djura.vulnerability_modeller

Seismic vulnerability and loss modelling.

djura.vulnerability_modeller.fragility.mlefit(param1, param2, total_count, count, data)

Maximum likelihood method Performs a lognormal cumulative distribution function fit to the data points based on maximum likelihood method

Parameters:

• param1 (float) – Median of the function, parameter of a statistical model to be found

• param2 (float) – Standard deviation of the function, parameter of a statistical model to be found

• total_count (int) – Number of data points

• count (int) – Number of failures

• data (Union[List, np.ndarray]) – The function, data points

Returns:

Negative Log likelihood to be minimized

Return type:

float

djura.vulnerability_modeller.fragility.fit_fragility(xs, counts, total_count, iml_range=None, beta=0.0)

class djura.vulnerability_modeller.backbone.SPOModel(**data)

Bases: BaseModel

displacement: List[float] | List[List[float]]

force: List[float]

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class djura.vulnerability_modeller.backbone.DispForceModel(**data)

Bases: BaseModel

displacement: List[float] | None

force: List[float] | None

mstar: float | None

gamma: float | None

sdof: bool | None

spo: Dict | None

damping: float | None

residual: float | None

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class djura.vulnerability_modeller.backbone.Backbone(data, method='backbone', model='bilin')

Bases: object

property backbone

djura.vulnerability_modeller.backbone.get_infill_spo(data)

class djura.vulnerability_modeller.demands.Demands(data)

Bases: object

g = 9.81

async estimate_drifts()

async estimate_accelerations()

class djura.vulnerability_modeller.eal.EAL(elr, mafe)

Bases: object

fit_loss_curve()

Fitting of a refined loss curve that passes through the performance limit states

Returns:

Coefficients of the analytical form Fitted ELRs Fitted MAFEs

Return type:

tuple[ndarray, ndarray, ndarray]

Notes

The analytical form is defined as:

\[MAFE = \mathrm{coef}_0 * exp( -\mathrm{coef}_1 * log(ELR) - \mathrm{coef}_2 * log(ELR)^2)\]

# noqa: E501, W605

get_eal(im_range, elr=None, mafe=None)

Computes approximate EAL using the ELRs and MAFEs associated with the performance limit states

Parameters:

• elr (List[float]) – Expected loss ratio (ELR), by default EAL.elr

• mafe (List[float]) – Mean annual frequency of exceedance (MAFE) of a limit state, by default EAL.mafe

Returns:

EAL

Return type:

float

class djura.vulnerability_modeller.pfa_profile.PFAProfile(method, bldg_type, psd, period, heights)

Bases: object

omega = array([], dtype=float64)

pfa_coefs = {'muho': {'rc-infill': {'2-4': [[0.645, -0.178, -0.148, -0.09, 0.136], [0.308, -0.46, 0.055, -0.168, 0.259]], '5-20': [[1.159, -0.027, -0.17, -0.048, 0.076], [0.708, -0.145, -0.206, -0.103, 0.087], [0.161, -0.463, -0.336, -0.291, 0.145], [0.259, -0.438, -0.256, -0.27, 0.196]]}, 'rc-mrf': {'2-4': [[0.23, -0.355, 0.213], [0.046, -0.77, 0.297]], '5-20': [[0.923, -0.029, -0.1], [0.636, -0.151, -0.154], [0.254, -0.34, -0.026], [0.135, -0.537, -0.025]]}}}

djura.vulnerability_modeller.utilities.cdf_lognormal(xs, median, beta)

Return type:

ndarray

djura.vulnerability_modeller.utilities.uncensored_regression(pars, x, y)

Uncensored regression to estimate the expected EDP given IM and the respective uncertainty due to record-to-record variability

Parameters:

• pars (List[float]) – Fitting function parameters

• x (ndarray) – For example, Intensity measure [im] values in m/s

• y (ndarray) – For example, Engineering demand parameters (EDPs)

Returns:

loss value to minimize

Return type:

float

djura.vulnerability_modeller.utilities.censored_regression(pars, x_unc, x_cens, y_unc, y_cens)

Censored regression to estimate the expected EDP given IM and the respective uncertainty due to record-to-record variability

Parameters:

• pars (List[float]) – Fitting function parameters

• x_unc (ndarray) – For example, uncensored Intensity measure [im] values in m/s

• x_cens (ndarray) – For example, censored Intensity measure [im] values in m/s

• y_unc (ndarray) – For example, uncensored Engineering demand parameters (EDPs)

• y_cens (float) – For example, censored Engineering demand parameters (EDPs)

Returns:

loss value to minimize

Return type:

float

djura.vulnerability_modeller.utilities.residual_log_dist(pars, x, y)

djura.vulnerability_modeller.utilities.get_loss_uncertainties(loss)

Estimate uncertainties

References

Silva, V. (2019) Uncertainty and correlation in seismic vulnerability functions of building classes. Earthquake Spectra. DOI: 10.1193/013018eqs031m.

Parameters:

loss (ndarray) – Loss values

Returns:

Uncertainties

Return type:

ndarray

djura.vulnerability_modeller.utilities.get_cdf(theta, beta, iml_range=None)

djura.vulnerability_modeller.utilities.set_ductilities(ductility_f)

djura.vulnerability_modeller.utilities.deep_merge(dict1, dict2)

Recursively merge dict2 into dict1