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While resilience metrics have been proposed and studied given a functionality recovery curve, they have not emphasized enough on accounting for the uncertainties in multihazard occurrences and intensities. Moreover, these resilience metrics are not risk-based (i.e., they do not express the system’s resilience loss as a frequency of exceedance), leading to inconsistencies in system performance description when compared to performance-based engineering frameworks. A risk-based tool termed the dysfunctionality hazard curve is proposed to assess the resilience of systems subjected to single hazards or multihazards with interevent dependencies. The dysfunctionality hazard curve expresses system resilience performance as frequency of exceedance of time to full functionality. In doing so, it characterizes system recovery as a sequence of repair activities and also considers the uncertainties in the multihazard occurrences and intensities. The dysfunctionality hazard curve is demonstrated for a residential building susceptible to earthquake and hurricane hazards. Results indicate that the dysfunctionality hazard curve for earthquakes is greater than that for hurricane winds under single hazards. Under multihazards, considering interevent dependencies during system recovery rather than ignoring them leads to a larger dysfunctionality hazard curve. Finally, the concept of the dysfunctionality hazard curve is also extended to a system-of-systems consisting of residential and commercial buildings.
Somayajulu L. N. Dhulipala. Dysfunctionality Hazard Curve: Risk-Based Tool to Support the Resilient Design of Systems Subjected to Multihazards. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 2021, 7, 04021014 .
AMA StyleSomayajulu L. N. Dhulipala. Dysfunctionality Hazard Curve: Risk-Based Tool to Support the Resilient Design of Systems Subjected to Multihazards. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2021; 7 (2):04021014.
Chicago/Turabian StyleSomayajulu L. N. Dhulipala. 2021. "Dysfunctionality Hazard Curve: Risk-Based Tool to Support the Resilient Design of Systems Subjected to Multihazards." ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 7, no. 2: 04021014.
Seismic fragility functions can be evaluated using the cloud analysis method with linear regression which makes three fundamental assumptions about the relation between structural response and seismic intensity: log-linear median relationship, constant standard deviation, and Gaussian distributed errors. While cloud analysis with linear regression is a popular method, the degree to which these individual and compounded assumptions affect the fragility and the risk of mid-rise buildings needs to be systematically studied. This paper conducts such a study considering three building archetypes that make up a bulk of the building stock: RC moment frame, steel moment frame, and wood shear wall. Gaussian kernel methods are employed to capture the data-driven variations in the median structural response and standard deviation and the distributions of residuals with the intensity level. With reference to the Gaussian kernels approach, it is found that while the linear regression assumptions may not affect the fragility functions of lower damage states, this conclusion does not hold for the higher damage states (such as the Complete state). In addition, the effects of linear regression assumptions on the seismic risk are evaluated. For predicting the demand hazard, it is found that the linear regression assumptions can impact the computed risk for larger structural response values. However, for predicting the loss hazard with downtime as the decision variable, linear regression can be considered adequate for all practical purposes.
Somayajulu Dhulipala. Gaussian Kernel Methods for Seismic Fragility and Risk Assessment of Mid-Rise Buildings. Sustainability 2021, 13, 2973 .
AMA StyleSomayajulu Dhulipala. Gaussian Kernel Methods for Seismic Fragility and Risk Assessment of Mid-Rise Buildings. Sustainability. 2021; 13 (5):2973.
Chicago/Turabian StyleSomayajulu Dhulipala. 2021. "Gaussian Kernel Methods for Seismic Fragility and Risk Assessment of Mid-Rise Buildings." Sustainability 13, no. 5: 2973.
Somayajulu L. N. Dhulipala; Chandrakanth Bolisetti; Richard Yorg; Philip Hashimoto; Justin L. Coleman; Mark Cox. Seismic Risk Assessment of Safety-Critical Nuclear Facilities for the Purpose of Risk-Informed Periodic Reevaluation. Nuclear Technology 2020, 1 -13.
