ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

A large number of gravity load–designed (GLD) reinforced concrete (RC) buildings are found in many seismic-prone countries including India. Many of them were built before the advent of seismic codes or with the utilization of old and inadequate seismic design criteria. Engineers and decision makers need to have information on the seismic vulnerability of such buildings in a given region for mitigation planning. The present study aims to evaluate the relative seismic vulnerability of a GLD building subjected to seismic hazards in a practical load and resistance factor format corresponding to various seismic zones of India. The results of this study show that the relative vulnerability of the GLD building (with respect to a building designed for seismic forces) shows a multifold increase from lower to higher seismic zone. It also indicates that the GLD buildings existing in higher seismic zones of India (IV and V) should be immediately uninhabited as an urgent mitigation measure.

Metamodeling techniques have been widely used as substitutes for high-fidelity and time-consuming models in various engineering applications. Examples include polynomial chaos expansions, neural networks, kriging, and support vector regression (SVR). This paper attempts to compare the latter two in different case studies so as to assess their relative efficiency on simulation-based analyses. Similarities are drawn between these two metamodel types, leading to the use of anisotropy for SVR. Such a feature is not commonly used in the SVR-related literature. Special care was given to a proper automatic calibration of the model hyperparameters by using an efficient global search algorithm, namely the covariance matrix adaptation–evolution scheme. Variants of these two metamodels, associated with various kernel and autocorrelation functions, were first compared on analytical functions and then on finite element–based models. From the comprehensive comparison, it was concluded that anisotropy in the two metamodels clearly improves their accuracy. In general, anisotropic $L_2$-SVR with the Matérn kernels was shown to be the most effective metamodel.

This paper presents a novel stochastic method for simulation of ground motions. The input is a user-specified earthquake scenario description, and the output consists of fully nonstationary acceleration time histories at a site of interest. A bimodal analytical evolutionary Kanai–Tajimi (K–T) model lies at the core of the predictive stochastic model. The K–T model parameters are linked through mixed-effects regression models to three commonly used ground motion physical predictors, moment magnitude $M_w$, closest distance $R_{rup}$, and average soil shear-wave velocity $V_{S_{30}}$ at the site of interest. An extensive Californian subset of the next-generation attenuation NGA-West2 database is used to develop and calibrate the regression models. The random effect terms in the developed regression models effectively describe the correlation among ground motions of the same earthquake event, while also accounting for the location dependent effects at each site. The simulation of sample ground motion realizations based on each specified earthquake scenario is facilitated by the spectral representation method (SRM). In order to evaluate the performance, assess the versatility and validate the proposed predictive model, the simulation-based attenuation of important scalar ground motion engineering metrics is studied and compared with results from well-established ground motion prediction equation (GMPE) models. The statistics of elastic response spectra of simulated time histories are also compared with the associated predictions of the NGA-West2 GMPE models based on a variety of earthquake scenarios. Nonlinear response-history analyses for representative single-degree-of-freedom and multiple-degree-of-freedom structural systems compare the seismically induced inelastic structural demand of the considered systems when subjected to sets of both recorded and corresponding simulated ground motions.

Decisions regarding infrastructure resilience are made under uncertainty and involve trade-offs among competing objectives. An effective way of understanding how uncertainties propagate and understanding trade-offs among multiple objectives is to use a computer simulation that integrates high-level representations of each infrastructure, their interdependencies, and their reactions to a variety of potential disruptions. To address this need for such a decision support tool, this paper considers a multidisciplinary federation of systems dynamics models for the purpose of simulating the response of critical and interconnected homeland infrastructures to a major disruption. With the addition of disease progression simulation, the models provide a high-level integrated analysis of pandemic influenza outbreak. By use of the models, options for mitigation and prevention such as the use of antivirals, surgical masks, and quarantine policies can be assessed. In this paper, the models are augmented with analytical methods of uncertainty analysis to determine the statistics of model outputs from the statistics of model inputs in order to determine the relative importance of the uncertainties in the model inputs and to identify the worst-case scenarios that have a given probability of occurrence. Techniques of reliability-based optimization are incorporated to find a robust optimal strategy for infrastructure resilience in which preparations are optimized for the worst-case scenario.

