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

Seaports play an essential role in the policies of sustainable mobility as critical links allowing the transfer of goods and passengers, thereby helping to increase the functionality and efficiency of the general system of transportation of a country. Furthermore, seaports represent priority entry points into an area affected by an earthquake and, thus, play a crucial role in rescue operations. Poor performance was exhibited by seaports in past and recent earthquakes, particularly for wharf structures, which suffered severe damage because of ground shaking and soil liquefaction. Seaports represent a multicomponent system in which each element has its own feature and vulnerability. Under earthquake loading, various facilities can be damaged—from wharves with their supporting systems to cranes and other utilities. A potential disruption of one element may trigger a domino (or cascade) effect that influences the performance of the entire system. This paper focuses on the influence of interdependencies among components in the seismic risk assessment of a seaport system. A methodology has been devised to assess the seismic risk of a port viewed as a multicomponent system and applied to the port of Gioia Tauro, the largest terminal for transshipment in Italy and one of the most important hubs for container traffic in the Mediterranean Sea. The Gioia Tauro seaport is in Calabria, which is the region in Italy characterized by the highest seismic hazard. This paper compares the results obtained in the assessment of the seismic vulnerability of the seaport, considering and neglecting the interdependencies among its components.

Data assimilation with a particle filter (PF) has attracted attention for use in Bayesian updating. However, PFs have a problem known as degeneracy, where weights tend to concentrate into only a few particles after a few iterations (all other particles degenerate), which causes poor computational performance. This study discusses the applicability of a PF to the Bayesian updating of model parameters and probabilistic prediction with the updated model and proposes a method that uses a PF to limit degeneracy. The proposed method, called iterative particle filter with importance sampling (IPFIS), uses iterative observation updating in a PF, a Gaussian mixture model, and importance sampling. Two examples are used to demonstrate the proposed algorithm. In the first example, posterior distributions of the stiffness parameters of a two-degree-of-freedom model are identified. In the second example, IPFIS is applied to a consolidation settlement problem of a soft ground due to embankment loading, and probability distributions of the geotechnical parameters in an elastoplastic finite-element model and the simulated settlement displacements are updated based on time-series observation.

Resilience is a concept that can be used to bring additional understanding to safety management, to complement traditional approaches. The additional understanding will enable more-informed safety management decisions to be made by operators. This is critical for operations in remote and hash environments. The concepts of resilience, such as robustness and rapidity, can be used to inform safety management decisions. A methodology was presented that uses quantitative techniques of system performance measurement and qualitative understanding of functional execution from the functional resonance analysis method (FRAM) to gain an understanding of these resilience concepts. Examples of robustness and rapidity using this methodology were illustrated, and how they can help operators manage their operation was discussed.

Traffic volume forecasts are fundamental to policymaking; yet, the impacts of forecast errors are rarely considered. Accordingly, traffic forecasts in 39 studies were compared to observed volumes after the forecast year had elapsed, yielding a mean error of 40%. To evaluate the effect of forecast accuracy, five case studies examined how forecast error affected investment decisions. Because errors of 11%, 12%, 20%, 33%, and 34% can alter investment decisions, the respective observed errors of 20%, 38%, 64%, 6%, and 52% meant the forecast error was potentially large enough to alter the investment decision based on each study’s stated criterion. Although striving for more accurate forecasts is laudable, the results from the 39 studies showed that only a quarter of the variation in study accuracy is not attributable to random variation. Accordingly, until highly accurate forecasts become the norm, an interim recommendation for planners is to use empirical decision intervals, as shown herein, indicating the magnitude of error can alter investment decisions.

Detecting anomalies is an important and challenging task for many applications. In recent years, spectral methods have been proposed to detect anomalous subgraphs embedded into a background graph using eigenvectors corresponding to some of the largest positive eigenvalues of the graph’s modularity matrix. The spectral methods use the standard Lanczos-type eigenvalue solver to compute these exterior eigenpairs. However, eigenvectors with interior eigenvalues could also indicate the existence of anomalous subgraphs. In this study, we propose an efficient method using a complex moment-based eigenvalue solver, which can efficiently search anomalous subgraphs related to eigenvectors with both exterior and interior eigenvalues. Experimental results show the potential of the proposed method.