AMA StyleSomayajulu L. N. Dhulipala, Chandrakanth Bolisetti, Richard Yorg, Philip Hashimoto, Justin L. Coleman, Mark Cox. Seismic Risk Assessment of Safety-Critical Nuclear Facilities for the Purpose of Risk-Informed Periodic Reevaluation. Nuclear Technology. 2020; ():1-13.
Chicago/Turabian StyleSomayajulu L. N. Dhulipala; Chandrakanth Bolisetti; Richard Yorg; Philip Hashimoto; Justin L. Coleman; Mark Cox. 2020. "Seismic Risk Assessment of Safety-Critical Nuclear Facilities for the Purpose of Risk-Informed Periodic Reevaluation." Nuclear Technology , no. : 1-13.
This report summarizes a joint effort between Argonne National Laboratory, Idaho National Laboratory, and Los Alamos National Laboratory to develop and deploy constitutive models targeted at predicting the life of Grade 91 alloy components subjected to high temperature environments typical of those that structural components in advanced nuclear reactors would experience. Two distinct, but complementary constitutive modeling approaches have been taken here. The first em- ploys a phenomenological viscoplastic model for which parameters have been calibrated based on exper- imental data for a wide range of Grade 91 alloy that has undergone a variety of processing. A Bayesian approach was used to derive distributions of uncertain parameters for this model based on this data set. The second approach is a reduced order model suitable for engineering-scale analysis that is based on the results of a large set of mesoscale simulations. Mesoscale models allow for the microstructure and composition of a particular alloy to be directly taken into account in the computation of the viscoplastic response, but are computationally expensive, which makes it impractical to directly call those models for the material constitutive response in an engineering-scale simulation. The reduced-order representation of the response of the underlying model used here allowsmore » for an engineering-scale model to take into account the characteristics of the underlying microstructure, while only incurring a reasonable computational expense. Both of these approaches have different strengths, and are applicable for different parts of the design/anal- ysis process. The phenomenological models can be readily parameterized based on a set of experimental data for a given class of materials and used for scoping calculations. Once a specific material is chosen and adequately characterized, the reduced order models can accurately predict the response of that specific alloy, and because the models are based on predictive models of the underlying microstructure, they can be used to more confidently predict the response under conditions in regions where there is limited experimental data. Both of these models have been integrated in the Grizzly code, which is used here to perform proof-of- concept uncertainty quantification analyses of a simple component under prototypical conditions. The built- in stochastic analysis capabilities in the MOOSE framework that Grizzly is built on are used here to run large sets of simulations for this uncertainty quantification analysis. As would be expected, because the reduced order models are developed for a much more tightly defined alloy, they predict tighter distributions of the time to failure than the phenomenological models, which are calibrated to a broader set of data. Also important is that these simulations demonstrate that a reduced order modeling approach can be successfully deployed to propagate uncertainties from the material scale to practical engineering-scale component simulations.« less
Lynn Brendon Munday; Som Dhulipala; Albert Casagranda; Stephanie Pitts; Benjamin Spencer; Aaron Tallman; M. Arul Kumar; Christopher Matthews; Mark Messner; Aritra Chakraborty. Multiscale-Informed Modeling of High Temperature Component Response with Uncertainty Quantification. Multiscale-Informed Modeling of High Temperature Component Response with Uncertainty Quantification 2020, 1 .
AMA StyleLynn Brendon Munday, Som Dhulipala, Albert Casagranda, Stephanie Pitts, Benjamin Spencer, Aaron Tallman, M. Arul Kumar, Christopher Matthews, Mark Messner, Aritra Chakraborty. Multiscale-Informed Modeling of High Temperature Component Response with Uncertainty Quantification. Multiscale-Informed Modeling of High Temperature Component Response with Uncertainty Quantification. 2020; ():1.
