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

The quest to answer the longstanding question of how safe is safe enough has led to an evolution of design philosophies. The traditional philosophy has been based on ensuring that system capacity exceeds demand. This philosophy has been manifested in such design methods as allowable stress design (ASD) and load and resistance factor design (LRFD). The level of safety in these methods is commonly calibrated to the acceptable practice using structural reliability methods. Such an approach to design aims to balance life safety and construction costs and works relatively well for service loads under which strength is nearly always a sufficient representative of system capacity. Under extreme loads, however, not only do factors such as ductility or energy-dissipation capacity come into play, but also the consequences of failure are vastly larger and may go beyond the failure site. For this reason, the more modern design philosophy of performance-based design emerged. According to this philosophy, a system is designed to meet a target performance, defined by limits on its performance metrics. These metrics were initially defined on structural responses, such as deformations and accelerations, and later evolved into risk measures based on monetary loss, downtime, and casualties. This philosophy is gradually making its way into modern design codes. Typical performance-based criteria focus primarily on what transpires during extreme loads. The need to incorporate the recovery of the system after such events has prompted the design philosophy to make another leap into resilience-based design (RBD). Resilience is defined herein as the ability to recover, within a predetermined period of time, in the aftermath of extreme events. Resilience transcends risk, i.e., a resilience analysis incorporates not only a risk analysis but also a recovery analysis. This paper provides a review of RBD in continuation of PBD and further introduces various applications of RBD into the design and assessment of civil infrastructure. The review covers the state of the art in resilience quantification approaches and associated computational platforms.

The extremely complex loading conditions on the clutch of a four-wheeled passenger vehicle frequently results in malfunction of the motor. The latest diagnostic methods for detecting the initiation of device failure have proven to be unreliable. The present research has been carried out to demonstrate the state of health of the motor on the basis of a nonlinear real time estimation approach. In order to fulfil this task, a systematic review was undertaken of the unscented particle filter (UPF) approach to handle the evolved noisy signal with in real time. Research facilitates the modeling of nonlinear behavior of elements via state-space equations embedded with a set of available real time measurements. The remaining useful life (RUL) of the motor (system) as a distribution function is estimated. The study highlights that the state space framework provides better results than the degradation modeling scheme to forecast the RUL of the system.

Following an investigation into the causes of a 40% difference between estimates of structural response to wind by two reputable wind engineering laboratories, the National Institute of Standards and Technology recommended the development of nationally accepted performance standards based on sound technical methods. The development of such standards requires the use of an uncertainty quantification procedure applicable to the response of structures subjected to wind loads. As part of this development, this paper considers the effect of uncertainty in the subjective determinations of the surface roughness lengths at sites with open and suburban exposures. Extensive data developed jointly by the University of Florida, Applied Research Associates, and the National Oceanic and Atmospheric Administration (NOAA)/National Hurricane Center were used in conjunction with analytic and probabilistic tools to quantify the effect of this uncertainty on the estimation of structural response to wind. It was found that neglecting this uncertainty can result in underestimation of the response by as much as 45%. It is recommended that techniques for the objective measurement of surface roughness lengths be used for structural design purposes and that, in the absence of such measurements, the effect of uncertainty in the determination of surface roughness be accounted for in structural design.

The risk assessment of tunneling-induced damage in buildings is a challenging task in geotechnical and structural engineering. It is important to consider the soil–structure interaction during the tunnel construction process. In this paper, finite-element (FE) simulation models of mechanized tunneling processes are combined with FE models of buildings to predict tunneling-induced damage. The soil–structure interaction is taken into account by considering the building stiffness in the tunneling process simulation model and by applying the computed foundation settlements as boundary conditions of the building model. The building damage risk is assessed by means of strains in the structural members and a corresponding category of damage is determined. Uncertainties of the geotechnical parameters and the structural parameters are quantified as random variables and intervals in the framework of polymorphic uncertainty modeling. For real-time predictions, the FE simulation models are approximated by artificial neural networks. This makes it possible to predict the structural damage risk according to scenarios of the operational tunneling process parameters in order to assist machine drivers during tunnel construction.

