The objective of this research is to develop a new framework for performance-based design and assessment of buildings subjected to large snow loads. Snow storms in recent years in the U.S. have caused structural failure, requiring costly repairs, interrupting business, damaging building contents and endangering life safety. Performance-based methods can improve the current approach to snow engineering by explicitly accounting for the risks of snow-induced collapse and building closure in decisions about structural design. The research approach progresses from the development of a probabilistic modular framework to relate snow hazard to structural response and building performance, to nonlinear simulation modeling and probabilistic assessment of the risk of collapse and building closure in structures subjected to snow loads, and to the application of snow load design procedures in a multi-hazard context. Deliverables include the demonstration of the application of the performance-based framework for snow design to typical flat-roof and special arena-type structures, tools for evaluating snow design decisions and building code provisions, documentation of research results, and a colloquia series for undergraduate researchers in civil engineering at CU.
This research seeks to develop a new tool for design of buildings to resist snow loads, based on improved characterization of risks of structural failure and building closure. These advancements will facilitate decisions to minimize risk to life and property, leading to improved design of individual buildings and reexamination of code provisions for snow loading. These developments are particularly timely due to increasingly variable weather patterns associated with global climate change. The project will engage a diverse group of undergraduate researchers to promote student interest in structures and hazards research and in pursuing graduate students in civil engineering. Undergraduate and graduate researchers will participate in a new department research colloquia organized by the P.I to increase engagement in the department and wider scholarly community.
This project develops a framework for performance-based design and assessment framework for structures subjected to snow loads. Large snow loads can cause significant damage to buildings, requiring costly repairs, interrupting business, and potentially endangering life safety. Moreover, snow loads dominate roof design in many parts of the country. Performance-based engineering is a methodology for design and assessment of engineered facilities, which ensures that building performance under normal and extreme loads meets the needs of owners, occupants and the public. This is accomplished through a feedback mechanism, which links performance metrics, predicted using probabilistic approaches and building simulation, to decisions about structural design and assessment. The project has furthered understanding of the design of structures to resist snow loads in four major areas. (1) Two new databases of snow-induced building failures were created to identify technical and other factors associated with elevated risk. The first database was generated using archived newspaper articles published between 1979 and 2009. The findings show that many incidents impact warehouses, strip malls, and other structures whose failure generally garners little attention, but have a large influence on communities. The second data collection effort built on the first, using a newly-developed survey instrument to gather additional data from owners and facility managers about the physical and economic impacts of snow-related building failures. These responses demonstrate that certain structural types (especially timber roofed) tend to experience higher levels of damage. On average, the buildings cost $166 per gross m2 to repair, and interrupted business or other operations for 122 days. (2) The project has generated new data quantifying the risk of collapse or extreme damage of modern buildings under uniform and drifted loads by exercising the proposed performance-based. Results show that roofs designed for the factored ultimate loads, but without considering serviceability deflection limits, do not achieve the level of reliability targeted by American building codes. The inclusion of deflection limits in design, reduces risk of damage in frequent snow events, and improves the safety assessment. The performance assessments are highly dependent on location and seasonal snowfall patterns, which alter the characteristics of the annual snow load distributions. This result implies that designing for uniform hazard (i.e. the 50 year ground snow load) does not ensure uniform risk. A byproduct of this study has been the development of nonlinear models of open web steel joist supported roof structures, which are very common and thought to vulnerable to snow loading. The simulation models developed in this project utilize nonlinear fiber elements to represent yielding and buckling in joist components and concentrated springs to capture connection flexibility and fracture. These models were exercised to explore how the behavior of these structures varies with different design characteristics, such as joist span and joist spacing, under both uniform and drifted snow loads. (3) Improved methods for determining design ground snow loads for input to building roof design and for estimating roof snow loads in a performance-based context have been developed. In the context of ground snow loads, two of the primary difficulties in estimating ground snow loads are: (1) insufficient historical weather records of snow fall and (2) lack of information to convert snow depth, which is measured at more weather stations, to snow loads, which are needed for roof design. We have developed an approach that takes advantage of an existing hydrological snowpack model, SNODAS. Multivariate cluster analysis of SNODAS data is used to regionalize weather stations based on shared properties. This clustering is useful for spatial interpolation of data between sites at which recordings are available. In addition, SNODAS predictions of the snow water equivalent are used to generate more rational density relationships for converting snow depth to load that are unique to each cluster. In the context of roof snow loads, the study has generated new probabilistic models that can be used to estimate the load on the roof, given the ground snow load and building and meteorological characteristics. Robust evaluation of these models is ongoing. Intellectual Merit: The framework for performance-based snow engineering develops new knowledge about the behavior and reliability of structures subject to extreme snow loading, advancing risk-informed snow design. In addition, the data collected to relate building closure and length of downtime, as a function of structural damage and societal factors, contributes to a wider body of scholarship linking engineering simulations to policy-making for hazards. Broader Impacts: The outcomes produced in this project facilitate decisions about risk mitigation to improve design of individual buildings and to evaluate snow design criteria. Through collaboration with the Structural Engineers Association of Colorado snow load committee, many of the advances are anticipated to be included in a new (2016) Colorado design snow load map. The study has engaged 7 students, including 4 women, in scholarly research. These students have gone on to successful careers or further study.