Chicago/Turabian StyleLynn Brendon Munday; Som Dhulipala; Albert Casagranda; Stephanie Pitts; Benjamin Spencer; Aaron Tallman; M. Arul Kumar; Christopher Matthews; Mark Messner; Aritra Chakraborty. 2020. "Multiscale-Informed Modeling of High Temperature Component Response with Uncertainty Quantification." Multiscale-Informed Modeling of High Temperature Component Response with Uncertainty Quantification , no. : 1.
Multivariate Bayesian inference can bring significant benefits to seismic hazard analysis: its multivariate feature enables computing scalar and vector hazard without making any approximations; Correlations between intensity measures are implicitly modeled, permitting direct simulation of ground motion selection tools such as the conditional mean spectrum and the generalized conditioning intensity measure. Its updating feature enables a seamless integration of new ground motion data into the hazard results. In this paper, we first develop a multivariate Bayesian ground motion model through the NGA-West2 database. The model functional form considers fault type, magnitude and distance dependencies, and also the linear and the rock intensity-dependent site response. We use a hybrid Markov chain Monte Carlo sampling to perform Bayesian inference consisting of Gibbs step and a multilevel Metropolis–Hastings step. We then perform several checks on the model to ensure that it is unbiased. Finally, we illustrate the merits of this multivariate Bayesian analysis through practical and contemporary examples, which include: ground motion model updating with ground motion data recorded in the last four years and not part of the NGA-West2 database; computation of scalar and vector seismic hazard using the un-updated and updated ground motion models for Los Angeles, CA; and simulation of the conditional mean spectrum under scalar and vector IM conditioning while accounting for different sources of aleatoric and epistemic uncertainties.
Somayajulu L. N. Dhulipala; Madeleine M. Flint. Capabilities of multivariate Bayesian inference toward seismic hazard assessment. Natural Hazards 2020, 103, 1 -22.
AMA StyleSomayajulu L. N. Dhulipala, Madeleine M. Flint. Capabilities of multivariate Bayesian inference toward seismic hazard assessment. Natural Hazards. 2020; 103 (3):1-22.
Chicago/Turabian StyleSomayajulu L. N. Dhulipala; Madeleine M. Flint. 2020. "Capabilities of multivariate Bayesian inference toward seismic hazard assessment." Natural Hazards 103, no. 3: 1-22.
Nonlinear site response modeling is a crucial aspect of Probabilistic Seismic Hazard Analysis. Site amplification models routinely rely on a rock intensity measure to characterize the strength of the bedrock motion. However, the adequacy of such intensity measures towards predicting amplifications across the oscillator period range has not been investigated in the literature. This paper analyzes the adequacy of rock intensity measures using state of the art criteria established in Performance-Based Earthquake Engineering and techniques from Information Theory. The efficiency and the sufficiency of several rock intensity measure are assessed. It was found that spectral accelerations at bedrock at short periods usually are adequate for predicting amplifications across the period range. This supports the current practice of using Peak Ground Acceleration in Ground Motion Models. However, for extremely soft sites, which demonstrate nonlinear effects well into the long period range, it is better practice to ensure that amplification factors and spectral acceleration share the same oscillator period. Finally, for predicting the peak shear strain (an important parameter that controls nonlinearity of site response), Peak Ground Velocity is generally adequate, and this conclusion is in line with the commonly used definitions of proxy shear strains.
Somayajulu L.N. Dhulipala; Adrian Rodriguez-Marek; Mahdi Bahrampouri. Intensity measure adequacy assessment for nonlinear site response using Information Theory. Soil Dynamics and Earthquake Engineering 2020, 134, 106144 .
AMA StyleSomayajulu L.N. Dhulipala, Adrian Rodriguez-Marek, Mahdi Bahrampouri. Intensity measure adequacy assessment for nonlinear site response using Information Theory. Soil Dynamics and Earthquake Engineering. 2020; 134 ():106144.
