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

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

The real-time identification and early warning of the state of the track are significant to keep the high-speed railway (HSR) safe, stable, and comfortable. This paper proposes a new risk identification method based on the time-ordered weighted averaging operator and track quality index (TOWA-TQI) by using the time-weighted vector to aggregate time-optimal information of objects. Meanwhile, an energy coefficient is introduced to make the weighted track irregularity data as the same energy as the original track irregularity data that can control the weighted amplitude at the same level. To quickly classify the objects, this paper proposes a center and radius clustering (C-R clustering) method that can classify the points into different categories by judging that the distance from the central point is less than the corresponding radius. Moreover, the specific location is located by labeling the categories. Lastly, a practical case is carried out to verify that the proposed method is more accurate and effective.

Characteristics at nodes in a network, such as values of demand, evolve over time. To make time-dependent decisions for a network, making time series predictions at each node in the network over time is often necessary. Typical time series prediction approaches are based on historical information. However, these fail to account for network-level factors that might affect nodal values. This paper proposes an approach for the time series prediction in nodal networks that accounts for both time history information and nodal characteristics in the prediction. The approach is based on recurrent neural networks and, in particular, gated recurrent units (GRU), creating a new GRU structure called a Pairwise-GRU to include the influence of both historical data and neighboring node information to predict values at each node in the network. The result is a more accurate and confident time series prediction. The performance of the proposed approach is tested using an electricity network in the southeastern United States. The results indicate that the proposed Pairwise-GRU outperforms existing methods in terms of increased accuracy and decreased uncertainty in the prediction. The approach performs particularly well for long-term, multiple-time-steps ahead predictions and anomalous hazard conditions in addition to normal operating scenarios.

The building industry has been deeply affected by the Covid-19 pandemic due to the cessation of many businesses and the changes in working methods. Building inspection is of great importance for ensuring safety and well-functioning steps in the building life cycle. This study aimed to determine the potential dangers threatening building inspection services during and after the Covid-19 pandemic. Accordingly, a risk assessment method based on a three-stage survey conducted with mainly construction inspection experts working in the Turkish construction industry was applied. Within this scope, both quantitative and qualitative data were utilized. An analysis of all findings concluded that careless inspection due to the shortening of site visits, unreliable remote inspection ways, and the everlasting paper-based bureaucratic steps of the system are the most threatening risk factors for building inspection mechanisms. All these risks have potential consequences, such as loss of life, crucial defects in building services, and the spread of the pandemic. Indirect risks are mainly caused by financial difficulties. In order to avoid all potential risks, standards should be introduced and technology-based precise remote control mechanisms should be improved through online tools and applications. Moreover, public bodies should provide adequate financial support. This study suggested immediate actions to be taken by the relevant parties against the impacts of Covid-19 by highlighting potential cases and their risks with regard to building inspection services. The implementation of such actions may enable practitioners to conduct more reliable inspections during and after the Covid-19 pandemic.

The fifth generation (5G) technology standard for cellular networks is currently being developed and is in the early stages of rollout across the United States. This upgrade from 4G LTE (long-term evolution) will not only have implications for the telecommunications network itself, but also on the many critical infrastructure systems that will use and depend on 5G for operations and functionality. Cellular vehicle-to-everything (C-V2X) technology utilizes 5G and has the potential to improve the safety and efficiency of the transportation system by allowing vehicles to communicate with one another and automating certain driving features. However, it is important to consider the risks that 5G brings, including cybersecurity risks, and how attacks through the 5G network can disrupt a traffic network that includes C-V2X technology. This paper presents a method to characterize the effects of several risk scenarios. Compared to prior qualitative risk assessments, outcomes include quantitative indicators measuring system safety and performance for analysis. The approach enables a more detailed and rigorous assessment of interdependent systems risks between the telecommunications and transportation networks than previously possible, particularly in the transition to 5G. A range of potential risk scenarios are assessed. The results show that cyberattacks that alter the behavior of vehicles cause delays across the entire network, cascading across the system and affecting more than just the vehicles directly targeted. The simulations show different levels of traffic delays and numbers of collisions for each risk scenario, indicating that the effects of a cyberattack can differ widely depending on the specifics of the attack. The simulation is easily adaptable to the location, C-V2X features, and risk scenarios of interest. Results provide information to formulate and prioritize risk mitigation strategies as the technology is developed to minimize the impacts of these attacks and system disruptions.

