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Micromechanics‐based continuum damage criteria have previously been developed to simulate the initiation of ductile fracture in structural steels under conditions with large‐scale plasticity where conventional fracture mechanics indices are invalid. Such models have been combined with methods to simulate ductile crack growth for monotonic loading. In this study, a micromechanics‐based adaptive cohesive zone model for simulating ductile crack propagation under monotonic loading is extended to handle cyclic loading. The proposed model adaptively modifies the cohesive traction–separation relationship for crack opening and closure, as the loading reverses between tension and compression. The approach is implemented into the finite element analysis platform WARP3D, and results of simulations that use the model are compared with data from coupon‐scale tests. The results demonstrate that the proposed model can accurately simulate the effect of crack propagation on specimen response, as well as other key aspects of observed behavior, including crack face closure and crack tunneling.

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.

Hospital systems play a critical role in treating injuries during disaster emergency responses. Simultaneously, natural disasters hinder their ability to operate at full capacity. Thus, cities must develop strategies that enable hospitals’ effective disaster operations. Here, we present a methodology to evaluate emergency response based on a model that assesses the loss of hospital functions and quantifies multiseverity injuries as a result of earthquake damage. The proposed methodology can design effective plans for patient transfers and allocation of ambulances and mobile operating rooms. This methodology is applied to Lima, Peru, subjected to a disaster scenario following a magnitude 8.0 earthquake. Our results show that the spatial distribution of healthcare demands mismatches the post-earthquake capacities of hospitals, leaving large zones on the periphery significantly underserved. This study demonstrates how plans that leverage hospital-system coordination can address this demand-capacity mismatch, reducing waiting times of critically injured patients by factors larger than two.

A methodology is introduced to assess the post‐earthquake structural safety of damaged buildings using a quantitative relationship between observable structural component damage and the change in collapse vulnerability. The proposed framework integrates component‐level damage simulation, virtual inspection, and structural collapse performance assessment. Engineering demand parameters from nonlinear response history analyses are used in conjunction with component‐level damage simulation to generate multiple realizations of damage to key structural elements. Triggering damage state ratios, which describe the fraction of components within a damage state that results in an unsafe placard assignment, are explicitly linked to the increased collapse vulnerability of the damaged building. A case study is presented in which the framework is applied to a 4‐story reinforced concrete frame building with masonry infills. The results show that when subjected to maximum considered earthquake level ground motions, the probability of experiencing enough structural damage to trigger an unsafe placard, leading to building closure, is more than 2 orders of magnitude higher than the risk of collapse.