This paper proposes a Bayesian method of measuring the similarity between a set of site-specific data and individual records in a generic geotechnical database. All data are assumed to be multivariate, uncertain, unique, sparse, and incomplete (MUSIC). A similarity measure is proposed, and the effectiveness of the proposed similarity measure is illustrated by considering a specific clay database and a specific site in Norway that is outside the database. The records are weighted by their similarity measures and combined with the limited site-specific data to construct a quasi-site-specific transformation model based on a more restricted and more relevant coverage of geotechnical characteristics relevant to the site.

This study introduces tractable algorithms to model the decentralized decision-making process for postdisaster restoration of interdependent networks. Restoration strategies are devised by a host of agents, who control different layers of interdependent networks. Because of interdependencies, each agent’s decisions are impacted by other agents’ decisions. However, agents communicate poorly in real-world settings—particularly after contingencies—and therefore, they cannot access all the information that is necessary in order to make a decision; instead, they compensate with their expert judgment. We propose the notion of Judgment Call to model this practical human contrivance. We explore several types of judgment, including pure random judgment, optimistic judgment, and judgments guided by importance measures. The performance of the method is compared to optimal solutions applied to a database of ideal networks that contains different configurations of random, scale-free, and grid networks. In addition, we apply the method to the interdependent infrastructure network of Shelby County, Tennessee, as a realistic case study. The results show that the method performs best when there is no randomness in the decentralized judgment process, which highlights the need to improve communication, monitoring, and modeling of distributed systems in order to reduce uncertainties. Moreover, the results show that interdependency density and resource availability influence the performance of the Judgment Call method the most. These insights are key for decision support in the practice of postdisaster recovery and community resilience.

Infrastructure systems are the backbones of the socioeconomic development of a community. However, after installation, these engineered systems undergo deterioration, leading to a degradation in their condition while in operation. In this work, a generalized modeling framework is proposed and validated for the diagnosis and prognosis of infrastructure systems based on real-time data. A data-driven modeling scheme, dynamic mode decomposition (DMD), is used for prognosis. The novelty of the proposed framework lies in the fact that the developed prognostic model is data-driven and physics informed, and the model works better on problems with unknown/implicit governing equations and boundary conditions. The developed prognostic model provides more accurate predictions based on real-time data and identification of dominant spatiotemporal modes, as evident from the application of mortar cube crack prediction under compressive testing. This framework can be recommended to researchers/practitioners for predicting the remaining useful life of infrastructure components and systems before their maintenance or failure. Such robust predictions of the future condition of existing infrastructure will be beneficial to stakeholders for sustainable development.

Power distribution systems are susceptible to significant damage when hit by a tornado. This damage can cause huge economic losses resulting from the cost of repairing failed distribution lines, the loss of electricity service, and the indirect impact on other infrastructure systems that depend on the power system. Hence, it is important to study the reliability of power networks when hit by tornadoes, especially in tornado-active regions. However, there is still a lack of risk-based methods to study the behavior of power distribution systems when subjected to tornadoes. In this paper, scenario-based reliability and cost analyses of power distribution systems subjected to tornadoes are performed. In addition, the cost-effectiveness of targeted hardening of the power distribution lines is investigated. A notional power distribution system assumed to be located in Norman, OK, is used to demonstrate the proposed framework. The results of the case study show that targeted hardening can considerably increase the reliability of the power distribution system and reduce the total direct cost when hit by a tornado.

