) – Lower bound for truncated log-normal distribution (x, y).

  • upper_bound (Tuple[float]) – Upper bound for truncated log-normal distribution (x, y).

  • theta (Tuple[float]) – Median of log-normal distribution.

  • std_ln (Tuple[float]) – Logarithmic standard deviation of the log-normal distribution (x, y).

Return type:

Tuple[List[float]]

Notes

~LN(ln(theta), sigma)

Returns:

Span lengths (x, y).

Return type:

Tuple[List[float]]

Parameters:

  • corr_coeff (float)

  • lower_bound (Tuple[float])

  • upper_bound (Tuple[float])

  • theta (Tuple[float])

  • std_ln (Tuple[float])

Examples

>>> corr_coeff = -0.92
>>> lower_bound = (3.5, 3.5)
>>> upper_bound = (7.5, 6.0)
>>> theta = (4.5, 4.5)
>>> std_ln = (0.35, 0.35)
set_layout(layouts)[source]

Set plan layout IDs for buildings.

Parameters:

layouts (List[str]) – Building layouts.

Returns:

Random building layout assignments.

Return type:

List[str]

Examples

>>> layouts = ["B01", "B02", "B03", "B04", "B04b",
...            "B05", "B06", "B07", "B08", "B09", "B10"]
set_typical_storey_height(cv, mu, lower_bound, upper_bound)[source]

Set typical storey heights.

Typical storey heights are represented with truncated log-normal distribution.

Parameters:

  • cv (float) – Coefficient of variation, s/mu.

  • mu (float) – Mean of normal distribution.

  • lower_bound (float) – Lower bound of truncated log-normal distribution.

  • upper_bound (float) – Upper bound of truncated log-normal distribution.

Returns:

Generated typical storey heights.

Return type:

List[float]

Examples

>>> cv = 0.07
>>> mu = 2.90
>>> lower_bound = 2.3
>>> upper_bound = 3.8
set_ground_storey_height(factors, probabilities, max_height)[source]

Set ground storey heights.

Parameters:

  • factors (List[float]) – Factors applied on typical storey heights to obtain ground storey heights.

  • probabilities (List[float]) – Corresponding probabilities of factors.

  • max_height (float) – Maximum considered ground storey height.

Returns:

Randomised ground storey heights.

Return type:

List[float]

Notes

1. ground_storey_height = min(factor * typical_storey_heights, max_height) 2. Factors have different probabilities.

Examples

>>> factors = [1.0, 1.1, 1.2, 1.3, 1.4]
>>> probability = [0.55, 0.10, 0.20, 0.10, 0.05]
>>> max_height = 4.20
set_slab_type(ss1_prob_given_ss1_or_hs, ss2_prob_given_ss2_or_hs, max_ss_short_span, max_ss2_aspect_ratio)[source]

Set slab typology following a span length based decision tree.

Parameters:

  • ss1_prob_given_ss1_or_hs (float) – Probability of having SS1 type slab given that the slab type is either SS1 or HS.

  • ss2_prob_given_ss2_or_hs (float) – Probability of having SS2 type slab given that the slab type is either SS2 or HS.

  • max_ss_short_span (float) – Upper limit for the shorter span length in solid slabs (SS1, SS2).

  • max_ss2_aspect_ratio (float) – Upper limit for the ratio of maximum to minimum span lengths (aspect ratio) in SS2 slabs.

Returns:

Slab typologies.

Return type:

List[Literal[1, 2, 3]]

Notes

1. Slab typologies: Solid two-way cast-in-situ slabs (1: SS2), Solid one-way cast-in-situ slabs (2: SS1), Composite slabs with pre-fabricated joists and ceramic blocks (3: HS). 2. Slab type is assigned as HS, if minimum span length is greater than the upper limit for minimum solid slab span length (minimum span length <= max_ss_short_span). 3. Otherwise, if the ratio of maximum to minimum span lengths is greater or equal to max_ss2_aspect_ratio, slab type is randomly as SS1 or HS based on ss1_prob_given_ss1_or_hs. 4. Otherwise, slab type is determined randomly as SS2 or HS based on ss2_prob_given_ss2_or_hs.

