ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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U.S. Gulf Coast refineries account for over half of the total refining capacity of the nation. However, less than a third of products refined in this region are used to supply local markets. Due to the highly centralized nature of the U.S. petroleum distribution network, disruptions affecting Gulf Coast refineries can have widespread impacts. The objective of this study is to develop a sufficient predictive model for the likelihood and expected duration of refinery shutdowns under hurricane hazards. Such models are currently lacking in the literature yet essential for risk modeling of the cascading consequences of refinery shutdown ranging from resilience analyses of petroleum networks to potential health effects on surrounding communities tied to startup and shutdown activities. A database of empirical refinery downtime and storm hazards data is developed, and statistical analyses are conducted to explore the relationship between refinery and storm characteristics and shutdown duration. The proposed method with the highest predictive accuracy is found to be a model comprised of a logistic regression binary classification component related to refinery shutdown potential and a Poisson distribution generalized linear model component related to downtime duration determination. To illustrate the utility of the newly developed model, a case study is conducted exploring the impact of two storms affecting the Houston Ship Channel and surrounding region. Both the regional refining resilience as well as the distribution network resilience are quantified, including uncertainty propagation. Such analyses reveal local community to nationwide impacts of refining disruptions and can support resilience enhancement decisions. This article is available in the ASME Digital Collection at .

Equitable allocation and distribution of the COVID-19 vaccine have proven to be a major policy challenge exacerbated by incomplete pandemic risk data. To rectify this shortcoming, a three-step data visualization methodology was developed to assess COVID-19 vaccination equity in the United States using state health department, U.S. Census, and CDC data. Part one establishes an equitable pathway deviation index to identify populations with limited vaccination. Part two measures perceived access and public intentions to vaccinate over time. Part three synthesizes these data with the social vulnerability index to identify areas and communities at particular risk. Results demonstrate significant equity differences at a census-tract level, and across demographic and socioeconomic population characteristics. Results were used by various federal agencies to improve coordinated pandemic risk response and implement a commitment to equity as defined by the Executive Order regarding COVID-19 vaccination and booster policy. This methodology can be utilized in other fields where addressing the difficulties of promoting health equity in public policy is essential. This article is available in the ASME Digital Collection at .

Despite the pipeline network being the safest mode of oil and gas transportation systems, the pipeline failure rate has increased significantly over the last decade, particularly for aging pipelines. Predicting failure risk and prioritizing the riskiest asset from a large set of pipelines is one of the demanding tasks for the utilities. Machine learning (ML) application in pipeline failure risk prediction has recently shown promising results. However, due to safety and security concerns, obtaining sufficient operation and failure data to train ML models accurately is a significant challenge. This study employed a Generative Adversarial Network (GAN) based framework to generate synthetic pipeline data (D_Syn) using a subset (70%) of experimental burst test results data (D_Exp) compiled from the literature to overcome the limitation of accessing operational data. The proposed framework was tested on (1) real data, and (2) combined real and generated synthetic data. The burst failure risk of corroded oil and gas pipelines was determined using probabilistic approaches, and pipelines were classified into two classes depending on their probability of failure: (1) low failure risk (P_f: 0–0.5) and (2) high failure risk (P_f: >0.5). Two random forest (RF) models (M_Exp and M_Comb) were trained using a subset of 70% of actual experimental pipeline data, (D_Exp) and a combination of 70% of actual experimental and 100% of synthetic data, respectively. These models were validated on the remaining subset (30%) of experimental test data. The validation results reveal that adding synthetic data can further improve the performance of the ML models. The area under the ROC Curve was found to be 0.96 and 0.99 for real model (M_Exp) and combined model (M_Comb) data, respectively. The combined model with improved performance can be used in strategic oil and gas pipeline resilience improvement planning, which sets long-term critical decisions regarding maintenance and potential replacement of pipes. This article is available in the ASME Digital Collection at .

Civil infrastructure systems become highly complex and thus get more vulnerable to disasters. The concept of disaster resilience, the overall capability of a system to manage risks posed by catastrophic events, is emerging to address the challenge. Recently, a system-reliability-based disaster resilience analysis framework was proposed for a holistic assessment of the components' reliability, the system's redundancy, and the society's ability to recover the system functionality. The proposed framework was applied to individual structures to produce diagrams visualizing the pairs of the reliability index (β) and the redundancy index (π) defined to quantify the likelihood of each initial disruption scenario and the corresponding system-level failure probability, respectively. This paper develops methods to apply the β-π analysis framework to infrastructure networks and demonstrates its capability to evaluate the disaster resilience of networks from a system reliability viewpoint. We also propose a new causality-based importance measure of network components based on the β-π analysis and a causal diagram model that can consider the causality mechanism of the system failure. Compared with importance measures in the literature, the proposed measure can evaluate a component's relative importance through a well-balanced consideration of network topology and reliability. The proposed measure is expected to provide helpful guidelines for making optimal decisions to secure the disaster resilience of infrastructure networks. This article is available in the ASME Digital Collection at .

