In-service deviations of viscous dampers can reduce the collapse safety margin of viscous-damped structures under strong earthquakes. This study examines two representative mechanisms: global degradation of the damper group and local failure of a subset of dampers. Incremental dynamic analyses are conducted for five damper-state scenarios using the 22 far-field ground-motion records recommended by ATC-63. To support reliability-oriented, uncertainty-aware collapse-capacity comparison with limited records, three complementary probabilistic inference frameworks are developed: an event-based fragility model using binary collapse indicators, a drift-margin model leveraging continuous deformation information from non-collapse responses, and a fusion model that combines both sources via a weighted composite likelihood with fusion strength governed by the weight w. For each scenario, the capacity scale parameter μ_m is reported as IM_50,m, and record-level bootstrap resampling is used to construct interval estimates. Multi-scenario effects are further summarized by the ensemble mean reduction b and inter-path dispersion σ_damper, offering compact measures of systematic shift and pathway-to-pathway variability. Results indicate a dominant systematic downward shift in median collapse capacity, with IM_50,m reduced by approximately 2.4–2.9% overall, whereas differences among degradation pathways are secondary and bounded by the intervals. Scenario rankings remain consistent across the three frameworks; fusion outputs show weak sensitivity to w and yield tighter interval constraints on σ_damper than the event-only baseline. The resulting interval-based parameters enable risk- and reliability-informed interpretation of degradation effects and provide a consistent basis for uncertainty quantification in probabilistic performance comparisons across scenarios.

Seismic collapse can cause catastrophic losses, and acceptable annual collapse probability with its CMR target is a core metric in performance-based design. Existing ATC-63-based CMR research mainly addresses non-damped systems and often uses a single lumped dispersion, obscuring damper-reliability contributions and hindering alignment with CECS 392 limits. This study proposes a unified, code-consistent decision framework for acceptable annual collapse probability and CMR that jointly accounts for seismic hazard and damper-related uncertainty. The total collapse dispersion is decomposed as ${\sigma }_{\mathrm{total},\mathrm{damp}}^2={\sigma }_{\mathrm{base}}^2 +{ \sigma }_{\mathrm{damper}}^2$, where ${\sigma }_{\mathrm{b}\mathrm{a}\mathrm{s}\mathrm{e}}$ represents background dispersion independent of dampers and ${\sigma }_{\mathrm{d}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{e}\mathrm{r}}$ captures incremental uncertainty induced by degradation and partial failure. A code-designed viscous-damped RC frame is evaluated under three scenarios (nominal damping, 20% damping-coefficient reduction, and 7% random damper failures). Using the same 14 records and $\mathrm{S}\mathrm{a}\left({\mathrm{T}}_1,5\%\right)$ as the intensity measure, multi-stripe IDA and Probit-based lognormal fragility fitting yield median collapse intensities $\mathrm{S}\mathrm{c}\approx 2.18-2.24 \mathrm{g}$, with only ~2–3% reduction under mild degradation/failure. A random-effects variance decomposition identifies σ_damper ≈ 0, indicating a limited marginal contribution of damper-related uncertainty within the degradation range considered in this study. Closed-form relationships between annual collapse rate, $\mathrm{S}\mathrm{c}$, and ${\sigma }_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l},\mathrm{d}\mathrm{a}\mathrm{m}\mathrm{p}}$ are then derived under a power-law hazard model and inverted to generate acceptable-risk intervals and CMR target curves/matrices. Results show that higher design intensity and larger ${\sigma }_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l},\mathrm{d}\mathrm{a}\mathrm{m}\mathrm{p}}$ demand substantially higher CMR, highlighting potential risk underestimation when relying solely on nominal CMR. The framework enables explicit identification of damper-related uncertainty from limited collapse data and provides a practical workflow for collapse-prevention design and post-assessment under explicitly defined scenario conditions, with a clear pathway for extension to broader scenario spaces.

To address the small ATC-63 record set for collapse-oriented motion selection and the limited interpretability of data-driven approaches, this study proposes a framework for identifying structural-collapse-critical ground motions. Using 5074 records from the PEER NGA-West2 database, we applied STA/LTA event detection and extracted multi-source features. A Gaussian mixture model (GMM) was then used to perform unsupervised clustering and identify four physically interpretable groups. LightGBM, XGBoost, and Random Forest were employed to test the separability of the cluster labels, with all three models achieving F1 scores above 0.89 and LightGBM reaching an accuracy of about 93%. SHAP-based feature-importance analysis was used at the model level to clarify feature contributions and improve interpretability. Cluster 2 exhibits markedly higher relative seismic energy, stronger time-domain variability, and more dominant frequencies, forming a typical strong-motion hazard signature. For external engineering verification, 22 ATC-63 far-field records were mapped onto the full dataset to examine cluster-level enrichment and coverage. Cluster 2 shows significant enrichment in engineering markers and high coverage and is therefore identified as the collapse-sensitive phenotype cluster (COP). Overall, the framework provides a technical basis for ground-motion selection in collapse assessment, fragility analysis, and design evaluation.