After the Wenchuan earthquake, frequent debris-flow disasters caused catastrophic damage to the infrastructures and inhabitants in the downstream. It is very important to properly determine the runout distances of debris flows to delineate potential hazard areas for the postearthquake mitigation work design and effective risk management. However, this is a difficult task due to the high variability and uncertainty of debris flows. This paper developed copula-based probabilistic approaches for predicting the runout distances of debris flows on depositional fans. The proposed approaches integrate the debris flow’s maximum runout distance ($L$), debris-flow volume ($V_D$), and catchment internal relief ($H$) into a three-dimensional copula-based probabilistic model. A probabilistic model of debris flows in the Wenchuan earthquake zone was developed based on the field investigation data of 133 channelized debris-flow events. The developed model is able to predict the possible maximum runout distances for a specific hazard level. The proposed approaches were validated using an independent field investigation dataset of debris flows. Results show that the proposed approaches properly estimate the maximum runout distances of debris flows in the Wenchuan earthquake zone for a specific probability level of 0.88.

In the last few decades, artificial ground freezing (AGF) has been used as a temporary soil stabilization and waterproofing technique in geotechnical engineering, especially in tunneling construction. In construction, drilling inaccuracy of freeze pipes occurs, slowing the formation of a connected frozen wall and in turn undermining the process of AGF. This study used a hydrothermal coupling finite-element model to investigate the heat transfer and formation of a frozen wall during the progress of AGF. The simulation results indicate that the drilling inaccuracy causes temperature fluctuation at the temperature monitoring point and prolongs the formation time of the frozen wall. The maximum drilling inaccuracy allowed in practice is recommended to be 0.2 m based on the model (established using the shield tunneling of Nanjing Subway Line 10). The low-temperature zone develops unevenly and finally forms a complete frozen wall with an elliptical ring in the presence of the seepage flow. Thus, the location of the temperature monitoring points needs to be reconsidered, rather than being distributed evenly around the perimeter of the tunnel in practical engineering, and more monitoring points should be placed to monitor the formation of the frozen wall.

This study identifies and classifies the root causes (i.e., the primary precursors) of cost overruns in oil and gas construction projects. Past studies generally focused on identifying the most immediate precursors of cost overruns and failed to address the need to identify their root causes. Specific root causes are inherent in projects with certain characteristics (e.g., projects in a particular domain or sector). Accordingly, research is needed to identify various cost overrun factors and their root causes in a context-specific manner. This study used content analysis to identify 38 recurrent cost overrun factors and 11 root causes in oil and gas construction projects. The cost overrun factors were classified based on their common root causes. The Delphi method was used to verify this classification. The evidence from 12 construction projects in the oil and gas sector that had faced cost overruns was used to verify the outcome of this study. The findings of the study will assist practitioners in mitigating the risk of cost overruns in oil and gas projects and meeting their budget goals.

Due to the influence of various uncertain factors, there will inevitably be certain errors between the prediction of finite-element (FE) model and observed data for a target structure. It is thus necessary to calibrate the initial FE model using the measured data to ensure the accuracy of the numerical model for the purpose of structural system identification and health monitoring. Although structural FE model updating methods have been extensively studied in the past few decades, the research based on deterministic methods in the current literature still occupies a large proportion, which cannot account for the uncertain effects during the model updating. The noise robustness of both the updating procedure and the generalization capability of the updated model are expected to be poor. The model updating based on the Bayesian theorem can quantify the uncertainty of model identification results, but it is computationally expensive for the Bayesian inference of regularization hyperparameters since the Hessian matrix is generally required to be evaluated repeatedly especially for a huge amount of uncertain model parameters. Also, effective prediction based on the refined FE model is still lacking in the literature, which is essential for judging and evaluating the quality of the updated model. This paper proposes a practical framework for structural FE model updating and prediction based on the Bayesian regularization with incomplete modal data. The structural model parameters and regularization hyperparameters are identified alternatively in an adaptive manner, and the Gauss-Newton method is used to approximate the true Hessian within the framework of the nonlinear least-squares algorithm. This is expected to improve the efficiency and robustness of model updating and prediction for handling large-scale FE models possessing a large number of uncertain model parameters. The proposed methodology is validated through the model updating and prediction conducted on a real-life pedestrian bridge based on field-testing data.

