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Past failure risk analyses of wind-impacted wood-frame structural load paths have tended to consider simplified resistance models that account for a few key load path connections, in which connection capacity distributions are generally based on benchmark experimental results. However, recent post-storm reconnaissance studies have demonstrated that connections in the load path of light wood-frame structures are themselves composed of multiple elements with many configurations and possible failure modes. This study presents a flexible approach for modeling wind uplift resistance in wood-frame load paths that includes a more exhaustive set of potential failure points yet is computationally efficient and readily adaptable to various load paths composed of different assemblages of structural members and connections. In this framework, ultimate capacities of connections and wood members are either based on design equations provided in the National Design Specification for Wood Construction or another applicable standard or computed from a comparable mechanics-based model. Analytical capacity estimates for roof sheathing, roof-to-wall connections, and wall-to-slab-foundation connections accord well with the range of published experimental results for these connections. Capacities of connections that act in parallel are summed to transform the load path into an analogous load chain of series components. System-level wind uplift resistance, defined by the weakest component in series, is evaluated by Monte Carlo simulation. By providing a more complete description of resistance than previous simplified models have done while avoiding the expense of a detailed finite-element or other solid mechanics model, the method proposed here holds promise as a rapid, consistent, and accurate way to quantify wind resistance in any arbitrary wood-frame load path, with applications including insurance risk analysis, hybrid data science frameworks utilizing post-storm reconnaissance data, and estimation of hazard intensity from structural damage observations. 

A framework is presented for evaluating the sensitivity behavior of parameters in a structural load path with respect to wind hazard analytical fragilities. A preliminary analysis applies the framework to a vertical light wood-frame load path. A variance-based sensitivity analysis method is employed to compute first-order sensitivity indices of all input parameters on the basis of load path system resistance, fragility median, and fragility standard deviation. The results indicate that a sensitivity analysis predicated on fragility median provides a reasonable description of load path parameter influence and may serve as a useful complementary tool alongside traditional load path fragility approaches. The framework can be useful for identifying which fragility model parameters are most essential out of a broader suite of possible parameters, and for offering guidance to reconnaissance efforts for focusing on the most influential perishable data to capture following extreme hazard events. 

Recent advances in post-windstorm reconnaissance have accelerated the amounts of perishable building performance data being collected after extreme windstorms, necessitating better frameworks for knowledge discovery from the data. One particularly promising approach to this need is Bayesian Networks (BN), which have grown in their application in natural hazards research due to their ability to explicitly model causal factors. In this study, a Naïve Bayes Network (NBN) was first developed to observe the influence of wind speed ratio, roof shape, number of stories, roof cover, and pre/post-IBC (2002) on the damage class of a structure and predict the probability of each damage class given a specified scenario. This initial model was derived solely from empirical data and the parameters of influence are modelled with conditional independence, and limiting the model’s use. An illustrative hybrid Bayesian Network is also proposed which combines empirical data, known wind engineering theory, and expert opinion to formulate a more holistic model of structural performance in windstorms better suited for parameter inference and building performance predictions. 

This study presents a framework for global sensitivity analysis of wind uplift resistance in wood-frame residential structures. The vertical load path is modeled probabilistically as an assemblage of connections, with resistance distributions based on connection design capacity and cumulative dead load. An established sensitivity analysis approach is applied to the load path resistance model to evaluate the influence of the input parameter set on the system resistance, which is taken as the resistance of the weakest connection in series. A preliminary analysis illustrates the potential of the framework as a useful tool for assessing the relative importance of structural attributes for wind resistance, adaptable to any arbitrary vertical load path and parameter set. The framework also facilitates the evaluation of the relative vulnerability of different load path configurations from structure to structure. 

This study describes a hybrid framework for post-hazard building performance assessments. The framework relies upon rapid imaging data collected by regional scout teams being integrated into broader data platforms that are parsed by virtual teams of hazards engineers to efficiently create robust performance assessment datasets. The study also pilots a machine-in-the-loop approach whereby deep learning and computer vision-based models are used to automatically define common building attributes, enabling hazard engineers to focus more of their efforts on precise damage quantification and other more nuanced elements of performance assessments. The framework shows promise, but to achieve optimal accuracy of the automated methods requires regional tuning. 

This study describes a hybrid framework for post-hazard building performance assessments. The framework relies upon rapid imaging data collected by regional scout teams being integrated into broader data platforms that are parsed by virtual teams of hazards engineers to efficiently create robust performance assessment datasets. The study also pilots a machine-in-the-loop approach whereby deep learning and computer vision-based models are used to automatically define common building attributes, enabling hazard engineers to focus more of their efforts on precise damage quantification and other more nuanced elements of performance assessments. The framework shows promise, but to achieve optimal accuracy of the automated methods requires regional tuning. 

This study presents a framework for global sensitivity analysis of wind uplift resistance in wood-frame residential structures. The vertical load path is modeled probabilistically as an assemblage of connections, with resistance distributions based on connection design capacity and cumulative dead load. An established sensitivity analysis approach is applied to the load path resistance model to evaluate the influence of the input parameter set on the system resistance, which is taken as the resistance of the weakest connection in series. A preliminary analysis illustrates the potential of the framework as a useful tool for assessing the relative importance of structural attributes for wind resistance, adaptable to any arbitrary vertical load path and parameter set. The framework also facilitates the evaluation of the relative vulnerability of different load path configurations from structure to structure.