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

Cyber-human-physical systems are deeply intertwined systems of physical components, cyber components, and human activities. Cyber components provide communication and control to the physical systems. In addition, human activities can affect or be affected by the behaviors of cyber and physical components. Modern critical infrastructures are a perfect example of such systems. Specifically, physical infrastructures (e.g., power grid, transportation) rely heavily on cyber infrastructures [e.g., supervisory control and data acquisition (SCADA) system] for control and monitoring. Also, humans interact with critical infrastructures in a complex way such that the resilience of critical infrastructures can be heavily affected by human activities. Indeed, critical infrastructures can suffer as a result of both large-scale events such as natural disasters and all kinds of other types of events such as cyber attacks and human operational errors. Therefore, the modeling and analysis of such complex systems are desirable. Such complex systems and their behavior can be modeled appropriately by networks for the purpose of assessing their key attributes, such as resilience. This paper provides an in-depth review of the network modeling and analysis approaches for cyber-human-physical systems. The focus of this paper is critical infrastructures, while the reviewed approaches can also be applied in other application domains.

Risk is ubiquitous and is associated with a very wide range of human activities. This has resulted in the development of an internationally agreed set of principles and guidelines for risk management in general. This paper describes the methods and processes that have been developed for general risk management, along with some of the knowledge upon which those methods and processes are based. It is argued that road safety could be improved by employing those methods and processes.

Estimation on the uniaxial compressive strength (UCS) of rock is an important issue in geotechnical engineering. Empirical relation establishment for UCS estimation is particularly favorable since core sample measurement is expensive, time consuming, and even infeasible. In this paper, two-stage Bayesian learning–based data analysis of UCS of rock is proposed. In the first stage, the sparse Bayesian learning, through the use of the automatic relevance determination (ARD) prior, is adopted to automatically select the relevance features among a set of possible features for the optimal empirical model. In the second stage, the optimal model-based outlier analysis for prediction reliability assessment is performed. The probability of outlier (PO) is utilized as a probabilistic measure for outlierness of a test point. The Gauss–Hermite quadrature is developed for efficiently evaluating the integral for the PO. A binary classification (regular class or outlier class) in the feature space is conducted based on the spatial distribution of the detected regular points and outliers, and the prediction unreliable region is depicted based on the classification result. In the example, the proposed two-stage Bayesian learning is applied for analyzing the UCS of the granite from Macao. The results show that the proposed learning is capable of conducting relevance feature selection and prediction reliability assessment simultaneously.

Exact analytic expressions are given to evaluate the reliability of systems consisting of components, connected in parallel or series, subject to imprecise failure distributions. We also proposed a simplified version of the first-order reliability method to deal with imprecision. This development allows engineers to evaluate the reliability of systems without having to resort to optimization techniques and/or Monte Carlo simulation. In addition, this framework does not need to assume a distribution for the epistemic uncertainty, which permits a robust analysis even with limited data. In this way, the approach removes a significant barrier to the modeling of epistemic uncertainties in industrial probabilistic safety analysis workflows.

With a view to enhance road network’s safety, it is crucial to focus primarily on its critical infrastructures, one part of which is tunnels. Fire safety of tunnels concerned intensely the public opinion after the disastrous trans-Alpine accidents in Europe in the late 1990s. Therefore, risk assessment was officially introduced for ensuring the tunnels’ level of safety. Bearing in mind that trapped-users’ performance can strongly determine a tunnel’s level of safety, this paper proposes an evacuation simulation model for increasing the efficiency of quantitative risk assessment. The model aims at acting as an add-in to the traditional computational fluid dynamics models by linking their results with trapped-users’ evacuation. The model structure is twofold. Initially, through estimating potential losses among trapped-users, the effectiveness of important parameters for the safety of the tunnel system is measured. Subsequently, if needed, the performance of additional to standard safety measures is examined. The model aids safety analysts in making better-informed decisions regarding a tunnel’s level of safety. An application example is presented to illustrate the utilization of the model.

Footbridges, as the fundamental structures spanning natural or artificial obstacles, not only perform the function of weight-bearing but also entail the role of pedestrian transporting. From the pedestrian transporting point of view, the understanding of how pedestrians move on a footbridge is crucial to ensuring the safety of pedestrians, particularly under the strike of all kinds of emergencies. In this sense, developing a rational and realistic simulation framework to describe the pedestrian dynamics and then to analyze the risk of crowds on a footbridge is of prime significance for a thorough consideration in the design stage and safety monitoring in the operating stage. This paper extended the floor field model onto the region level for a better description of the crowd status on the footbridge. Herein, the factors that influence the moving strategies of the pedestrian were incorporated, such as distance to destination, herding behavior, inertia effect, and right-moving preference. And the pedestrian simulations in different parts of the region were effectively organized to reflect the real status of pedestrian transportation on the footbridge. On the basis of the data extracted from the pedestrian simulation process, a procedure was established to fulfill the task of analyzing the risk evolution of onbridge crowds over the time horizon. As an illustration, two kinds of footbridge schemes were investigated in terms of the crowd’s risk evolution via the adoption of the proposed framework. This work combined the pedestrian simulation model and the risk analyzing method in an integrated framework in a bid to lend some initial, yet original insight into the risk evolution analysis of crowds on the region level from the simulation perspective.