This study originated with the intent of qualifying traffic string stability from empirical observations. A new responsiveness angle measure was developed to assess driver instincts under vehicle-following conditions. In this measure, the degree of the follower vehicle’s attention towards its leader vehicle’s actions is quantified. In understanding string stability in the traffic stream and assessing the propagation of disturbances, the newly conceptualized measure was used along with a discrete Fourier transform to measure the frequencies associated with responsiveness angle sequences. In this transform, a higher frequency of the angle depicts unstable conditions and vice versa. In assessing string stability from the empirical observations, vehicular trajectory data were developed from three study sections. Two study sections tended to have homogeneous lane-wise traffic, whereas the third section had mixed (heterogeneous) traffic. The results of the string stability analysis over the study sections showed that string stability varied with the change in traffic flow conditions, road geometries, and traffic flow type. In the case of free-flow conditions, the traffic streams were found to be stable with marginal disturbances in the responsiveness angle. From the analysis, it was observed that, in the case of study Section 3, around 26 instances of the stream were extremely unstable conditions (frequency equal to 10). For study Sections 1 and 2, the traffic stream was unsteady for 4 and 13 instances, respectively. However, as the traffic flow level rose, string stability deteriorated. This study demonstrated a novel approach to analyzing string stability based on actual traffic conditions that can be implemented in real time for traffic stream monitoring.

This paper presents a study of the system reliability of mechanically stabilized earth (MSE) walls by considering all four external failure modes: excessive eccentricity, lateral sliding, insufficient bearing capacity, and deep-seated instability. The focus of this study is (1) to quantify the correlation between any two failure modes, (2) to statistically examine the dependency between any two failure modes, (3) to estimate the system probability of failure using both Monte Carlo simulation (MCS) and formula of union, and (4) to validate the probability bounds of system reliability. Based on MCS, the probabilities of the intersections and unions of various combinations of failure modes are evaluated. The results from this study point to the importance of incorporating all failure modes in a series system of MSE walls and reveal the statistical correlation and independence among different failure modes.

The paper presents an optimal design for a gravity retaining wall based on a mixed-integer constrained optimization. To solve this optimization problem, a real coded genetic algorithm was applied. The gravity retaining wall optimization model (OPT-GRW) includes a cost function of the wall, which is limited by design and resistance constraints. The deterministic optimization model, based on partial safety factors, was extended by an optimization approach in which the cost objective function was constrained by a defined target probability of failure. The paper presents an example for determining the most cost-effective gravity retaining wall for given design parameters. The presented case study shows the optimization process based on Eurocode 7, which takes into account part of the uncertainty of the soil parameters. As the study progresses, the need for optimization based on the probability of failure becomes crucial, as several designs exist with the same probability of failure, but only one is optimal.

The risk level of an expansive soil landslide (ESL) is a key parameter employed for slope stability assessment. It also serves as a starting point for a risk management strategy constructed to mitigate the risk of slope failures and their harmful consequences. However, it is usually underestimated because of the complexity of geological and environmental conditions and the lack of systematic instability categorization. This study develops a probability evaluation method using Takagi–Sugeno (T–S) fuzzy fault tree analysis (T–S FFTA) for qualitative and quantitative risk assessment of the ESL considering soil properties, geological settings, weather conditions, and engineering activities. Fuzzy variables were used to determine the magnitude of the failure. The T–S fuzzy logic enables subjective and reasonable assessment by experts, with a combination of fuzzy variables to describe the relationships among events. Finally, two recent expansive soil landslides in China were investigated, and T–S FFTA was employed to identify their failure modes and critical risk factors.