Examples

>>> ss1_prob_given_ss1_or_hs = 0.50
>>> ss2_prob_given_ss2_or_hs = 0.65
>>> max_ss_short_span = 6.0  # in meters
>>> max_ss2_aspect_ratio = 2.0
set_beam_type(wb_prob_given_hs)[source]

Set beam types.

Parameters:

wb_prob_given_hs (float) – Probability of having wide beams (WB) given slab type is HS.

Returns:

Beam typologies.

Return type:

List[Literal[1, 2]]

Notes

  1. If slabs are solid slabs, beams are set as emergent beams (2).

  2. Otherwise, randomly assign wide (1) or emergent beam (2) based on the user-defined wb_prob_given_hs parameter.

Examples

>>> wb_prob_given_hs = 0.50
set_column_type(square_prob)[source]

Set the type of column or cross-section geometry.

Parameters:

square_prob (float) – Probability of having square columns.

Returns:

Randomly assigned column types for each building, square (1) or rectangular (2).

Return type:

List[Literal[1, 2]]

Examples

>>> square_prob = 0.50
set_material_class(material_class, probability, material)[source]

Randomly assign material classes to buildings given their occurrence probabilities.

Parameters:

  • material_class (List[str]) – List of defined material classes.

  • probability (List[float]) – Probabilities of having different material classes. Sum should be equal to 1.0.

  • material (Literal["steel", "concrete"]) – Type of material, which can be either “steel” or “concrete”.

Returns:

Randomly assigned material classes for building.

Return type:

List[str]

Examples

>>> material_class = ["S240", "S400"]
>>> probabilities = [0.60, 0.40]
set_construction_quality(quality_ids, probabilities)[source]

Set construction quality in a randomised fashion.

Parameters:

  • quality_ids (List[int]) – Quality identifiers, IDs.

  • probabilities (List[float]) – Corresponding probabilities for construction qualities. Sum should be equal to 1.0.

Returns:

Construction quality sample.

Return type:

List[int]

Examples

>>> quality_ids = [1, 2, 3]
>>> probabilities = [0.6, 0.3, 0.1]
set_ext_infill_type(infill_types, probabilities)[source]

Set masonry exterior infill wall typologies in a randomised fashion.

Parameters:

  • infill_types (List[int]) – Infill typology IDs.

  • probabilities (List[float]) – Corresponding probabilities for infill typologies. Sum should be equal to 1.0.

Returns:

Exterior masonry infill typology sample.

Return type:

List[int]

Examples

>>> infill_types = [1, 2, 3]
>>> probabilities = [0.6, 0.3, 0.1]
set_int_infill_type(infill_types, probabilities)[source]

Set masonry interior infill wall typologies in a randomised fashion.

Parameters:

  • infill_types (List[int]) – Infill typology IDs.

  • probabilities (List[float]) – Corresponding probabilities for infill typologies. Sum should be equal to 1.0.

Returns:

Interior masonry infill typology sample.

Return type:

List[int]

Notes

It is assumed that interior infills should be weaker than exterior ones, if they co-exist.

Examples

>>> infill_types = [1, 2, 3]
>>> probabilities = [0.33, 0.34, 0.33]
set_infill_conf(infill_confs, probabilities)[source]

Set masonry infill wall configurations in a randomised fashion.

Parameters:

  • infill_confs (List[int]) – Infill configuration IDs.

  • probabilities (List[float]) – Corresponding probabilities for infill configuration IDs. Sum should be equal to 1.0.

Returns:

Masonry infill configuration sample.

Return type:

List[int]

Examples

>>> infill_confs = [1, 2, 5, 6]
>>> probabilities = [0.25, 0.25, 0.25, 0.25]
reset()[source]

Reset all the attributes of the Sampler class instance.

Return type:

None