This paper presents the derivation, verification, and application of sampling-based reliability sensitivities. The direct differentiation method is employed to develop the analytical derivatives of the failure probability, and thus the reliability index, with respect to the distribution parameters of the underlying random variables in a sampling analysis. Particular attention is devoted to deriving the formulation for correlated random variables with arbitrary probability distributions. This entails analytical differentiation of the Nataf transformation. The resulting formulation is verified through a linear example with a closed-form solution and a nonlinear example. Thereafter, the proposed approach is utilized in two real-world applications. First, it is used to identify the random variables that are most influential on the seismic reliability of a reinforced concrete structure. Second, the proposed approach is employed to prioritize a building portfolio for retrofit based on the amount of reduction of the risk to the entire portfolio per dollar spent on retrofitting each building. The proposed approach is robust and works for highly nonlinear or nondifferentiable limit-state functions. It also only slightly increases the computational cost of sampling because it does not need the gradient of the limit-state function with respect to the underlying random variables.

The seismic design of buildings uses global ductility factor and occupancy importance factor ($IF$) as two major fixed parameters in defining the safety of the structure. The study of performance variation of the structure with global ductility factor is available but there is hardly any study that provides information regarding the increase in the level of safety achieved by increasing the $IF$ values. Being a building categorical dependent parameter, $IF$ is used by the international seismic design codes for increasing the design loads of the structure. The change in the level of safety achieved through the variation in the value of the $IF$s for reinforced concrete (RC)–framed buildings will perhaps be an important and useful representation of the stakeholders for the approximate damage cost estimation. This article performs the structural safety assessment against seismic load using a standard structural reliability method with second-order hazard approximation to evaluate the effect of the $IF$ on the level of safety and the cost associated with the building. Results show that an overall reduction of 50%–60% in the damage index of the selected buildings can be achieved by increasing the $IF$ from a value of 1.0–2.0 with a consequent increase in the cost of the building.

Metro networks are complex systems that provide efficient and reliable transportation services for communities and play prominent roles in sustaining local economies, yet investments and safety enhancements to the networks may not receive appropriate attention. Metro networks consist of a large number of interacting nodes and links. Any adverse event leading to disrupted network components’ interaction and connectivity would dramatically affect the safety and well-being of commuters as well as direct and indirect costs associated with performance loss. Therefore, enhancing network resilience could lead to a boost in network efficiency and performance, ideally taken in a cost-effective manner. This paper provides a methodology to quantitatively measure the most vulnerable segments of a metro network using the Washington, DC Metro as a case study. Analyzing the network vulnerability is a basis to measure the resilience of the network. This paper then offers strategies to increase the resilience of the metro by enhancing its topology prior to any failure, such as adding an interloop (loop line hereafter) to the network. In addition, an approach to identifying proper postfailure recovery strategies with special attention not only on restoring connectedness but also on minimizing the total cost associated with a disruptive event resulting in resilience loss is extensively elucidated. The analysis results show that the most vulnerable segments of the Washington, DC Metro are transfer stations and their associated links located in the central part of the city. As such, adding an optimal loop line could create redundancy to these vulnerable segments and improve network resilience by increasing the network efficiency. Furthermore, the proposed recovery analysis and cost model herein enables decision-makers to identify the best recovery strategy according to both paramount recovery sequence and minimum cost consideration. The best recovery sequence typically reflects the order of components ranked based on their degree of vulnerability in the network. The use of the methodology proposed herein may lead to significant societal benefits by reducing the risk of catastrophic failures, providing references for mitigation of disruption due to adverse events, justifying capital improvements to the network, and related pursuits.

This paper proposes a data collaboration analysis framework for distributed data sets. The proposed framework involves centralized machine learning while the original data sets and models remain distributed over a number of institutions. Recently, data has become larger and more distributed with decreasing costs of data collection. Centralizing distributed data sets and analyzing them as one data set can allow for novel insights and attainment of higher prediction performance than that of analyzing distributed data sets individually. However, it is generally difficult to centralize the original data sets because of a large data size or privacy concerns. This paper proposes a data collaboration analysis framework that does not involve sharing the original data sets to circumvent these difficulties. The proposed framework only centralizes intermediate representations constructed individually rather than the original data set. The proposed framework does not use privacy-preserving computations or model centralization. In addition, this paper proposes a practical algorithm within the framework. Numerical experiments reveal that the proposed method achieves higher recognition performance for artificial and real-world problems than individual analysis.