Multivariate normal distribution is used widely to characterize the uncertainties and correlations for correlated geotechnical data. The success of constructing a multivariate normal distribution depends on the reliable estimation of the marginal probability density functions (PDFs) and the correlation matrix. This paper focused on the normal transformation which is related to the fitted marginal PDFs and investigated its effect on the performance of the constructed multivariate normal distributions, i.e., the normality of the multivariate normal distribution, the fitness of the simulated data with the original data, the rationality of the derived point estimate equations, and validation of the equations based on validation data sets. Three normal transformation methods with different types of fitted marginal PDF, namely Johnson transformation, three-parameter lognormal transformation, and Box–Cox transformation, were compared based on their application to a real soil database. It was found that all the three normal transformation methods are applicable in the framework of multivariate normal distribution, although the transformed variables do not follow the multivariate normal distribution. The consistence of normality of the transformed variables with the performance of the constructed multivariate normal distribution in estimating the unknown parameters using Bayesian updating technique was verified. The Johnson transformation method is the recommended method for constructing the multivariate normal distribution for the real databases due to its robustness and superiority in normal transformation.

It is not uncommon to encounter engineering structures that can be modeled as series systems with a large, even unlimited, number of correlated components (e.g., component number greater than 1,000). It can be a significant challenge to estimate the failure probability of a large-series system, at least partially, due to difficulties in quantifying correlation among components, nonlinear system responses, and high dimensionality of uncertain parameters. Direct Monte Carlo simulation (MCS) is a straightforward method to address these difficulties, but it may need prohibitive computational costs if the component number is large and the system failure event is rare. This study proposes an efficient method called adaptive MCS for reliability analysis of series systems with a large number of components. The proposed approach is a variant of direct MCS, and it estimates the system failure probability by iteratively identifying failure samples from all direct MCS random samples without the need to explore them exclusively. The performance (including convergence and computational efficiency) of the proposed method was systematically explored using a number of series system examples under various settings (e.g., component number, correlation among components, and component reliability). It is shown that the results from adaptive MCS converge to estimates from direct MCS with much lower computation effort. The proposed approach is applicable to series systems with a large number of components (say greater than 1,000) and assumes that different components are correlated to a certain degree so that they may share some failure samples. As the component number and the correlation coefficient among components increase, the improvement of computational efficiency by the adaptive MCS becomes more prominent, leading to significant computational savings in comparison with direct MCS. Finally, the proposed method was applied to efficiently evaluating the system failure probability of a two-layer slope with a large number of potential slip surfaces (i.e., components).

The present study investigates the effect of spatial variability of soil parameters on the probability of failure of strip footings against bearing pressure resting on a homogeneous and two-layered soil system. The uncertain parameters (soil properties) are considered as fuzzy variables described by a triangular membership function. To incorporate spatial variability in reliability analysis, both isotropic and anisotropic scales of fluctuation are considered in the numerical simulation. Several design charts (in terms of failure probability) are presented for soil deposits having different friction angles under both isotropic and anisotropic field conditions. Variation of the probability of failure with different coefficients of variation (COVs) of soil internal friction angle is also examined. Scales of fluctuation or spatial correlation lengths are also varied within a wide range to identify the effect on the probability of failure. For a homogeneous soil stratum, results reveal that the difference in the failure probability can increase up to 53% for the variation of COV of soil friction angle and scale of fluctuation. It was also found that the effect of spatial correlation length is significant up to a certain distance which is a function of width of the footing, but beyond that, the effect is negligible.

Railways are vital infrastructures whose design is complex and time-consuming. In addition to multiple conflicting objectives and highly-constrained search spaces, their design also faces great uncertainties. The aim of this study is to optimize railway alignments considering decision-makers’ preference uncertainty for multiple objectives, which can influence the alignment determination macroscopically and fundamentally. First, a multiobjective model is built by integrating costs (including construction and operation costs) and seismic risks (including direct and indirect losses) for mountain railway optimization. To solve this model, a particle swarm algorithm is improved by incorporating a multicriteria tournament decision (MTD). Then, a robust optimization MTD (RO-MTD) method is developed to find cost-risk tradeoffs by addressing the uncertainty of decision-makers’ preferences. The major steps of the RO-MTD include (1) treating uncertain preferences as variables, (2) sampling the uncertain space of preferences, (3) analyzing all possible preference scenarios, and (4) integrating those analyses to achieve a robust evaluation. Finally, the preceding approaches are applied to a complicated real-world case. By comparing the RO-MTD and MTD as well as the computer-generated alignment and the best manually-designed one, the effectiveness of the proposed method is confirmed.