Chicago/Turabian StyleSomayajulu L.N. Dhulipala; Adrian Rodriguez-Marek; Mahdi Bahrampouri. 2020. "Intensity measure adequacy assessment for nonlinear site response using Information Theory." Soil Dynamics and Earthquake Engineering 134, no. : 106144.
Civil infrastructure systems are subjected to multiple hazards, including natural and anthropogenic, that disrupt their function or the level of service offered. Estimating the function recovery of these systems (or how soon normalcy of operations will be restored) when subjected to repeated hazard events by considering the inter-event dependencies is an important problem in multihazard infrastructure resilience. However, this problem has been less addressed in the field. This paper proposes a series of semi-Markov processes model to capture the inter-event dependencies in infrastructure recovery when subjected to successive hazard events. Recovery after each new hazard event is represented by a unique semi-Markov process that models the reduced recovery rates and the increased recovery times caused by the system’s incomplete recovery from the preceding event. Two novel formulations of the inter-event dependency modeling, namely Maximal Effects Dependency (considers the worst impact of two successive hazard events) and Cumulative Effects Dependency (considers the aggregated impacts of two successive hazard events), are proposed and discussed. The model is demonstrated by considering the following applications: Three-state system subjected to deterministic and random occurrences of identical hazard events; and Multihazard resilience of a building in Charleston, SC, considering earthquake and hurricane hazards. Results indicate that considering inter-event dependencies in recovery modeling can lead to lesser-predicted resilience, thereby affecting resilience-based decision-making.
Somayajulu L.N. Dhulipala; Madeleine M. Flint. Series of semi-Markov processes to model infrastructure resilience under multihazards. Reliability Engineering & System Safety 2019, 193, 106659 .
AMA StyleSomayajulu L.N. Dhulipala, Madeleine M. Flint. Series of semi-Markov processes to model infrastructure resilience under multihazards. Reliability Engineering & System Safety. 2019; 193 ():106659.
Chicago/Turabian StyleSomayajulu L.N. Dhulipala; Madeleine M. Flint. 2019. "Series of semi-Markov processes to model infrastructure resilience under multihazards." Reliability Engineering & System Safety 193, no. : 106659.
Deaggregation is one of the products of probabilistic seismic hazard analysis (PSHA) suitable for identifying the relative contributions of various magnitude-distance bins to a hazard or intensity measure (IM) level. In this paper, we elucidate some interesting features of deaggregations, such as: their monotonically decreasing nature with IM; their invariance to any minimum IM level; and the pertinence of their bins to a complementary cumulative distribution function (CCDF). We use these features of hazard deaggregation along with copula functions in a simplified method for computing vector deaggregation and vector hazard given the scalar counterparts. We validate our simplified procedure at a hypothetical site surrounded by multiple fault sources where seismic hazard is calculated using a logic tree. We also demonstrate the application of our approach to a real site in Los Angeles, CA. Finally, we explore whether the invariance property of deaggregations can be used to compute scalar hazard curves using new ground motion prediction models/IMs, and find that for low to moderate levels of IM, a reasonable approximation is obtained.
Somayajulu L. N. Dhulipala; Adrian Rodriguez-Marek; Madeleine M. Flint. Computation of Vector Hazard Using Salient Features of Seismic Hazard Deaggregation. Earthquake Spectra 2018, 34, 1893 -1912.
AMA StyleSomayajulu L. N. Dhulipala, Adrian Rodriguez-Marek, Madeleine M. Flint. Computation of Vector Hazard Using Salient Features of Seismic Hazard Deaggregation. Earthquake Spectra. 2018; 34 (4):1893-1912.
Chicago/Turabian StyleSomayajulu L. N. Dhulipala; Adrian Rodriguez-Marek; Madeleine M. Flint. 2018. "Computation of Vector Hazard Using Salient Features of Seismic Hazard Deaggregation." Earthquake Spectra 34, no. 4: 1893-1912.