In earthquake engineering, recorded accelerograms must be critically analyzed to predict the seismic behavior of structures under future earthquakes. Recent studies have highlighted that, in addition to amplitude, the spectral content of accelerograms also plays a key role in the seismic analysis of structural systems. Spectral content varies significantly from one accelerogram to another, even for records in the same soil category. It follows that in order to account for uncertainty about future seismic events, it is necessary to analyze an ensemble of recorded accelerograms and perform statistical calculations. To this aim, in this study, the spectral content of a large set of accelerograms recorded on rigid soil deposits was analyzed. Then, ground motion acceleration was modeled as a zero-mean stationary Gaussian random process, and statistical analysis was performed to characterize the main parameters of the pertinent power spectral density (PSD) function. Although the Chauvenet’s criterion was applied to discard outliers from the set of accelerograms, it was found that the main parameters of the power spectrum would be more appropriately defined in interval form. Therefore, seismic acceleration was characterized by an imprecise PSD function, which, unlike well-established power spectra with deterministic parameters, may be viewed as representative of the set of actual accelerograms recorded on rigid soil deposits. Finally, to assess structural safety, the imprecise PSD function of ground motion acceleration was embedded into the formulation of the classical first-passage problem. Due to the imprecision of the excitations, the reliability function of the selected extreme value stress process turned out to have an interval nature.

The capacity deterioration of structures over time may impair their seismic structural reliability significantly, and, thus, they should be taken into account when assessing their seismic performance. In this study, a closed-form solution was proposed for time-dependent seismic reliability analysis of deteriorating structures. The capacity deterioration on the temporal scale was captured by the generalized capacity and deterioration function, and the Fréchet distribution was used to describe the probabilistic behavior of the peak ground acceleration. The deterioration of structural seismic capacity was modeled by a power function of time, and, thus, it was a nonlinear process if the exponent was other than 1. For a representative case, with some minor assumptions about the probability models of the deterioration process and of the seismic hazard, the proposed reliability analysis method was further simplified without the computation of integrals, which is beneficial for use in practical engineering. A numerical example was presented to demonstrate the accuracy and applicability of the proposed reliability analysis method. Future works will include the extension of the capacity deterioration model to more general forms for use in seismic structural reliability assessment through benchmark studies of real-world aging structures.

The risk-based approach is a powerful tool to assess the impact of hazards on bridge networks. The results of risk analysis of a bridge network can be integrated into an optimization process to obtain the optimal maintenance strategy for the network under investigation. In the process of risk analysis, the time-variant probability of failure profile of each bridge in the network can be obtained through reliability analysis, while network analysis needs to be carried out to determine the failure consequences of each bridge. The deterministic user equilibrium approach, which assumes that each driver has the perfect information on the traffic status and will adopt the path that maximizes his/her own benefit, is widely adopted to calculate traffic flows on each link in the network. Given that the assumption behind the deterministic user equilibrium can be highly unrealistic, this paper adopts a stochastic user equilibrium approach to analyze the traffic flow on each link, thereby producing a more accurate estimation of failure consequences associated with bridge failure. The failure consequences associated with both deterministic user equilibrium and stochastic user equilibrium are used in the optimization process to determine the influence of user equilibrium calculation on the optimal maintenance strategy for a bridge network subjected to corrosion. In addition, A709-50CR, a corrosion-resistant steel, is adopted as a material for the new girders to replace the corroded carbon steel girders. Comparison is made on the optimal maintenance strategies associated with using A709-50CR and carbon steel for replacement. The results show that the user equilibrium calculation has a profound influence on the optimization results. In addition, A709-50CR is economically beneficial at achieving low life-cycle risk compared with carbon steel.