This paper introduces a method for the optimal generation of multivariate intensity measure (IM) maps representing various aspects of the intensity of an earthquake over a region that can be used as input for regional hazard and loss-estimation analyses. The proposed method is an extension of the single-variate hazard quantization (HQ) methodology for the effective sampling of IM maps with a single intensity measure. The use of multiple intensity measures to describe an earthquake enables a more comprehensive representation of the event, with richer information. For instance, to conduct accurate regional seismic loss analyses of a portfolio of structures with different fundamental periods, maps of spectral accelerations at different periods (multivariate IM maps) are necessary. With this in view, the multivariate HQ method is proposed for the effective generation of a relatively small set of multivariate IM maps that can capture the seismicity of a region. A case study of the seismic region of Charleston, South Carolina, is presented. The sample space is represented by a large set of pairs of IM maps representing the spectral acceleration at two different periods, with each pair representing a specific earthquake event. The simulated pairs of IM maps carry the stochastic characteristics of the associated seismic events, such as the spatial correlation of the ground motions and the cross correlation between different intensities at different sites in the region. The ability of the proposed method to provide an accurate estimation of the hazard curve for both intensity measures and to correctly capture the spatial autocorrelations and the spatial cross correlation among the two intensity measures was investigated quantitatively. Experiments were conducted to demonstrate the robustness of the method and the effect of the sample size on the performance of the method.

During the expansion of metro subway projects, numerous safety hazards occur due to geotechnical site conditions, the complexity of the construction work, the concealment of the project, and the lengthy construction period. This study aims to identify the factors contributing to safety hazards and develop a risk evaluation system to determine the safety level in metro subway projects. A questionnaire survey administered to 224 participants working in different metro subway projects was used to collect the data. A second-order confirmatory model of hazard indexes was developed using structural equation modeling (SEM). A case study of an ongoing metro subway project was used to apply the developed risk evaluation system. Based on the developed model, the studied metro project scored 89.73, indicating the hazards level in this project is relatively controllable. The risk evaluation system developed in this study is useful to measure the safety level in metro construction projects to enable project managers and other stakeholders to determine the safety areas that need development and prevent potential hazards during the construction of this type of project.

Quantitative resilience assessment is an essential to the lifetime management of civil structures. With the uncertainties arising from both physical and socioeconomic dimensions, resilience needs to be measured probabilistically. Although existing resilience frameworks have addressed this need, none statistically characterize how much a system’s resilience migrates and in what direction it migrates. This paper proposes a lifetime resilience migration quantification (LRMQ) framework based on nonparametric distance metrics. In this framework, system resilience positioning is proposed by comparing the resilience of a variable system against two reference systems. Two decision-making analytics, a binary resilience classification (BRC) diagram and a lifetime resilience attenuation (LRA) model, are proposed. The proposed method is evaluated with success by performing numerical experimentation over a representative river-crossing bridge. It is observed that the bridge’s system resilience attenuates progressively in its lifetime; with the consideration of a scour countermeasure, resilience is effectively enhanced via visualizing the proposed BRC diagram and LRA model.

Relatively little research has been conducted to systematically quantify the nationwide earthquake risk of gas pipelines in the US; simultaneously, national guidance is limited for operators across the country to consistently evaluate the earthquake risk of their assets. Furthermore, many challenges and uncertainties exist in a comprehensive seismic risk assessment of gas pipelines. As a first stage in a systematic nationwide assessment, we quantify the earthquake risk of gas transmission pipelines in the conterminous US due to strong ground shaking, including the associated uncertainties. Specifically, we integrate the US Geological Survey 2018 National Seismic Hazard Model, a logic tree–based exposure model, three different vulnerability models, and a consequence model. The results enable comparison against other risk assessment efforts, encourage more transparent deliberation regarding alternative approaches, and facilitate decisions on potentially assessing localized risks due to ground failures that require site-specific data. Based on the uncertainties approximated herein, the resulting sensitivity analyses suggest that the vulnerability model is the most influential source of uncertainty. Finally, we highlight research needs such as (1) developing more vulnerability models for regional seismic risk assessment of gas pipelines; (2) identifying, prioritizing, and measuring input pipeline attributes that are important for estimating seismic damage; and (3) better quantifying seismic hazards with their uncertainties at the national scale, for both ground failures and ground shaking.