Certain applications in seismic demand analysis require the use of a vector of intensity measures (IMs). In these cases, a vector seismic hazard analysis or deaggregation for vector variables becomes necessary. One such example is ground motion selection procedures that must be conditioned on multiple IMs. In this paper we review some key features of seismic hazard deaggregation identified in Dhulipala et al. (2017): (1) deaggregation plots decrease monotonically with IM level, (2) they are invariant to the choice of IM for a low IM level, and (3) their bins correspond to a complementary cumulative distribution function. Using these characteristics along with Copula functions, the vector hazard and the corresponding deaggregation matrices can be readily derived while also respecting the logic-tree approach underlying the hazard curve. The proposed approach is applied to a site in Los Angeles, CA, considering three IMs: PGV, PGA, and spectral acceleration for a period of 1 s. In addition, we compare Gaussian- and t-Copulas in predicting the vector seismic hazard and we find that a t-Copula predicts slightly higher hazards in general. We attribute this discrepancy to the “heavy-tailedness” of a t-distribution.
Somayajulu L. N. Dhulipala; Adrian Rodriguez-Marek; Madeleine M. Flint. Salient Features of Seismic Hazard Deaggregation and Computation of Vector Hazard. Geotechnical Earthquake Engineering and Soil Dynamics V 2018, 1 .
AMA StyleSomayajulu L. N. Dhulipala, Adrian Rodriguez-Marek, Madeleine M. Flint. Salient Features of Seismic Hazard Deaggregation and Computation of Vector Hazard. Geotechnical Earthquake Engineering and Soil Dynamics V. 2018; ():1.
Chicago/Turabian StyleSomayajulu L. N. Dhulipala; Adrian Rodriguez-Marek; Madeleine M. Flint. 2018. "Salient Features of Seismic Hazard Deaggregation and Computation of Vector Hazard." Geotechnical Earthquake Engineering and Soil Dynamics V , no. : 1.
In probabilistic seismic demand analysis, evaluation of the sufficiency of an intensity measure (IM) is an important criterion to avoid biased assessment of the demand hazard. However, there exists no metric to quantify the degree of sufficiency as per the criterion of Luco and Cornell (2007). This paper proposes a site‐specific unified measure for degree of sufficiency from all seismological parameters under consideration using a total information gain metric. This unified metric for sufficiency supports not only comparison of the performance of different IMs given a response quantity but also assessment of the performance of a particular IM across different response quantities. The proposed sufficiency metric was evaluated for a 4‐story steel moment frame building, and the influence of ground motion selection on the degree of sufficiency was investigated. It was observed that ground motion selection can have a significant impact on IM sufficiency. Because computing the total information gain requires continuous deaggregation across the IM space, an approximate deaggregation technique that allows for a more practical estimation of marginal deaggregation probabilities is proposed. It is expected that the total information gain metric proposed in this paper will aid in understanding the efficiency‐sufficiency relation, thus enabling the selection of a proper scalar IM for a given site and application in probabilistic seismic demand analysis.
Somayajulu L. N. Dhulipala; Adrian Rodriguez-Marek; Shyam Ranganathan; Madeleine Flint. A site-consistent method to quantify sufficiency of alternative IMs in relation to PSDA. Earthquake Engineering & Structural Dynamics 2017, 47, 377 -396.
AMA StyleSomayajulu L. N. Dhulipala, Adrian Rodriguez-Marek, Shyam Ranganathan, Madeleine Flint. A site-consistent method to quantify sufficiency of alternative IMs in relation to PSDA. Earthquake Engineering & Structural Dynamics. 2017; 47 (2):377-396.
Chicago/Turabian StyleSomayajulu L. N. Dhulipala; Adrian Rodriguez-Marek; Shyam Ranganathan; Madeleine Flint. 2017. "A site-consistent method to quantify sufficiency of alternative IMs in relation to PSDA." Earthquake Engineering & Structural Dynamics 47, no. 2: 377-396.