In this paper, the failure costs of an arch dam’s optimum shape were investigated, taking into account the interaction between dam, reservoir, foundation, and hydrodynamic effects resulting from seismic loading. Construction, lifecycle, and failure costs were considered to address the risk-based arch dam optimization problem. Both the flexibility and damping effects of the foundation rock were considered. The optimization was carried out using a hybrid algorithm on the basis of the charged system search and big bang-big crunch methods. A real-world arch dam was investigated as a numerical example to demonstrate the efficiency of the proposed method. The numerical results revealed the computational advantages of the new method and the new defined risk optimization problem for optimal design of arch dams.

Corrosion is one of the main causes of pipeline failure, which can have large social, economic, and environmental consequences. To mitigate this risk, pipeline operators perform regular inspections and repairs. The results of the inspections aid decision makers in determining the optimal maintenance strategy. However, there are many possible maintenance strategies, and a large degree of uncertainty, leading to difficult decision making. This paper develops a framework to inform the decision of whether it is better over the long term to continuously repair defects as they become critical or to just replace entire segments of the pipeline. The method uses a probabilistic analysis to determine the expected number of failures for each pipeline segment. The expected number of failures informs the optimal decision. The proposed framework is tailored toward mass amounts of in-line inspection data and multiple pipeline segments. A numerical example of a corroding upstream pipeline illustrates the method.

The major cause of earthquake damage to an embankment is the liquefaction of the soil foundation that induces ground-level deformations. The aim of this paper is to assess numerically the effect of the liquefaction-induced settlement of the soil foundation on a levee due to real earthquakes. The seismic vulnerability is evaluated in terms of analytical fragility curves constructed on the basis of nonlinear dynamic finite-element (FE) analysis. However, FE analysis can be expensive because of the very large number of simulations needed for an accurate assessment of the system failure behavior. This problem is addressed by building a Gaussian process (GP) emulator to represent the output of the expensive FE model. A comparison with the FE reference results suggests that the proposed GP model works well and can be successfully used as a predictive tool to compute the induced damage on the levee. Findings also illustrate clearly the importance and the advantages of an adequate definition of the input parameters to build the GP model.

The present paper deals with uncertainties related to modeling undrained shear strength of soft sensitive clays using finite-element effective stress–based soil models. Two effective stress soil models, the isotropic Soft Soil and the anisotropic S-CLAY1S models, are used in this study. The performance of the two models is evaluated by comparing the simulated undrained shear strength, resulting from undrained stress paths, to an existing multivariate database of undrained shear strength data points from 24 test sites from Finland. Finite-element model parameters are derived from existing correlations based on basic clay properties. Furthermore, the modeled data points are compared with the undrained shear strength predicted by some existing correlations for Scandinavian clays. Bias and uncertainties of the simulated data points associated with the multivariate database and the correlations are calculated. The results presented appear to be satisfactory, considering that the finite-element input parameters are based on soil plasticity and consolidation stresses.

A key project management capability is time management, prioritizing activities in time while tracking emergent and future conditions along with the influences of those conditions on project metrics. The research objective of this paper is to introduce a method of updating of importance for project activities as a class of entity. The methodology is based on identification of scenarios with two modes of disruption: shifting preferences related project management objectives, and disruptions of project networks and constituent activities. The findings are that time management and control can be guided by this approach to recognize and address the most and least disruptive scenarios as combinations of emergent and future conditions. The management implications point to allocating managerial resources where most needed. The results are demonstrated in a time-critical example of posthurricane disaster response.

This paper is focused on the development of an efficient system-level reliability-based design optimization strategy for uncertain wind-excited building systems characterized by high-dimensional design variable vectors (in the order of hundreds). Indeed, although a number of methods have been proposed over the last 15 years for the system-level reliability-based design optimization of building systems subject to stochastic excitation, few have treated problems characterized by more than a handful of design variables. This limits their applicability to practical problems of interest, such as the design optimization of high-rise buildings. To overcome this limitation, a simulation-based method is proposed in this work that is capable of solving reliability-based design optimization problems characterized by high-dimensional design variable vectors while considering system-level performance constraints. The framework is based on approximately decoupling the reliability analysis from the optimization loop through the definition of a system-level subproblem that can be fully defined from the results of a single simulation carried out in the current design point. To demonstrate the efficiency, practicality, and strong convergence properties of the proposed framework, a 40-story uncertain planar frame defined by 200 design variables is optimized under stochastic wind excitation.