Although the properties of geomaterials vary spatially, geotechnical site investigations often take sparse measurements from a limited number of locations. To estimate geotechnical properties at unsampled locations, interpolation is often needed. This paper presents a Bayesian supervised learning method for interpolation of site-specific geotechnical data from sparse measurements. The interpolation is considered as a supervised learning problem and is solved under a Bayesian framework. Numerical examples are used to evaluate performance of the proposed method and to provide a comparative study with ordinary kriging, a popular interpolation method in geosciences applications. Results show that when the available measurement points are sparse and limited, the Bayesian supervised learning method performs better than kriging. When the number of measurement points is large, results from the proposed method and kriging are almost identical. In addition, the proposed method is data-driven and nonparametric. It does not require a detrending process when dealing with nonstationary data, and it bypasses estimation of a parametric form of autocorrelation structure (e.g., semivariogram in conventional kriging interpolation). A well-known challenge in kriging is the selection of a suitable semivariogram function form or a suitable trend function form for detrending, given sparse geotechnical data. The proposed Bayesian supervised learning method bypasses these challenges and is particularly suitable for nonstationary geotechnical data. Standard preprocessing steps such as outlier removal and noise reduction apply to Bayesian supervised learning.

Hurricane hazard is one of the major causes of the loss of life and property. It has recently led to enormous economic losses and social disruption, specifically in coastal regions. Monetary loss and damage to the built infrastructure represent a significant portion of overall hurricane-induced financial losses. A detailed simulation of hurricane loss at a regional scale requires a large amount of specific information, which is usually not available with a sufficient level of certainty. The existing wind-induced loss models often assume a prescribed mathematical structure to describe the dependency between aggregated loss and hazard intensity in an average sense. The effect of uncertainty is introduced by treating model parameters as random variables. In the present study, a new approach to tackle this problem is introduced, which relies on a more rigorous and reliable quantification of the associated uncertainties. In particular, the loss induced by wind is modeled as a nonstationary stochastic process for which a probabilistic representation is constructed using polynomial expansion. As a case study, economic loss data collected by an insurance company are used to calibrate and test the predictive capability of the proposed stochastic hurricane loss model. This representation has the advantage of being based on minimal prior assumptions and constraints, in addition to being computationally less demanding because it generates the vulnerability at a coarser regional level. In order to quantify the regional risk from the proposed loss model, an extension to the evaluation of the storm risk curve or loss-exceedance curve for the region is presented.

This paper proposes a fully probabilistic and analytical measurement framework for assessing the resilience of linear power-distribution systems affected by hurricane wind. The topology of rural power-distribution systems can be largely characterized as a set of linear subsystems that emanate from one power substation and individually feature zero redundancy. Moreover, rural power-distribution systems are less robust owing to their high vulnerability to material aging, and they demand longer recovery times due to low socioeconomic resources in rural areas. The proposed framework includes a mechanical analysis of a coupled wood pole and feeder line as a system unit, a definition of component restoration and system-level recovery functions, and, finally, a definition of a new resilience measure, referred to as total mean system resilience (TMSR). Numerical experimentation is provided that validates the effectiveness and analytical tractability of the framework. Insight into how physical aging, local resourcefulness, and spatial sparseness interplay and affect the system resilience is given quantitatively in this paper.

