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

Spatial variability is one of the major sources of uncertainty in geotechnical applications. This variability is characterized customarily by the scale of fluctuation. Scale of fluctuation describes the distance over which the parameters of a soil or rock are similar or correlated. The scale of fluctuation is required in order to best characterize and to simulate a spatially variable field. This paper first provides an overview of the various methods available for estimating the scale of fluctuation from cone penetration test (CPT) data, along with two examples for comparing the methods. The first part reveals some issues with two popular estimation methods, namely the method of moments and the maximum-likelihood method (MLE). The method of moments is less sensitive to the choice of the autocorrelation function (ACF), but it could be less precise and may be based on a correlation estimator that does not produce a positive definite autocorrelation matrix. MLE can be very sensitive to the choice of the classical one-parameter ACF. It is not uncommon to assume such an ACF, rather than to identify the ACF from actual soil data with a more general two-parameter Whittle–Matérn (WM) model. This practice may not be robust. Nonetheless, a literature survey is useful if these caveats are kept in mind. The second part of this paper provides a database table of horizontal and vertical scale of fluctuation values in different locations and for different materials, collected from published case studies, which can be used as a reference when field data are not readily available. The probable range of values as a function of soil type is provided to inform sensitivity analysis and to guide the selection of a prior distribution for Bayesian analysis.

High-performance control systems (HPCS) are sophisticated vibration-mitigation devices that include active, semiactive, and hybrid systems. HPCS leverage feedback mechanisms to dynamically adjust the damping force in response to motion, offering a good mitigation performance over a wide excitation bandwidth. These damping systems are attractive for multihazard mitigation of civil structures. However, the application of HPCS necessitates the design and integration of a closed-loop control system that can be susceptible to local failure or reduction in performance in the form of, for example, malfunctions of HPCS components, noisy measurements, wear, aging, and power unavailability. It follows that these sources of uncertainties may cause concerns and impede the wide acceptability and deployment of HPCS. A solution is to enhance the robustness of the closed-loop configuration through, for example, added redundancies and/or more robust, yet more expensive, components. In this paper, a Kriging-based design procedure is presented enabling the robust design of closed-loop control systems. The design procedure consists of constructing a Kriging surrogate that maps uncertainties to structural performance, and using that surrogate to identify the most influential sources of uncertainties. After, these identified uncertainties are made more robust in order to decrease the variance in the HPCS performance over the lifetime of the structure. The performance of the HPCS is directly quantified through a life cycle cost analysis to obtain probability distributions on the expected financial impacts. The proposed procedure is demonstrated on a numerically simulated 39-story structure exposed to wind loads. Uncertainties in both the HPCS configuration (i.e., damping devices and sensors) and external load are considered. Results demonstrate that the proposed framework can be used to substantially improve the robustness of the closed-loop system. Also, through a comparison against a robust passive-viscous case, it is shown that robustly designed HPCS significantly outperform a passive system in terms of vibration mitigation and life cycle costs.

The value of information (VoI) framework, based on Bayesian preposterior analysis, can be used to estimate the most likely benefit associated with a particular structural health monitoring (SHM) strategy. The errors within the VoI framework can be traced to the underlying predictive models and the inspection instruments. Conventional VoI analysis assumes a nonerroneous predictive model. Also, it considers only the (unbiased) random errors associated with inspection instruments. In this paper, the authors propose a VoI framework that explicitly considers the different uncertain errors within the predictive models and inspection instruments. Global sensitivity analysis and parametric investigations are performed to study the sensitivity of the VoI framework to various error parameters by estimating Sobol’ indices through Monte Carlo simulations and polynomial chaos expansions. It is found that the VoI framework is highly sensitive to the errors within the predictive model. This study recommends that any VoI analysis should be preceded with a thorough quantification of the errors within the predictive models lest an inaccurate estimate of the VoI is obtained.

