Advancing the Design of Resilient and Sustainable Buildings: An Integrated Life-Cycle Analysis

Angeles, Karen; Patsialis, Dimitrios; Taflanidis, Alexandros A.; Kijewski-Correa, Tracy; Buccellato, Aimee; Vardeman, Charles

doi:10.1061/(asce)st.1943-541x.0002910

Cited by 23 publications

(13 citation statements)

References 48 publications

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“…Evidently these statistics are useful if a lognormal assumption is evoked for the EDP distribution, since in that case they define the parametric form of the distribution, though they still have a utility even if such an assumption is not made, since they help quantify the median seismic performance and life-cycle cost/sustainability metrics. 3,46 Thus, the conditional logarithmic EDP mean, 𝑚(𝛉) = 𝐸[ln(𝑧)|𝛉], and variance 𝑠 2 (𝛉) = Var[ln(𝑧)|𝛉], where E[.] denotes the expectation operator and Var [.…”

Section: Conditional Distribution and Statistics For Validationmentioning

confidence: 99%

“…18 even when the heteroscedasticity was completely ignored for the aleatoric variance. Since the quantification of the EDP dispersion is important for many PBEE and probabilistic risk assessment frameworks, 3,46 it is evident that variant V 2 will not be able to offer adequate accuracy for many practical applications in these domains.…”

Section: Metamodel Input Corresponding To the Seismic Hazard Parametersmentioning

confidence: 99%

“…To accommodate a consistent comparison across the three metamodel variants, the validation will be established with respect to the conditional EDP distribution (or statistics) for a specific seismic scenario and structural configuration, as this distribution can be estimated across all of them, and moreover this is a distribution that is frequently explicitly encountered in PBEE applications 3,46 . The conditional distribution is obtained through the propagation of all sources of uncertainty related to the ground motion model (

{\bf \theta}

m and

{\bf \theta}

w ), and is actually the only distribution related to the seismic hazard that can be quantified through variant V 2 , since this variant implementation treats all the ground motion model uncertainties as part of the aleatoric uncertainty description, and therefore it is not able to provide predictions for any desired value of those parameters (in order to be able to accommodate an alternative validation with respect to such values).…”

Section: Implementation For Approximating the Edp Distributionmentioning

confidence: 99%

“…Beyond the EDP distribution, the first and second order EDP statistics could be of interest. Evidently these statistics are useful if a lognormal assumption is evoked for the EDP distribution, since in that case they define the parametric form of the distribution, though they still have a utility even if such an assumption is not made, since they help quantify the median seismic performance and life‐cycle cost/sustainability metrics 3,46 . Thus, the conditional logarithmic EDP mean,

m({\bf \theta})=E[\ln (z)|{\bf \theta}]

, and variance

{s}^{2}({\bf \theta})=\textit{Var}[\ln (z)|{\bf \theta}]

, where E [.]…”

Section: Implementation For Approximating the Edp Distributionmentioning

confidence: 99%

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Addressing the different sources of excitation variability in seismic response distribution estimation using kriging metamodeling

Kyprioti

Taflanidis

2022

Earthq Engng Struct Dyn

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This paper investigates the impact of the different sources of excitation variability within the stochastic kriging framework recently developed by the authors for the estimation of the distribution of engineering demand parameters (EDPs) in applications that the seismic hazard is described through stochastic ground motion models. For a given seismic event, described by seismicity characteristics such as the moment magnitude and the distance to source, one can distinguish two type of uncertainties in the excitation description: (g.i) the stochastic sequence utilized within the excitation model; (g.ii) the predictive models relating the ground motion parameters, such as significant duration, arias intensity, or frequency properties of seismic waves, to the seismicity characteristics. The original formulation of the authors treats (g.i) as aleatoric uncertainty and (g.ii) as parametric uncertainty, including the latter in the model parameters for the risk characterization, representing the metamodel input. Implementation establishes the EDP distribution approximation by utilizing a database of EDP estimates for a set of different input parameters, considering replications of these estimates for different stochastic sequences for some small part of this database. The database with replications is first leveraged to approximate the heteroscedas-2466

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Section: Conditional Distribution and Statistics For Validationmentioning

confidence: 99%

Section: Metamodel Input Corresponding To the Seismic Hazard Parametersmentioning

confidence: 99%

{\bf \theta}

m and

{\bf \theta}

Section: Implementation For Approximating the Edp Distributionmentioning

confidence: 99%

m({\bf \theta})=E[\ln (z)|{\bf \theta}]