Modal properties are recognized as indicators reflecting structural condition in structural health monitoring (SHM). However, changing environmental and operational variables (EOVs) cause variability in the identified modal parameters and subsequently obscure damage effects. To address the issue caused by EOV-related variability, this study investigated the variability of modal frequencies in long-term SHM of a steel plate-girder bridge. A Bayesian fast Fourier transform (FFT) method was used for operational modal analysis in a probabilistic viewpoint. Bayesian linear regression (BLR) and Gaussian process regression (GPR) models were utilized to capture the variability in the identified most probable values (MPVs) of modal frequencies as temperature-driven models, and the limitation of these models for data normalization with latent EOVs is discussed. To overcome the interference of latent EOVs indirectly, a long short-term memory (LSTM) network was established to trace the variability as an autocorrelated process, with a traditional seasonal autoregressive integrated moving average (SARIMA) model as a benchmark. Finally, an anomaly detection method based on residuals of one-step-ahead predictions by LSTM was proposed associating with the Mann-Whitney $U$-test.

The philosophy currently adopted by AASHTO Bridge Design Specifications (BDS) is based on considering uncertainties inherent in the structural design process by proposing load and resistance factors. Factors used in the load and resistance factor design (LRFD) method are calibrated using theory of reliability. Research efforts to calibrate load factors for gravity loads can be found in the literature; however, calibration of load factors for temperature loads has not received similar attention. Temperature loads are inevitable environmental loads that affect bridges on a daily and diurnal basis, causing additional stresses and deformations. The first step in conducting such a calibration is to understand the uncertainties inherent therein. This study established a methodology for determining the probability distribution of maximum daily temperature gradient in slab-on-girder concrete bridges using temperature data from the John James Audubon Bridge in south Louisiana. Field data from a finite monitoring time period of 5 years were analyzed statistically and then extrapolated to obtain the largest extreme values over the expected design life of the bridge using extreme value theory. Recorded data were used to investigate the best-fit distribution type for maximum daily temperature differences, which revealed that the beta distribution type is the best fit for maximum daily temperature data. The largest extreme maximum daily temperature difference values were determined using the peak over threshold (POT) method. Extrapolated temperature gradients, that is, the largest extreme maximum daily temperature difference values, were found to be best represented by a Gumbel distribution.

A displacement estimation method for a nonlinear single-degree-of-freedom (SDOF) system under seismic excitation is proposed based on an extended Kalman filter (EKF). This method first identifies time intervals where a system experiences significant nonlinearity. For a time period when the system is in an elastic phase, available observations for EKF are acceleration, displacement from numerical integration, and residual displacement. During a time period with significant nonlinearity, acceleration and virtual displacement measurements are employed as observations. Two EKF schemes are applied in this part. In the first scheme, displacement is estimated along with time-varying stiffness using an augmented state vector. In the second scheme, a bilinear hysteresis model with optimized system parameters is employed. The results are further smoothed by extended Kalman smoother (EKS). The proposed displacement estimation method is numerically studied on a bilinear SDOF system and applied to various hysteresis models and earthquake excitations. Data obtained in shaking table experiments on a full-scale bridge pier and a 4-story building are analyzed to validate the method. The displacements are estimated with high accuracies.

A modified general framework of preposterior analysis is proposed here, which takes the uncertainties and dependencies associated with the information relevant to structural performance into account, to provide a sound basis for decision making. The information that we collect to update our knowledge of structural performance forms the core of preposterior analysis. It normally is not perfect and dependent on each other. Special emphasis is placed on the identification of the potential sources of uncertainties and dependencies of the information samples. The proposed framework is formulated with the consistent representation of such uncertainties and dependencies. The framework’s application is illustrated through an example of the update of the knowledge about the condition state of gas turbine engine components. The potential uncertainties and dependencies associated with future samples of the information relevant to the flaws of the engine components are presented, and further, their effects on the update of knowledge of the components’ state are investigated.