There is global interest by transportation planners and enterprise operators to monitor the inherent uncertainty of transportation networks. Traditional performance metrics may erroneously prioritize project initiatives based on disruptions measured from ideal driving speeds; however, commuters and enterprise operators have demonstrated the ability to accommodate recurrent highway congestion by adjusting departure times, transportation modes, origins, or destinations in logistics planning. Recent performance metrics of transportation network reliability have demonstrated the importance of measuring disruptions from normal operating conditions. Using disaggregate speed data, typical conditions are assessed by mean and median speeds across disparate hours of the day and days of the week. In this paper, we establish a quantitative multicriteria framework for measuring operational disruptions based on the intensity and duration of observed deviations from normal conditions. Advances in data collection provide the disaggregated data that can be used to identify when disruptions occur and the extent of affected volume. This approach influences the prioritization of infrastructure improvements based on deviations from typical conditions and informs appropriate mitigation strategies based on the category and time of disruption. A demonstration of the approach to a geographically diverse region is provided, with implications for several agency-planning horizons.

Despite the importance of force-controlled components, the implications of their failure to the overall performance of controlled rocking steel braced frames (CRSBFs) have not been examined in prior studies. This paper presents a reliability-based methodology for quantifying the relationship between the response of CRSBF force-controlled components (frame beams, columns, and braces) and system-level performance. Structural response simulation of nonlinear models was used to investigate the relationship between the behavior of CRSBF force-controlled components and system-level performance metrics. Vulnerability- and risk-based assessments of the collapse and unsafe placard trigger (UPT) limit states were performed with and without considering the possibility of force-controlled component failure, using varied ratios of the resistance to the load factors $\varphi /\gamma$ used in the design of CRSBFs. The results of a case study using three-, six-, and nine-story structures showed that force-controlled component failure had a larger influence on collapse safety compared to unsafe placard assignment. For both limit states, the effect of force-controlled component behavior was lesser for taller buildings.

This paper efficiently determined exact stochastic responses of orthotropic rectangular Kirchhoff plates under stationary and nonstationary random excitations in a semianalytical manner. The transcendental frequency equations of orthotropic plates were solved exactly for the high-order natural frequencies. The power spectral density functions of elastic stochastic responses were derived analytically by means of the pseudoexcitation method. The semianalytical method (SAM) is proposed to ensure the accuracy of differential and integral operations in the space domain and to enhance the computational efficiency of random vibration analysis. The exact stochastic responses of the plates, especially the distribution of stochastic von Mises stress, to various stationary, temporal and spectral nonstationary random excitations were achieved, and the higher efficiency of SAM than of the analytical method, and its higher accuracy than that of Monte Carlo simulation, were illustrated. The remarkable effects of boundary conditions, material, and loading cases on uncertainty propagation of the orthotropic plate were determined. The obtained exact stochastic responses of orthotropic plate can be taken as the benchmark to verify other numerical methods.

Performance-based design (PBD) is becoming a benchmark approach for design and performance assessment of civil structures because it facilitates communication between stakeholders and design professionals with decision metrics such as economic losses, downtime, and probabilistic cost functions. However, to reliably estimate the response of a building, it is necessary to consider uncertainties in structural parameters and external load characteristics, which makes the probabilistic assessment phase of PBD time consuming and often impractical. In wind-excited tall buildings, this aspect is combined with the long duration of typical wind events (in the order of hours) and the time required to conduct a single numerical simulation, further increasing the computational demand of the uncertainty analysis. As a solution, this paper presents a novel multiple-surrogate models framework for the probabilistic assessment of wind-excited tall buildings. In the proposed framework, the structural system is decomposed into a set of subsystems and multiple-surrogate models are used to map the uncertain building dynamic properties and wind load characteristics to its structural response, expressed in terms of peak floor acceleration and maximum story drift ratio. The proposed approach is demonstrated on a 40-story building, located in Boston, Massachusetts, and exposed to synthetic wind load time histories. The accuracy of the multiple-surrogate models to estimate the structural response of the building is first evaluated. Then, an application of the multiple-surrogate models to the cost assessment of the case study building is presented to demonstrate the capabilities of the proposed approach to probabilistically assess the performance of a structure and its potential integration in PBD. Results show that using the multiple-surrogate models framework represents a viable solution to handle the large dimensional probabilistic assessment of wind-excited tall buildings under uncertainties in dynamic properties and wind load characteristics.