Stochastic response analysis of nonlinear high-dimensional systems subjected to random excitations is a challenging issue in various engineering fields. The newly developed globally-evolution–based generalized probability evolution equation (GE-GDEE), as a partial differential equation governing the joint probability density of quantities of interest, rather than that of the whole state vector as in the Fokker-Planck-Kolmogorov (FPK) equation, provided a powerful tool to obtain probability density of responses of nonlinear high-dimensional systems. The dimension of GE-GDEE can be reduced with great flexibility, mostly to one or two in engineering applications. In this method, the equivalent drift coefficient serves as the ensemble driving force of probability densities, and thus its construction is critical to the accuracy of the solution. For this purpose, in the present paper, the exact stationary equivalent drift coefficient of the GE-GDEE is derived for a class of additive white-noise excited nonlinear multi-degree-of-freedom (MDOF) systems that achieve energy equipartition in their stationary stage. Based on the deduced explicit expression, a physical interpretation is provided to intuitively reveal the relationship between the equivalent drift coefficient and the inherent physical properties of systems. This leads to the physically driven approach for the determination of equivalent drift coefficient by imposing corresponding constraints on the quantities of interest. The proposed approach is verified by applying the GE-GDEE to the stochastic response analysis of two nonlinear MDOF systems excited by Gaussian white noise. The numerical results demonstrate that utilizing pertinent physical properties of a dynamical system yields a good estimation of the equivalent drift coefficient. In contrast, the method of regression based solely on sample data causes larger errors in assessing the equivalent drift coefficient and the probability density functions of the system responses. Also, the resulting probability density functions (PDFs) verify the high accuracy of the GE-GDEE provided the equivalent drift coefficient is constructed with high accuracy.

Coastal resilience, risk analysis, disaster recovery, and hazard mitigation are all vital concepts to the functioning of coastal regions. This research aimed to adapt the most recent recovery and resilience functions developed for residential homes by considering measurable factors that are known to influence recovery, including available resources and preparedness. The loss function developed for residential homes damaged by hurricanes was adapted to account for multiple hazards. The resilience model developed through this study investigated resiliency of individual residential homes across several communities. The model was validated using the observed damage from Hurricane Sandy in 2012 in the study region. The results of this study provide improved hurricane loss, recovery, and resilience functions for more accurate use with individual residential structures. Thus, this research provides an essential, innovative contribution to the quantification of infrastructure resilience at the residential level.

Currently, dilemma and option zones’ failures are independently used to analyze the intergreen interval at signalized intersections. Therefore, the present research work was initiated to integrate these failures. First, the dilemma and option zones were modeled using the first-order second-moment method. Then, game theory was used to model the association between the dilemma and option failures. The failure probabilities of the dilemma and option zones were evaluated for various traffic conditions using Monte-Carlo simulation considering the Nash equilibrium. Next, the overall system probability was analyzed, based on the combined dilemma and option failures, given different intergreen intervals, speeds, and coefficients of variation. Finally, the study proposed a methodology for identifying the intergreen interval to limit system failure. This would aid practitioners in designing traffic lights at intersections and keeping proper intergreen intervals to limit the dilemma and option failures.

Extreme weather events with an increased frequency have caused widespread damages to overhead power distribution systems (OPDS), an essential lifeline infrastructure, resulting in enormous societal and economic losses for communities. Cascading failure is a critical issue within OPDSs and starts with the failure of a system component, such as a pole, leading to large power outages. Therefore, the resilient assessment of OPDSs under extreme weather events could help evaluate system vulnerability and the performance of recovery strategies. Traditionally, the assessment is performed using an unweighted network based on topology, but this lacks the inclusion of OPDS structural properties. Therefore, a resilient assessment framework for OPDS subject to hurricane winds is proposed in this study with the integration of structural properties to consider the impact of system safety. Instead of the traditional unweighted network, a fragility-weighted topological network is formed to evaluate the performance of an OPDS against cascading failure. The system is found to be more vulnerable under an intentional failure scenario based on a comparison of performance under different attack scenarios. In addition, the impact of electricity load redistribution within the system can be obtained by performing dynamic analysis. Finally, different restoration strategies are included in the framework for comparison. Postdisruption restoration plans can be optimized in terms of recovery speed to benefit utility managers and decision makers from the improved resilience.

