Showing papers by "Gregory G. Deierlein published in 2023"
TL;DR: The Natural Hazards Engineering Research Infrastructure Computational Modeling and Simulation Center gathered 60 researchers, developers, and practitioners working in natural hazards engineering (NHE) for a workshop to prioritize research questions and identify community needs for data and computational simulation capabilities as discussed by the authors .
Abstract: With the aim of fostering the development of robust tools to simulate the impact of natural hazards on structures, lifelines, and communities, the Natural Hazards Engineering Research Infrastructure Computational Modeling and Simulation Center gathered 60 researchers, developers, and practitioners working in natural hazards engineering (NHE) for a workshop to prioritize research questions and identify community needs for data and computational simulation capabilities. Participants used their wide-ranging expertise in earthquake, coastal, and wind hazards from engineering, planning, data sciences, and social sciences perspectives to identify five major thrusts of recommended future work, including detailed suggestions for each: (1) development of housing and household recovery models; (2) integration of existing models into flexible computational workflows; (3) investment in the collection of high-value open data; (4) commitment to sharing and utilizing high-value data; and (5) development of versatile, multidisciplinary testbed studies. Participant responses and workshop data were analyzed with the help of an ontology that the authors designed to support data classification in a broad range of NHE applications. The paper also includes observations and suggestions for planning and conducting interactive workshops of this type.
4 citations
TL;DR: In this article , an overview of the cripple wall experimental program is provided, including the variables investigated, and a discussion of how the damage observations were used to develop probabilistic component-level damage fragility functions for cripple-wall dwellings is provided.
Abstract: Assessment of damage to homes following a significant earthquake has found that the primary reason for major structural damage and failure is due to inadequate lateral bracing of cripple walls and inadequate sill bolting of the cripple wall to the foundation. Since the 1970s, methods to retrofit weak cripple walls and improve sill anchorage have been developed and implemented on many dwellings to improve their seismic performance, most notably available are the Federal Emergency Management Agency (FEMA) P-1100 guidelines. To assess the seismic vulnerability of existing and retrofitted cripple walls, experimental programs were performed and documentation of the evolution of damage during these experiments were produced and correlated to the structural capacity and residual drift of the specimens. Using the documented damage evolution, existing damage fragility functions were revised for wood light-frame sub-assemblies. In this article, an overview of the cripple wall experimental program is provided, including the variables investigated. Documentation of the damage evolution during these experiments is presented. Damage states based on repair triggers associated with imposed displacements of the experiments are defined. Finally, a discussion of how the damage observations were used to develop probabilistic component-level damage fragility functions for cripple-wall dwellings is provided.
TL;DR: In this article , a probabilistic learning on manifolds (PLoM) method is presented to estimate structural seismic response. But, the method is not suitable for large numbers of analyses to evaluate multiple models of alternative design realizations with a site-specific set of ground motions.
Abstract: Nonlinear response history analysis (NLRHA) is generally considered to be a reliable and robust method to assess the seismic performance of buildings under strong ground motions. While NLRHA is fairly straightforward to evaluate individual structures for a select set of ground motions at a specific building site, it becomes less practical for performing large numbers of analyses to evaluate either (1) multiple models of alternative design realizations with a site‐specific set of ground motions, or (2) individual archetype building models at multiple sites with multiple sets of ground motions. In this regard, surrogate models offer an alternative to running repeated NLRHAs for variable design realizations or ground motions. In this paper, a recently developed surrogate modeling technique, called probabilistic learning on manifolds (PLoM), is presented to estimate structural seismic response. Essentially, the PLoM method provides an efficient stochastic model to develop mappings between random variables, which can then be used to efficiently estimate the structural responses for systems with variations in design/modeling parameters or ground motion characteristics. The PLoM algorithm is introduced and then used in two case studies of 12‐story buildings for estimating probability distributions of structural responses. The first example focuses on the mapping between variable design parameters of a multidegree‐of‐freedom analysis model and its peak story drift and acceleration responses. The second example applies the PLoM technique to estimate structural responses for variations in site‐specific ground motion characteristics. In both examples, training data sets are generated for orthogonal input parameter grids, and test data sets are developed for input parameters with prescribed statistical distributions. Validation studies are performed to examine the accuracy and efficiency of the PLoM models. Overall, both examples show good agreement between the PLoM model estimates and verification data sets. Moreover, in contrast to other common surrogate modeling techniques, the PLoM model is able to preserve correlation structure between peak responses. Parametric studies are conducted to understand the influence of different PLoM tuning parameters on its prediction accuracy.
TL;DR: In this article , the authors examined composite moment connections for seismic applications and proposed an update of the Guidelines for Design of Joints between Steel Beams and Reinforced Concrete Columns, published by the ASCE in 1994.
Abstract: The research on composite moment connections for seismic applications is examined to propose an update of the Guidelines for Design of Joints Between Steel Beams and Reinforced Concrete Columns, published by the ASCE in 1994. These guidelines were developed based on limited experimental data and restrict composite moment frames to zones of low to moderate seismicity. Recent experimental data, however, support their use in high seismic regions. A database of full‐scale connection tests is included, accompanied by a comparison between strength measured experimentally vs. the calculated joint strength. Results from this comparison show good agreement.
TL;DR: In this article , a simulation-based methodology is presented to identify relevant damage indicators and safety thresholds for building structures, which can increase the accuracy and confidence of post-earthquake evaluations.
Abstract: After a strong earthquake, criteria are needed to determine whether buildings are safe to reoccupy based on observable damage. This paper presents a simulation-based methodology to identify relevant damage indicators and safety thresholds for building structures. Prior knowledge of the most relevant damage indicators and their thresholds can increase the accuracy and confidence of post-earthquake evaluations. Current practice to translate observable damage into a tagging decision relies on qualitative guidelines based on past earthquake experience and judgment, which may be susceptible to speculation and interpretation. In addition, past experience may not be relevant to newer structural systems and to large or complex (e.g., high-rise) buildings. To augment past observations and data from structural component tests, nonlinear dynamic analyses can be used to estimate the collapse safety of structures with simulated damage. Technologies to execute these simulations have matured over the years, although to date they have not been systematically applied to evaluate the destabilizing effects of simulated damage on collapse safety. In this paper, a methodology is presented to use numerical simulations of damage to identify and evaluate relevant damage indicators that can be quantitatively related to safety thresholds. Damage indicators are selected based on their reliability in estimating the structural safety and their sensitivity to modeling uncertainty, that is, where the preferred indicators are insensitive to variability in the structural materials and model parameters. The safety threshold for each damage indicator is selected to maximize accuracy in post-earthquake building assessments. The methodology is demonstrated through an application study of ductile reinforced concrete frame buildings. Results show that aggregated indices of structural component damage (e.g., aggregated over the floor of a building) outperform other damage indicators based on peak or residual drifts or simpler percentages of damaged components. Subject to agreement of a number underlying assumptions, this methodology can be applied to a wider variety of structures to improve post-earthquake evaluation guidelines.