Time-variant reliability problems have been studied widely by scholars recently, and they usually are solved on the basis of the outcrossing rate. The common method to calculate the outcrossing rate involves a static parallel system reliability problem of two components, and sequentially the time-variant reliability can be computed by Rice’s formula. This formula, however, is not ideal because of its assumptions of Poisson distribution and independence for outcrossings, and thus a significant error may be generated. Therefore, the formula for computing time-variant reliability was rederived and modified in this paper. Time-variant reliability first was derived based on the failure rate, and then the approximate relationship between the outcrossing rate and failure rate was provided. Consequently, Rice’s formula for calculating time-variant reliability based on outcrossing rate can be modified from the perspective of failure rate, which can remove the drawbacks of the assumptions of Rice’s formula. Several examples were provided to validate the efficiency and accuracy of the proposed method.

Tunneling-induced settlements in soft soil are inevitable and often suspected to impair buildings along the alignment. Public focus is thus targeted toward the consequences rather than the benefits of tunneling projects. To reduce the risk of damage, costly compensation is frequently applied in practice when crossing below critical structures, and critical scenarios are usually assessed via deterministic analyses of analytic models. These models are well known and overestimate potential damage since they conceive of buildings as idealized, simple Timoshenko beams without considering soil–structure interaction. The present study introduces an approach to providing an efficient prediction of realistically idealized structures prone to settlement-induced damages based on random input. This approach uses a nonlinear finite-element simulation of façades to render damage assessment. Global sensitivity analysis that uses both elementary effects and Sobol indices identifies random but irrelevant input that is consequently fixed to mean values. Response surfaces yield surrogate models for subsequent damage prediction via polynomial regression. As input, these surfaces employ ordinary distribution functions and extreme value distributions to ensure predictability at the tails. The inherent error of any prediction with response surfaces is first reduced by cross-validation and then comparatively rated with alternative forecasts through artificial neural networks. Finally, the mean probabilities of occurrence and standard deviations are determined considering the imperfection of the surrogate models for the classified damage events.

Infrastructure resilience is defined as the ability of a system to withstand and recover from the effects of natural or man-made hazards. For any community, quantifying its sociophysical infrastructure resilience during and after any disruptive event is important for planners, designers, and decision-makers. However, a global approach for resilience quantification becomes challenging due to the fact that infrastructure systems’ performance varies from location to location and the recovery process is also complex and region-specific. In this work, an integrated Geographic Information System (GIS)-Bayesian Belief Network (BBN) framework is developed to model and quantify the resilience (vulnerability and recovery) of network infrastructure systems against flood hazards. To this end, a simple case study is demonstrated for quantifying flood resilience of a roadway network in a community in northeast India. Data collection is done using a GIS platform and a probabilistic graphical model (BBN model) is used to model uncertainties in resilience quantification based on the available data and judgments. The main contributions of the proposed resilience model are: (1) the model can provide more accurate and realistic estimates based on beliefs; (2) the model can be updated as and when more data is available; and (3) sensitivity analysis of the validated road network resilience model to facilitate risk-informed decision-making against future flood disaster.

Structural reliability is a widely used tool to estimate the safety level and remaining service life of civil structures and infrastructure systems. Using a stochastic process to model the resistance or structural response, the structural failure can be treated as the probability of the structural response exceeding the resistance (upcrossing). It has been widely assumed that the occurrence of upcrossings is independent, and thus a Poisson process can be used to describe the sequence of upcrossings. This may lead to significant error in the estimate of structural reliability. This paper presents a new method for structural reliability assessment, where the correlation between upcrossings is taken into account. A closed-form solution is derived for structural reliability, which is beneficial for its application in practical engineering. An illustrative example is presented to demonstrate the applicability of the proposed approach.