Modular components and assemblies involve complex geometric relationships whereby excessive geometric variability in a critical component can affect the overall geometry of the aggregated and assembled structure onsite. These unique geometric relationships can expose modular projects to a number of related risks that propagate from one to another by means of escalation phenomena. Improper assessment and reactive management of these related risks typically results in extensive site-fit rework, cost overruns, schedule delays, and quality issues. Current modularization practices and previously developed risk management frameworks/toolkits lack a practical methodology for quantitatively evaluating the unique relationships among tolerance-related risks and for proactively managing their impact. These considerations motivated the research introduced in this paper: the development of a framework for the holistic assessment and efficient management of excessive geometric variability risks in modular construction projects. In the framework presented here, a classical risk assessment technique, in which a probability-impact risk model is employed for evaluating risks individually from a local perspective, is linked with an innovative technique for considering interactions among risks from a global perspective, which employs the concepts of a design structure matrix and pairwise comparisons using the analytical hierarchy process. The results of a case study conducted for validation purposes demonstrate that the developed framework can provide industry practitioners (owners, designers, fabricators, and contractors) with a better understanding of the risk profile for a project as well as new insights into the development of proactive mitigation strategies from both a local and a global perspective.

The flows and densities of traffic streams play an important role in defining the performance of roadways and corresponding improvement strategies. Traffic flows and densities are the outcome of complex psychophysical actions of drivers. Actions performed by the drivers while driving can be quantified in terms of the headway and/or spacing that they maintain with respect to the vehicles they follow. However, the inherent randomness that exists in human driving behaviors results in random headway and spacing, which leads to uncertainties in predicted traffic flows and densities. As a result, it is important to quantify these uncertainties, because they play an important role in proposing improvement strategies. In this study, a derived distribution method–based uncertainty quantification of traffic flows and densities is proposed; it involves the modification of deterministic flow–headway and density–spacing relationships into probabilistic ones. Analytical expressions were derived for the probability distributions of flows and densities, given the headway and spacing distributions, respectively, which are conditional on velocities. The estimation of the distribution parameters and the validation of the proposed approach were carried out using the Next Generation Simulation (NGSIM) trajectory dataset. The results indicated that the proposed analytical distribution models represented empirical field observations quite accurately.

Risk assessment is an integral part of project management; however, its implementation is often challenging. One of the key challenges is how to collect reliable data for modeling risks. The objective of this paper is to introduce a network-based approach for risk assessment when data is limited or unreliable. The proposed structural network measures focus on relative connectivity of project tasks, resources, and risk events; or in other words, the data that are typically available from project planning and considered more reliable. The central concept is built upon the method called resource-based project risk management, which involves the identification and assessment of risks in terms of resource impacts and the subsequent effects on duration and/or cost of project tasks. The proposed methodology is illustrated using case study data.

This study employs a random parameters binary logistic regression (LR) to characterize the impact of environmental and structural parameters on concrete highway bridge deck deterioration nationwide. Two specific gaps in the literature are addressed: (1) the use of a nationwide dataset for analysis, and (2) the implementation of a methodology to account for unobserved heterogeneity. A total of 3,262 bridge deck deterioration observations derived from the authors’ nationwide concrete highway bridge deck performance inventory (NCBDPI) database were used in this study. The deterioration rate (DR) was computed as the decrease in the concrete bridge deck condition rating (CR) over time. Bridge decks with deterioration rates (DR) below a certain threshold were categorized as the lowest deteriorated bridge decks (lowest DR) and decks with DR above a certain threshold were considered among the highest deteriorated (highest DR). The following variables were found to be significant in the final model: average daily truck traffic (ADTT), climatic region, distance from seawater, bridge deck area, age of bridge, type of design and/or construction, structural material design, deck protection, type of membrane, type of wearing surface, and maintenance responsibility. The results show that bridge decks with a high ADTT, age of bridge, bridge decks located in cold regions, and those that are close to seawater are associated with the highest DR group of bridge decks. Furthermore, the type of design and/or construction and maintenance responsibility play a role in decks being associated with highest DR.