A generic data-driven approach is presented that employs machine learning to predict the future reliability of components in utility networks. The proposed approach enables utilities to implement a predictive maintenance strategy that optimizes life-cycle costs without compromising safety or creating environmental issues. Any machine learning technique that qualifies as a probabilistic classifier can be employed within the proposed approach. To identify the data-driven model that performs best, a practical metric to assess the performance of the competing models is proposed. This metric is specifically designed to quantify the forecasting performance with respect to maintenance planning. Additionally, a data-driven sensitivity analysis approach is discussed that allows for an assessment of the influence of the different features on the model prediction. Through an application example, it is demonstrated how the proposed approach can be applied to predict future defect rates of pipe sections for maintenance planning in a large gas distribution network.

For hybrid reliability analysis under random and multi-super-ellipsoidal variables (HRA-RM) with small failure probability, a combination of kriging and subset simulation importance sampling (SSIS) was proposed in this paper. Firstly, to quantify epistemic uncertainties more accurately, the super-ellipsoidal model was used to replace interval/ellipsoid ones. Besides, the real performance function was replaced by a kriging metamodel, which can be updated sequentially by selecting candidate samples from the first and last levels of SSIS. Due to the differences between hybrid reliability analysis (HRA) and probability reliability analysis, an expected modified risk function was adopted to obtain the next updated point. Two varying convergence conditions corresponding to the first and last levels of SSIS were employed in this paper to further improve the efficiency. Under the final kriging metamodel, the maximum failure probability of HRA-RM with small failure probability was calculated by the samples in all levels of SSIS. Finally, four validation examples were applied to demonstrate the accuracy and efficiency of the proposed method.

Production scheduling is one of the most substantial indicators in achieving short-term, medium-term, and long-term production planning goals. Satisfying this indicator can remarkably reduce total costs and enhance profitability. The haulage system significantly affects the mine production scheduling. Indeed, the uncertain nature of the haulage system causes considerable differences between the nominal and actual haulage production capacity. Thus, it is necessary to predict the haulage fleet size considering various variables to maintain production scheduling and reach nominal production capacity. Although the haulage fleet size plays a crucial role in satisfying medium-term production planning goals, it is often determined under steady-state conditions without quantifying and analyzing uncertain variables such as reliability, availability, and maintainability (RAM). In this study, a simulation optimization algorithm is developed to determine the most optimal fleet requirements considering influential factors such as RAM analysis, production scheduling, material flow rate, and stochastic environmental and operational phenomena. The proposed methodology addresses the haulage fleet size at one stage by developing a parallel combination of mixed-integer programming and discrete-event simulation. This approach has been validated using a haulage fleet operation at the Sungun mine. In this case, the analysis procedure is accomplished by determining the optimal haulage fleet size with and without considering RAM during the mining operation. Then, all mining zones are assessed based on the number of failures and loss of production capacity. During 1,500 operation hours, the lost production capacity in Mining Zones 1, 2, and 3 were 346,000, 319,000, and 481,900 t, respectively. Also, a sensitivity analysis is conducted to identify the impact of the failure and downtime of each subsystem of the haul trucks on the haulage production capacity. Thus, the engine subsystem was the most critical truck’s subsystem. The findings showed the desired performance of the proposed methodology.

Floods account for the highest annual average losses from natural hazards across the United States, and the occurrence of repeat flood inundation events in United States communities is increasing. Distinguishing damages caused by distinct flood events in a community that has experienced repeated flooding is difficult, and best practices for repeat flood metrology are needed to better inform and validate flood damage models. This paper presents a longitudinal methodology for measuring impacts from repeated flood inundation through a case study of buildings in Lumberton, North Carolina, where major flood events occurred in 2016 and 2018. Sources of uncertainty encountered in flood damage assessments are presented to inform best practices for future investigations of repeat flood events. A novel initial state parameter is introduced for accurate damage characterization for a repeat flood event. This paper presents the first analysis of statistical distributions of damage conditioned on flood depth for a set of buildings that have been flooded in two consecutive events, and the results show how floods with similar intensities occurring in the same area at two different times can exhibit differing distributions. Flood damage data sets for the two floods are combined to derive flood damage fragilities, and we propose the creation of a flood damage database by aggregating data from various flood events across the United States to enable more robust fragility functions that can be applied across geographies and flood events.