Siamak Sattar

As the United States government is planning a mega investment for its aged and new infrastructure, it is essential to offer technical tools for t |

The concept of resilience has been used extensively across the sciences in engineering and the humanities. It is applied to ecology, medicine, economics, and psychology. The novel coronavirus disease (COVID-19) has posed an extraordinary challenge to the resilience of healthcare systems, communities, and nations and has profoundly altered our previous day-to-day operations. This paper presents a discussion of the definitions and characteristics of resilient systems. Scenarios are utilized to qualitatively explore key relationships, responses, and paths for recovery across different system types. The purpose is to develop an integrated approach that can accommodate simultaneous threats to system resilience, in particular, impacts from a natural hazard in conjunction with COVID-19. This manuscript is the first to advocate for more in-depth and quantitative research utilizing transdisciplinary approaches that can accommodate considerations across our built environment and healthcare system infrastructures in pursuit of designing systems that are resilient to both natural hazards and pandemic impacts.

Quantification of the impact of uncertainty in material properties as well as the input ground motion on structural responses is an important step in implementing a performance-based earthquake engineering (PBEE) framework. Among various sources of uncertainty, the variability in the input ground motions, a.k.a. record-to-record, greatly affects the assessment results. The objective of this paper is to quantify the uncertainty in structural response with hybrid uncertainty sources. In this paper, multiple matrix completion methods are proposed and applied on a case study structure. The matrix completion method is a means to estimate the analyses results for the entire set of input parameters by conducting analysis for only a small subset of analyses. The main algorithmic contributions of our proposed method are twofold. First, we develop a sampling technique for choosing a subset of representative simulations, which allows improving the accuracy of the estimated response. An unsupervised machine learning technique is used for this purpose. Next, the proposed matrix completion method for uncertainty quantification is further refined by incorporating a regression model that is trained on the available partial simulations. The regression model improves the initial sampling as it provides a rough estimation of the structural responses. Finally, the proposed algorithm is applied to a multi-degree-of-freedom system, and the structural responses (i.e., displacements and base shear) are estimated. Results show that the proposed algorithm can effectively estimate the response from a full set of nonlinear simulations by conducting analyses only on a small portion of the set.

One important aspect of performance-based earthquake engineering is developing structural models that can accurately capture key characteristics of dynamic responses up to structural collapse. This in turn requires the solution algorithm to be capable of capturing large displacement responses through appropriate use of geometric nonlinearity. Small-displacement-based solution algorithms possess this capability in the analysis with high computational efficiency and relatively good accuracy; while large-displacement-based finite element analysis algorithms address geometric nonlinearity through satisfying equilibrium in the deformed position which capture large displacement accurately but with poor computational efficiency. The objective of this research is to investigate the impact of epistemic uncertainty in small-displacement-based algorithms for capturing large displacement responses. Consistent models of steel moment frames are developed for analysis using various solution algorithms based on different geometric nonlinearity formulations. Response history analysis is performed under the same input ground motions. Analysis results show that some formulations address the geometric nonlinearity more appropriately with less uncertainty than others.