Probabilistic seismic performance of hybrid CFS-HRS wall systems with curved dampers: A mechanics-informed Monte Carlo approach

Hybrid cold-formed steel (CFS) and hot-rolled steel (HRS) wall systems can improve the lateral resistance of lightweight construction, but their seismic performance depends strongly on the configuration of supplemental energy-dissipating dampers. Conventional incremental dynamic analysis (IDA) can quantify collapse fragility, but it is computationally demanding for broad damper-parameter studies. This study develops a mechanics-informed Monte Carlo (MC) framework for probabilistic seismic performance assessment of a hybrid CFS-HRS wall system equipped with curved steel dampers (CSDs). A calibrated finite element (FE) database of 36 CSD configurations was used, covering six installation angles (30°, 45°, 55°, 60°, 65°, and 75°), two plate thicknesses (10 and 13 mm), and three plate depths (30, 40, and 50 mm). Closed-form stiffness, yield, ductility, and collapse-capacity relationships were combined with MC sampling of material and modeling uncertainties to derive lognormal fragility functions and reliability indices at intensity measures (IMs) of 0.20, 0.50, and 1.00 g. The results show that installation angle is the governing parameter: the median device-level capacity index decreases by approximately 70% as the angle increases from 30° to 75°. Increasing plate thickness from 10 to 13 mm improves the median capacity by approximately 30%, while increasing plate depth from 30 to 50 mm improves the median capacity by approximately 67%. The logarithmic dispersion remained nearly constant, ( β ln = 0.45 ) , indicating that geometry mainly shifts the median capacity rather than altering relative uncertainty. The reported ( S a , med ) values should be interpreted as HWP subassembly capacity indices rather than complete building-level collapse capacities. The proposed framework provides a computationally efficient and physically interpretable tool for preliminary CSD configuration screening before system-level nonlinear dynamic verification.