EDP-IM: bilinear system with file-based backbone¶

Predicts EDP-IM curves for a bilinear system where the backbone is provided as a JSON file rather than defined inline. Three backbone input methods are supported: "backbone" (parametric), "idealized" (force-displacement points), and "spo" (pushover curve).

Corresponds to test_backbone with backbone1.json.

Input files¶

backbone1.json — parametric backbone (damping, ductility, hardening ratio, period).
backbone2.json, backbone3.json — idealized force-displacement backbone.
backbone4.json, backbone5.json — SPO pushover backbone (requires separate SPO text files).

Parametric backbone (`"backbone"` method)¶

import json
from djura.vulnerability_modeller.backbone import Backbone
from djura.edp_im.predict import edp_im

with open("backbone1.json", encoding="utf-8") as f:
    data = json.load(f)

backbone = Backbone(data, "backbone", "bilin").backbone

result = edp_im({
    "method": "shahnazaryan-oreilly",
    "hysteresis": "bilin",
    "im_type": "sa",
    "backboneMethod": "backbone",
    "backbone": backbone,
})

print("Status:", result["status"])

SPO pushover backbone (`"spo"` method)¶

When the backbone is derived from a pushover curve, load the force and displacement arrays from text files and attach them to the backbone data:

import json
import numpy as np
from djura.vulnerability_modeller.backbone import Backbone
from djura.vulnerability_modeller.utilities import to_json_serializable
from djura.edp_im.predict import edp_im

with open("backbone4.json", encoding="utf-8") as f:
    data = json.load(f)

# spo_topdisp.txt: columns are [force, disp_storey1, disp_storey2, ...]
spo = np.loadtxt("spo_topdisp.txt")
data["spo"] = {
    "force": spo[:, 0],
    "displacement": spo[:, 1:],
}

backbone = Backbone(
    to_json_serializable(data), "spo", "bilin"
).backbone

result = edp_im({
    "method": "shahnazaryan-oreilly",
    "hysteresis": "bilin",
    "im_type": "sa",
    "backboneMethod": "spo",
    "backbone": backbone,
})

print("Status:", result["status"])

Using average spectral acceleration (Sa_avg)¶

Replace "im_type": "sa" with "im_type": "sa_avg" in any of the calls above to predict EDP-IM relationships in terms of average spectral acceleration instead of Sa(T):

result = edp_im({
    "method": "shahnazaryan-oreilly",
    "hysteresis": "bilin",
    "im_type": "sa_avg",
    "backboneMethod": "backbone",
    "backbone": backbone,
})