Civil infrastructure systems have two essential characteristics: long service life and low probability of failure. Under these circumstances, life-cycle management (LCM) of civil infrastructure involves time preference of decision-makers on potential gains associated with maintenance actions and the perception of low probability (i.e., risk perception). In the decision-making process, decision-makers are likely to discount the desirability of decision alternatives involving future gains and low probabilities. This attitude is usually referred to as delay and probability discounting. This paper presents a novel approach for the LCM of civil infrastructure considering delay and probability discounting. The model and parameters used in this approach are obtained from a survey conducted among practicing civil engineers. The survey involved low probabilities and long time horizons commonly encountered in the field of civil engineering. The proposed approach is applied to the LCM of an existing highway bridge. It is found that probability discounting can reflect the risk aversion in the decision-making process, while the delay discounting may hinder timely maintenance actions, leading to higher life-cycle risk of civil infrastructure.

In an advanced traveler information system (ATIS), this study examines how to map a driver’s time constraints and risk-taking behavior to real-time routing in a probabilistic time-dependent network (or stochastic network). Accounting for en route delays and alternate routings, ATIS networks are shown to exhibit other than the first-in first-out property (FIFO) behavior: drivers who depart earlier may not arrive ahead of those who depart later. In this paper, the term FIFO is used well beyond the traditional connotation of a single queue or a single path; it applies toward multiple routes. It is used to describe a well-recognized phenomenon in dynamic traffic assignment, wherein a commuter who delays their departure time may arrive at work earlier than one who takes off earlier. Given a network with full spatiotemporal information, a wait-time search algorithm is employed to account for the best-planned delays at the origin or en route. The algorithm elicits the bottlenecks in the network and obtains the optimal wait times a driver needs to avoid these bottlenecks, given their tolerance for risk. The herein defined routing policy makes decision at every network node—based on the current states—to determine the optimal wait time (if any) and the next-hop node. The model also valuates a driver’s risk tolerance by imputing the worth of safety as a cost metric. Empirical results were obtained from a central Arkansas highway network based on incident reports obtained from the state police between the years 2000 to 2003. Solidly founded on Bellman’s optimality condition, the fundamental diagram of traffic flow, and multiattribute utility theory, the algorithm is shown to be operationally feasible for real-time applications.

This work concerns the stochastic finite-element method (SFEM) reliability analysis of a skeletal, very slender steel telecommunication tower calibrated with a full-scale pushover experiment carried out for the same tower. The basic SFEM implementation is completed with common application of the generalized stochastic perturbation technique (SPT) and of the response function method with polynomial basis of the statistically optimized order. Verification of the proposed stochastic technique is carried out for the first four probabilistic moments by a comparison with traditional crude Monte Carlo simulation and, alternatively, with semianalytical methodology using the same polynomial structural responses as SPT and the well-known probabilistic integral definitions. Characteristic mean wind speed is introduced here as the input random Gaussian variable, while the computational model is subjected to the wind profile linearly dependent upon this mean speed consistent with applicable European design codes. The SFEM analysis is carried out here including large deformations of the tower elements and dynamic excitations in each node induced by the given trial wind spectrum. Finally, second-order reliability method (SORM) analysis is provided on the basis of the resulting extreme normal forces in the tower legs and their counterparts taken directly from the full-scale pushover experiments. The obtained results show that reliability indices for these towers according to the engineering codes have larger magnitudes than these corresponding to the experimental statistics and SFEM.

Site investigation through boreholes, such as vane shear tests or cone penetration tests, provides an effective means to assess the performance of geotechnical sites. Boreholes should be located such that they provide a maximum amount of information. This study develops a systematic approach for optimizing site-exploration programs based on maximizing the value of information. To compute the value of information, test data are randomly generated based on the prior model of soil parameters. Bayesian updating of the slope reliability is carried out by application of an adaptive version of the Bayesian updating with structural reliability methods (BUS) in combination with subset simulation, which is efficient in tackling problems with discrete random field representations. The approach is used for the reliability assessment of a slope in undrained clay, wherein the site-exploration program is optimized with vane shear tests to identify the undrained shear strength. Results show that the approach is able to identify the optimal location and number of boreholes.

Aging affected structural components negatively impact the lateral load–resisting capacity and fragility of highway bridges during seismic shaking. Until now, a majority of existing literature on corrosion deterioration of highway bridges assumed a constant or time-varying degradation rate that is independent of initial climate conditions or their variations along service life. Recent research on climate change indicates a time-dependent shift in atmospheric temperature and relative humidity due to global warming. These effects, compounded along the service life with heightened chloride ingress, require careful consideration for bridges located in regions near marine sources and characterized by moderate to high seismicity. The present study proposes a multihazard framework to evaluate seismic fragility of highway bridges considering earthquake hazard, aging effects, and global warming due to climate change. The framework, when demonstrated on case-study multispan continuous steel girder bridges in the central and southeastern United States, reveals that potential climate change effects are likely to further exacerbate the seismic performance of aging bridge structures.