Next-generation non-linear and collapse prediction pre-trained XGBoost models for short to long period systems.
Project description
XGBoost - $\rho-\mu-T$
Next-generation non-linear and collapse prediction models for short to long period systems via machine learning methods
The machine learning approach: Exterme Gradient Boosting (XGBoost)
Makes predictions for a strength ratio - ductility - period relationships
Key arguments:
- $R$ - strength ratio based on spectral acceleration
- $\rho$ - strength ratio based on average spectral acceleration
- $\mu$ - ductility
- $T$ - period
$$ R=\frac{Sa(T)}{Sa_y} $$
$$ \rho_2=\frac{Sa_{avg,2}(T)}{Sa_y} $$
$$ \rho_3=\frac{Sa_{avg,3}(T)}{Sa_y} $$
where
- $Sa(T)$ stands for spectral acceleration at fundamental period
- $Sa_y$ stands for spectral acceleration at yield
- $Sa_{avg,2}(T)$ stands for average spectral acceleration computed at periods $∈ [0.2T:2T]$
- $Sa_{avg,3}(T)$ stands for average spectral acceleration computed at periods $∈ [0.2T:3T]$
Installation
pip install xgb-rhomut
Example prediction
Dynamic strength ratio prediction of non-collapse scenarios at a dynamic ductility level of 3.0:
import xgbrhomut
model = xgbrhomut.XGBPredict(im_type="sa_avg", collapse=False)
prediction = model.make_prediction(
period=1,
damping=0.05,
hardening_ratio=0.02,
ductility=4,
dynamic_ductility=3.0
)
prediction:
{
"median": float,
"dispersion": float
}
Other methods
xgbrhomut.r_mu_t.ec8.strength_ratio(mu=3, T=1, Tc=0.5)
Limitations
Limitations in terms of input parameters are:
- $T$ ∈ [0.01, 3.0] seconds
- $\mu$ ∈ [2.0, 8.0]
- $\xi$ ∈ [2.0, 20.0] %
- $a_h$ ∈ [2.0, 7.0] %
- $a_c$ ∈ [-30.0, -100.0] %
- $R$ ∈ [0.5, 10.0]
where
- $T$ stands for period
- $\mu$ stands for ductility
- $\xi$ stands for damping
- $a_h$ stands for hardening ratio
- $a_c$ stands for softening ratio (necessary to compute fracturing ductility, where collapse is assumed)
Predictions made using the non-linear analysis resutls of 7292 unique SDOF systems amounting in total to 26,000,000 observations (collapse + non-collapse).
References
- Shahnazaryan D., O'Reilly J.G., 2023, Next-generation non-linear and collapse prediction models for short to long period systems via machine learning methods, Under Review
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for xgb_rhomut-1.1.3-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 852c577d415c6602d8a380a7e9c50822969a367e1a171afab0627562e88e94c6 |
|
MD5 | d6282f7f7ff6652ba886dd2241c3c005 |
|
BLAKE2b-256 | e963586d5eea563c2be6bc95406c9e1a7b2437aac76896d26aa693a724898d7a |