Reliability-based design has been a widely used methodology in the design of engineering structures. For example, the structural design standards in many countries have adopted the load and resistance factor design (LRFD) method. In recent years, the concept of resilience-based design has emerged, which additionally takes into account the posthazard functionality loss and recovery process of a structure. Under this context, the following questions naturally arise: can we establish a linkage between reliability-based design and resilience-based design? Does there exist a simple resilience-based design criterion that takes a similar form of LRFD? This paper addresses these questions, and the answer is “yes”. To this end, a new concept of structural resilience capacity is proposed, which is a generalization of structural load bearing capacity (resistance). The probabilistic characteristics (mean value, variance, probability distribution function) of resilience capacity are derived. Applying the concept of resilience capacity, this paper explicitly shows the relationship between the following four items: time-invariant reliability-, time-invariant resilience-, time-dependent reliability-, and time-dependent resilience-based design methods. Furthermore, an LRFD-like design criterion is proposed for structural resilience-based design, namely, load and resilience capacity factor design (LRCFD), whose applicability is demonstrated through an example. The LRCFD method can also be used, in conjunction with LRFD, to achieve reliability and resilience goals simultaneously of the designed structure. This article is available in the ASME Digital Collection at .

The power transmission infrastructure is vulnerable to extreme weather events, particularly hurricanes and tropical storms. A recent example is the damage caused by Hurricane Maria (H-Maria) in the archipelago of Puerto Rico in September 2017, where major failures in the transmission infrastructure led to a total blackout. Numerous studies have been conducted to examine strategies to strengthen the transmission system, including burying the power lines underground or increasing the frequency of tree trimming. However, few studies focus on the direct hardening of the transmission towers to accomplish an increase in resiliency. This machine learning-based study fills this need by analyzing three direct hardening scenarios and determining the effectiveness of these changes in the context of H-Maria. A methodology for estimating transmission tower damage is presented here as well as an analysis of impact of replacing structures with a high failure rate with more resilient ones. We found the steel self-support-pole to be the best replacement option for the towers with high failure rate. Furthermore, the third hardening scenario, where all wooden poles were replaced, exhibited a maximum reduction in damaged towers in a single line of 66% while lowering the mean number of damaged towers per line by 10%. This article is available in the ASME Digital Collection at .

A technique based on the Wiener path integral (WPI) is developed for determining the stochastic response of diverse nonlinear systems with fractional derivative elements. Specifically, a reduced-order WPI formulation is proposed, which can be construed as an approximation-free dimension reduction approach that renders the associated computational cost independent of the total number of stochastic dimensions of the problem. In fact, the herein developed technique can determine, directly, any lower-dimensional joint response probability density function corresponding to a subset only of the response vector components. This is done by utilizing an appropriate combination of fixed and free boundary conditions in the related variational, functional minimization, problem. Notably, the reduced-order WPI formulation is particularly advantageous for problems where the interest lies in few only specific degrees-of-freedom whose stochastic response is critical for the design and optimization of the overall system. An indicative numerical example is considered pertaining to a stochastically excited tuned mass-damper-inerter nonlinear system with a fractional derivative element. Comparisons with relevant Monte Carlo simulation data demonstrate the accuracy and computational efficiency of the technique. This article is available in the ASME Digital Collection at .

Several on-line identification approaches have been proposed to identify parameters and evolution models of engineering systems and structures when sequential datasets are available via Bayesian inference. In this work, a robust and “tune-free” sampler is proposed to extend one of the sequential Monte Carlo implementations for the identification of time-varying parameters which can be assumed constant within each set of data collected but might vary across different sequences of datasets. The proposed approach involves the implementation of the affine-invariant Ensemble sampler in place of the Metropolis–Hastings sampler to update the samples. An adaptive-tuning algorithm is also proposed to automatically tune the step-size of the affine-invariant ensemble sampler which, in turn, controls the acceptance rate of the samples across iterations. Furthermore, a numerical investigation behind the existence of inherent lower and upper bounds on the acceptance rate, making the algorithm robust by design, is also conducted. The proposed method allows for the off-line and on-line identification of the most probable models under uncertainty. The proposed sampling strategy is first verified against the existing sequential Monte Carlo sampler in a numerical example. Then, it is validated by identifying the time-varying parameters and the most probable model of a nonlinear dynamical system using experimental data. This article is available in the ASME Digital Collection at .

Engineering design and technological risk assessment both entail learning or discovering new knowledge. Optimal learning is a procedure whereby new knowledge is obtained while minimizing some specific measure of effort (e.g., time or money expended). A paradox is a statement that appears self-contradictory, contrary to common sense, or simply wrong, and yet might be true. The paradox of optimal learning is the assertion that a learning procedure cannot be optimized a priori—when designing the procedure—if the procedure depends on knowledge that the learning itself is intended to obtain. This is called a reflexive learning procedure. Many learning procedures can be optimized a priori. However, a priori optimization of a reflexive learning procedure is (usually) not possible. Most (but not all) reflexive learning procedures cannot be optimized without repeatedly implementing the procedure which may be very expensive. We discuss the prevalence of reflexive learning and present examples of the paradox. We also characterize those situations in which a reflexive learning procedure can be optimized. We discuss a response to the paradox (when it holds) based on the concept of robustness to uncertainty as developed in info-gap decision theory. We explain that maximizing the robustness is complementary to—but distinct from—minimizing a measure of effort of the learning procedure. This article is available in the ASME Digital Collection at .