The robust result of analytical fuzzy reliability analysis (FRA) represents the main effort at evaluating the fuzzy reliability index. In this study, a bioloop-based hybrid method is proposed for structural FRA. The genetic operator as optimization solver combined with adaptive descent chaos control (ADCC) as a probabilistic solver called GA-ADCC is applied to evaluate the fuzzy reliability index. The ADCC-based reliability method is formulated based on a dynamical chaos control factor that is computed using an adaptive descent approach from the new and previous results. In GA-ADCC, an outer loop–based genetic optimizer constructs the membership reliability index using an alpha level set. To compute the membership functions of the reliability index, three structural problems are used to show the capability of the proposed method. Results demonstrate that the proposed GA-ADCC method can be used to evaluate reasonable uncertainty bounds in FRA, and it provides the accurate member shape functions for reliability index.

This article studies incentives to share risk as a risk management tool to address issues of interdependency among risk assessment, risk perception, and risk management in large civil engineering projects. The decision problem of an operator in charge of constructing reliable sea defenses is studied, and it is shown that operators have no incentive to reduce the likelihood of rare but extreme floods because their liability for damage costs cannot exceed the value of their private assets. An evaluation is made of the level of liability that induces cost-effective safety measures to reduce the probability of extreme events without bankrupting the operator, and its impact on the welfare of society is studied. It turns out that society will be better protected at a significantly lower cost when tacitly retaining the residual risk of extreme damage costs. The findings offer an explanation as to why stakeholders tend to ignore the potential costly consequences entailed by the failure of civil engineering projects. Although these catastrophic failures are rare, this paper contends that society, as a whole, should bear the residual risk of such events.

Due to the complex and uncertain nature of geomaterial properties, establishing representative parametric models between clay parameters is a challenging task. Nonparametric machine learning offers an accessible approach to develop empirical transformation models of clay parameters based on the available measurement. In this study, nonparametric modeling of clay parameter relationships via broad learning system (BLS) is introduced. Broad learning architecture provides an effective tool for nonparametric modeling based on noise-corrupted data. The architecture of deep learning is configured with stacks of hierarchical layers, which consume expensive computational cost for network training. In contrast, the network of BLS is established in a flat architecture and it can be modified incrementally. As a result, the broad learning flat network can be reconfigured efficiently to accommodate additional training data. To demonstrate the performance of the learning algorithm for clay parameters, the comprehensive global database CLAY/10/7490 with 7,490 data points from over 250 studies worldwide is utilized and analyzed.

Flood events are one of the most common natural disasters in the United States and can disrupt businesses; strain the financial resources of agencies that respond; and often leave households dislocated for days, months, or permanently. Community resilience planning requires an assessment of the damage and loss caused by a hazard followed by recovery modeling, which couples the socioeconomics with the physical-infrastructure recovery process. This paper focuses on the first part of that analysis chain, namely damage and loss modeling to riverine flooding at the community level, with a case study of Lumberton, North Carolina, using empirical damage fragilities. The process includes the major components toward flood-loss quantification. The losses in the case study are computed from the damage fragilities and compared with the deterministic flood loss analysis in HAZUS-MH, which uses stage-damage functions. For the case study presented in this paper, the fragility-based approach resulted in slightly higher loss estimates. The fragility-based approach presented as part of this study can provide a mechanism to propagate uncertainty in damage and loss estimates. This ability to propagate such uncertainty into the analysis would allow for risk-informed decision making for floods using a similar approach to what is currently done for earthquake and wind community-level loss analyses.

Utility operators must rely on predictive analyses regarding the availability of their subsurface assets, which highly depend on damage by increasing amounts of excavation work. However, straightforward use of standard statistical techniques, such as logistic regression or Bayesian logistic regression, does not allow for accurate predictions of these rare events. Therefore, in this paper, alternative approaches are investigated. These approaches involve weighting the likelihood as well as over- and undersampling the data. It was found that these data methods could substantially improve the accuracy of predicting rare failure events. More specifically, an application based on the real data of a Dutch water utility operator showed that undersampling and weighting improved the balanced accuracy, varying between 0.61 and 0.66, whereas the proposed methods resulted in failure predictions on between 38% and 58% of the validation data set. Hence, the proposed methods will enable utility operators to arrive at more accurate forecasts, enhancing their asset operation decision-making.