EDP-IM: masonry infill frame

Predicts EDP-IM curves for a masonry infill frame using edp_im_infill. The pushover curve is loaded from two text files (lateral forces and storey displacements) and the backbone is built via get_infill_spo.

Corresponds to test_infill with im_type="sa".

Input files

• infill.json — damping, modal mass (mstar), and participation factor (gamma).

• spo_disps_infill.txt — storey displacement matrix from pushover (rows = load steps).

• spo_shear_infill.txt — base shear vector from pushover.

Running the example

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

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

disp = np.genfromtxt("spo_disps_infill.txt")
rx   = np.genfromtxt("spo_shear_infill.txt")

spo = {"force": rx, "displacement": disp}

body = {
    "backbone": {
        "spo": to_json_serializable(spo),
        "damping": data["damping"],
        "gamma": data["gamma"],
        "mstar": data["mstar"],
        "sdof": False,
    },
    "im_type": "sa",
    "backboneMethod": "spo",
}

# Build the infill backbone and attach the SPO curve
bb = dict(Backbone(body["backbone"], "spo", "infill").backbone)
bb["im_type"] = body["im_type"]
bb["r-plot"], bb["mu-plot"] = get_infill_spo(bb)

result = edp_im_infill(bb)

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

SDOF normalisation

If the pushover forces and displacements are already normalised to SDOF quantities, set "sdof": True and apply the appropriate factors before passing to spo:

f_factor = 800 * 1.3
d_factor = 1.3

spo = {
    "force": rx / f_factor,
    "displacement": disp / d_factor,
}

body["backbone"]["spo"] = to_json_serializable(spo)
body["backbone"]["sdof"] = True