, and variance

{s}^{2}({\bf \theta})=\textit{Var}[\ln (z)|{\bf \theta}]

, where E [.]…”

Section: Implementation For Approximating the Edp Distributionmentioning

confidence: 99%

See 2 more Smart Citations

Addressing the different sources of excitation variability in seismic response distribution estimation using kriging metamodeling

Kyprioti

Taflanidis

2022

Earthq Engng Struct Dyn

Self Cite

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“…However, there has been an exponential increase in research on the construction sector oriented toward a sustainable approach and the search for a circular economy in the last decade. Lately, several studies have been carried out to evaluate sustainability in construction projects, from bridges [4,5], buildings [6,7], construction elements such as pavements [8], or retaining walls [9,10], among many other aspects of construction design and management.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive ANP & ELECTRE IS-Based MCDM Model Using Quantitative Variables

et al. 2022

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The analytic network process (ANP) is a discrete multi-criteria decision-making (MCDM) method conceived as a generalization of the traditional analytic hierarchical process (AHP) to address its limitations. ANP allows the incorporation of interdependence and feedback relationships between the criteria and alternatives that make up the system. This implies much more complexity and intervention time, which reduces the expert’s ability to make accurate and consistent judgments. The present paper takes advantage of the usefulness of this methodology by formulating the model for exclusively quantitative variables, simplifying the decision problem by resulting in fewer paired comparisons. Seven sustainability-related criteria are used to determine, among four design alternatives for a building structure, which is the most sustainable over its life cycle. The results reveal that the number of questions required by the conventional AHP is reduced by 92%. The weights obtained between the AHP and ANP groups show significant variations of up to 71% in the relative standard deviation of some criteria. This sensitivity to subjectivity has been implemented by combining the ANP-ELECTRE IS methods, allowing the expert to reflect the view of the decision problem with greater flexibility and accuracy. The sensitivity of the results on different methods has been analyzed.

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Stochastic emulation with enhanced partial‐ and no‐replication strategies for seismic response distribution estimation

Yi,

Taflanidis

2024

Earthq Engng Struct Dyn

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Modern performance earthquake engineering practices frequently require a large number of time‐consuming non‐linear time‐history simulations to appropriately address excitation and structural uncertainties when estimating engineering demand parameter (EDP) distributions. Surrogate modeling techniques have emerged as an attractive tool for alleviating such high computational burden in similar engineering problems. A key challenge for the application of surrogate models in earthquake engineering context relates to the aleatoric variability associated with the seismic hazard. This variability is typically expressed as high‐dimensional or non‐parametric uncertainty, and so cannot be easily incorporated within standard surrogate modeling frameworks. Rather, a surrogate modeling approach that can directly approximate the full distribution of the response output is warranted for this application. This approach needs to additionally address the fact that the response variability may change as input parameter changes, yielding a heteroscedastic behavior. Stochastic emulation techniques have emerged as a viable solution to accurately capture aleatoric uncertainties in similar contexts, and recent work by the second author has established a framework to accommodate this for earthquake engineering applications, using Gaussian Process (GP) regression to predict the EDP response distribution. The established formulation requires for a portion of the training samples the replication of simulations for different descriptions of the aleatoric uncertainty. In particular, the replicated samples are used to build a secondary GP model to predict the heteroscedastic characteristics, and these predictions are then used to formulate the primary GP that produces the full EDP distribution. This practice, however, has two downsides: it always requires minimum replications when training the secondary GP, and the information from the non‐replicated samples is utilized only for the primary GP. This research adopts an alternative stochastic GP formulation that can address both limitations. To this end, the secondary GP is trained by measuring the square of sample deviations from the mean instead of the crude sample variances. To establish the primitive mean estimates, another auxiliary GP is introduced. This way, information from all replicated and non‐replicated samples is fully leveraged for estimating both the EDP distribution and the underlying heteroscedastic behavior, while formulation accommodates an implementation using no replications. The case study examples using three different stochastic ground motion models demonstrate that the proposed approach can address both aforementioned challenges.

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Advancing the Design of Resilient and Sustainable Buildings: An Integrated Life-Cycle Analysis

Cited by 23 publications

References 48 publications

Addressing the different sources of excitation variability in seismic response distribution estimation using kriging metamodeling

Addressing the different sources of excitation variability in seismic response distribution estimation using kriging metamodeling

An Adaptive ANP & ELECTRE IS-Based MCDM Model Using Quantitative Variables

Stochastic emulation with enhanced partial‐ and no‐replication strategies for seismic response distribution estimation

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