Stochastic soil modeling aims to provide reasonable mean, variance, and spatial correlation of soil properties with quantified uncertainty. Because of difficulties in integrating limited and imperfect prior knowledge (i.e., epistemic uncertainty) with observed site-specific information from tests (i.e., aleatoric uncertainty), a reasonably accurate estimate of the spatial correlation is significantly challenging. Possible reasons include (1) only sparse data being available (i.e., one-dimensional observations are collected at selected locations); and (2) from a physical point of view, the formation process of soil layers is considerably complex. This paper develops a Gaussian Markov random field (GMRF)-based modeling framework to describe the spatial correlation of soil properties conditional on observed electric cone penetration test (CPT) soundings at multiple locations. The model parameters are estimated using a novel stochastic partial differential equation (SPDE) approach and a fast Bayesian algorithm using the integrated nested Laplace approximation (INLA). An existing software library is used to implement the SPDE approach and Bayesian estimation. A real-world example using 185 CPT soundings from Alameda County, California is provided to demonstrate the developed method and examine its performance. The analyzed results from the proposed model framework are compared with the widely accepted covariance-based kriging method. The results indicate that the new approach generally outperforms the kriging method in predicting the long-range variability. In addition, a better understanding of the fine-scale variability along the depth is achieved by investigating one-dimensional residual processes at multiple locations.

Fragility functions define the probability of meeting or exceeding some damage measure (DM) for a given level of engineering demand (e.g., base shear) or hazard intensity measure (IM; e.g., wind speed, and peak ground acceleration). Empirical fragility functions specifically refer to fragility functions that are developed from posthazard damage assessments, and, as such, they define the performance of structures or systems as they exist in use and under true natural hazard loading. This paper describes major sources of epistemic uncertainty in empirical fragility functions for building performance under natural hazard loading, and develops and demonstrates methods for quantifying these uncertainties using Monte Carlo simulation methods. Uncertainties are demonstrated using a dataset of 1,241 residential structures damaged in the May 22, 2011, Joplin, Missouri, tornado. Uncertainties in the intensity measure (wind speed) estimates were the largest contributors to the overall uncertainty in the empirical fragility functions. With a sufficient number of samples, uncertainties because of potential misclassification of the observed damage levels and sampling error were relatively small. The methods for quantifying uncertainty in empirical fragility functions are demonstrated using tornado damage observations, but are applicable to any other natural hazard as well.

Structural health monitoring (SHM) may be exploited to estimate the mechanical properties of existing structures and check for potential damage. Among commonly used methodologies for property characterization, the Bayesian approach holds the lead because it is endowed with the particular advantage of quantifying associated uncertainties. These uncertainties arise owing to diverse factors including (1) sensor accuracy and positioning, (2) environmental influences, and (3) modeling errors. In minimizing the influence of sensor-related uncertainties, an optimal design may be adopted for the SHM campaign to maximize the information content of the measurements. Here, a procedure based on Bayesian experimental design is proposed to quantify the expected utility of the sensor network. The positions of the used sensors are selected in a way that maximizes the Shannon information gain between the prior and posterior probability distributions of the parameters to be estimated. In order to numerically solve the resulting optimization problem, surrogate models based on polynomial chaos expansion (PCE) and stochastic optimization methods are used. The use of surrogates allows one to reduce the computational cost of the associated model runs. The method is applied to a large-scale example, namely the Pirelli Tower in Milan.

A coordinated network of bidirectional chargers can be useful to improve grid stability and resilience and provide revenues to owner/operators of fleets of electric vehicles. This paper quantifies the resilience of a portfolio of vehicle-to-grid technology investments and milestones when it is subjected to a variety of emergent and future conditions that involve technology, environment, market prices, regulations, organizations, and the like. The technical approach consists of three layers, starting from wide national strategic goals and ending with the specific and targeted needs of an individual initiative. Investigated in this analysis are 5 criteria, 4 scenarios, 15 emergent conditions, and 35 initiatives . The results suggest that the most robust and crucial initiative is $x_3$, Regional resource planning, and the most disruptive scenario is $s_2$, Private industry support. The paper has a context in the Systems Engineering Body of Knowledge (SEBoK) (BKCASE Editorial Board 2017) as a systems engineering management methodology that serves to elicit and translate the interests, needs, and knowledge of multiple stakeholders into models of preferences that have a role in the monitoring of emergent and future conditions.