This paper presents a highly efficient approach for bounding the responses and probability of failure of nonlinear models subjected to imprecisely defined stochastic Gaussian loads. Typically, such computations involve solving a nested double-loop problem, where the propagation of the aleatory uncertainty has to be performed for each realization of the epistemic parameters. Apart from near-trivial cases, such computation is generally intractable without resorting to surrogate modeling schemes, especially in the context of performing nonlinear dynamical simulations. The recently introduced operator norm framework allows for breaking this double loop by determining those values of the epistemic uncertain parameters that produce bounds on the probability of failure a priori. However, the method in its current form is only applicable to linear models due to the adopted assumptions in the derivation of the involved operator norms. In this paper, the operator norm framework is extended and generalized by resorting to the statistical linearization methodology to account for nonlinear systems. Two case studies are included to demonstrate the validity and efficiency of the proposed approach.

The usual approach to time-variant reliability problems is PHI2 method, which defines the outcrossing rate as a two-component parallel system problem and calculates it through a two-dimensional numerical integration. To improve the computational efficiency, an explicit model of outcrossing rate is proposed in this study, which is referred to as EPHI2. Based on the proposed explicit model, EPHI2 method avoids the numerical integration required in PHI2 method. Comparison between the outcrossing rates obtained by PHI2 and EPHI2 methods was conducted, and EPHI2 method was proved to have enough accuracy for calculating outcrossing rates. The application of EPHI2 method for time-variant reliability analysis was investigated through three numerical examples. It is found that the proposed EPHI2 method is more efficient than PHI2 method in time-variant reliability analysis, with the results nearly the same.

Power distribution systems are very vulnerable during hurricane events. Failure of power distribution systems could bring significant disruptions to the community’s daily activities. Sparse historical hurricane data are insufficient to establish hurricane risk models. Therefore, it has been challenging to evaluate the pole-wire system’s performance during hurricane events under strong winds. In the present study, a probabilistic framework integrating hurricane risk modeling and physics-based analysis is proposed to assess the reliability of the power distribution system subjected to hurricane winds. Based on historical hurricane data, hurricane tracks are simulated using a modified statistical method by matching the synthetic data with the statistical characteristics from historical hurricanes facilitated by a copula model. Using a novel statistical model that implements a machine learning (ML) algorithm hurricane intensities are predicted. A hurricane risk model is established using the synthetic hurricane data. Fragility curves for each pole are obtained by physics-based Monte Carlo simulations facilitated by ML-based regression models instead of the empirical fitting model in order to incorporate the most influential factors. A surrogate model trained by the ML algorithm is employed to obtain the system fragility curve with a low computational cost. Finally, the annual failure probability of the pole-wire system could be obtained by integrating the annual hurricane wind speed probability density and the pole-wire system fragility curve.

The riserless drilling system (RDS) is a dual gradient drilling system. Offshore natural gas hydrate (NGH) drilling projects are often subject to various risks due to uncertainty factors (marine environment and complex operating conditions). When encountering the NGH layer during drilling, significant risks can be generated by the decomposition and secondary generation of the NGH. In order to predict risk accidents, environmental factors and equipment factors are analyzed in this study. First, a bow-tie (BT) model is established, which is then transformed into a Bayesian network (BN) using a mapping algorithm. Secondly, uncertainty modeling is carried out in BN. The leaky noisy-OR node is added to BN and the uncertain influence of the logical relationship is considered. Then, considering the dynamic uncertainty of equipment factors, the established BN is transformed into an uncertain dynamic BN (UDBN) through the transition probability matrix. In addition, fuzzy theory and expert judgment are used to quantify the prior probability of equipment failures. Considering the effect of NGH, the risk of RDS is analyzed by the developed model, and the dynamic risk probabilities under three working conditions are obtained. The sensitivity analysis of the equipment was also carried out. The final result shows that the drilling risk probability in an NGH layer is higher than that under normal working conditions. Due to the influence of NGH, the probability of risks also becomes uncertain. After adding uncertain factors, the probability of drilling risk occurrence is reduced. The correctness of the established UDBNs model is verified by the Petri nets method.