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

In data-driven structural health monitoring (SHM), the signals recorded from systems in operation can be noisy and incomplete. Data corresponding to each of the operational, environmental, and damage states are rarely available a priori; furthermore, labeling to describe the measurements is often unavailable. In consequence, the algorithms used to implement SHM should be robust and adaptive while accommodating for missing information in the training data—such that new information can be included if it becomes available. By reviewing novel techniques for statistical learning (introduced in previous work), it is argued that probabilistic algorithms offer a natural solution to the modeling of SHM data in practice. In three case-studies, probabilistic methods are adapted for applications to SHM signals, including semisupervised learning, active learning, and multitask learning.

Access management is a set of strategies used by transportation planners to address mobility concerns without the need for major highway expansions. Although access management can significantly improve highway mobility, congestion, and safety, limited resources prevent transportation agencies from managing each access point within their network. Therefore, there is a need for a macrolevel framework that helps these agencies in prioritizing their access management needs. This research contributes to the transportation planning field by developing a two-phase methodology that prioritizes competing needs for access management across a large network of highways. The first phase, filtering, evaluates the economic efficiency of access management projects from a travel time improvement perspective. This phase extends current risk–cost–benefit analysis methods, which integrate risk analysis and cost–benefit analysis with data uncertainties. The second phase, classification, identifies a set of site-specific qualitative and quantitative factors that can affect the travel time of road users. These factors are used to set priorities for access management projects. The developed methodology was demonstrated by applying it to thousands of access points located on four major US routes. The results are of interest to transportation planners, safety managers, and state and local municipalities.

Transportation planning for highways is informed by performance metrics with aggregated data that can obfuscate the uncertainty of performance conditions across hours, days, and weeks. Recent advances in data collection methods provide disaggregated speed data at a regional level, which can be processed to inform planners and stakeholders of system performance hotspots. Based on the methods of corridor trace analysis (CTA), this paper extended the framework through temporal disaggregation of highway performance metrics, classified as a temporal corridor trace analysis, t-CTA. This approach introduces a temporal weight and temporal value associated with observed performance during discrete periods. The temporal value allows stakeholders to address uncertainty in logistics and scheduling, adjusting the significance of measured performance based on when the adverse conditions are observed. The t-CTA framework provides for analysis of multiple performance criteria across geographic regions and multiple temporal domains. This approach was demonstrated for a limited-access highway and principal arterial road network evaluated across five disparate periods. The availability of probe speed data and accessibility of the t-CTA framework has implications for national planning initiatives that must consider multiple objectives.

This paper presents a probabilistic procedure for assessing the structural responses of a statically indeterminate frame caused by soil spatial variability. Neglecting the soil spatial variability can underestimate the probability of structural failure, and lead to an unsafe structural design. The soil spatial variability is simulated by the random field theory implemented with Monte Carlo simulation. The diagrams of bending moment, shear force, and axial force induced by the differential settlement between the two footings of this indeterminate frame are solved by using the force method. The differential settlement, angular distortion, and frame internal forces are analyzed statistically. The probability of failure for three major structural failure modes, i.e., strength failure, in-plane buckling failure, and out-of-plane buckling failure, was rigorously assessed, revealing the dominant failure mode. It was demonstrated that there is a critical scale of fluctuation of soil property in the structural analysis. This study developed an innovative procedure for researchers and practitioners to assess the superstructure performance subjected to uncertain subsurface conditions.

The deterioration of bridge infrastructure along with diminishing funding resources necessitates reliable planning for future budget needs to maintain bridges at an acceptable level of performance. Although existing methodologies do consider uncertainties in individual bridge deterioration, there is a gap in the development of a network-level budget planning framework incorporating such stochastic phenomena. In this paper, a network-level needs-prediction simulation framework is proposed, that constructs confidence intervals for the output within a specific precision and significance level. Additionally, as the vast network size sets a demand for high computational effort, the Latin hypercube sampling technique is introduced to reduce the inherent simulation variance and decrease the number of replications needed. Ultimately, the applicability of the proposed methodology is demonstrated using a case study pertaining to a network of structures comprising bridges and culverts within the Austin District of the Texas DOT (TxDOT). The results confirm the capability of the proposed methodology in providing meaningful budget confidence interval estimates at the network level by using a significantly reduced number of computational resources.

Failure sampling is a structural reliability method based on modified conditional expectation suitable for complex problems for which reliability index–based approaches are inapplicable and simulation is needed. Such problems tend to have nonsmooth limit-state boundaries or are otherwise highly nonlinear. Previous studies recommended implementation of failure sampling with an extrapolation technique using Johnson’s distribution or the generalized lambda distribution. However, what implementation method works best is problem-dependent. The uncertainty of which approach provides best results for a particular problem limits the potential effectiveness of the method. In this study, a solution is proposed to this issue that eliminates this uncertainty. The proposed approach is an optimized ensemble that forms a uniquely weighted solution by utilizing the predictive capability of multiple curves to maximize accuracy for any particular problem. It was found that the proposed approach produces solutions superior to the methods of implementing failure sampling previously presented in the literature.

The present paper contributes on how to model maintenance decision support for the rail components, namely on grinding and renewal decisions, by developing a framework that provides an optimal decision map. A Markov decision process (MDP) approach is followed to derive an optimal policy that minimizes the total costs over an infinite horizon depending on the different condition states of the rail. A practical example is explored with the estimation of the Markov transition matrices (MTMs) and the corresponding cost/reward vectors. The MDP states are defined in terms of rail width, height, accumulated million gross tons (MGT) and damage occurrence. The optimal policy represents a condition-based maintenance plan with the aim of supporting railway infrastructure managers to take the best maintenance decision among a set of three possible actions depending on the state of the rail. Overall, the optimal policy requires railway infrastructure companies to have a tight control over their assets, in particular railway lines, in order to constantly monitor the actual condition of the rails.

This paper presents a new method for performing reliability-based design optimization (RBDO) of structures based on sequential optimization and reliability assessment (SORA). SORA is an effective method for solving RBDO that separates uncertainty analysis from optimization loops for reducing computational cost. However, SORA has some limitations, such as an inability to deal with problems involving discrete design variables or discontinuity in a domain, the dependency of solutions on the starting point of numerical solutions, and the lack of guarantee that global optimum solutions will be found. In this paper, a global decoupling method is proposed to tackle these limitations and improve the performance of SORA. This method links SORA enhanced with modified chaos control (ESORA) to an improved differential evolution (IDE) to perform RBDO. To deal with RBDO problems with discrete design variables, a rounding method is integrated into IDE. IDE also utilizes an adaptive selection scheme in a mutation step and an elitist strategy in the selection phase. To improve the efficiency of the method for RBDO problems with highly nonlinear performance functions and nonnormal random variables, a modified chaos control is employed to assess reliability constraints. Five numerical examples are considered to investigate the strength of the proposed method, illustrating its appropriate efficiency and accuracy. The proposed method is also extendable to more complex problems such as system reliability-based structural optimization and RBDO of nonlinear structures.

During extreme weather events, such as hurricanes or major winter snowstorms, power distribution systems, such as the overhead power distribution systems consisting of wood poles and power wires, would encounter failures that can bring massive power outages. Daily lives for communities are significantly affected by the interruptions of the regular service of power distributions. Fragility assessments of pole-wire systems under high wind loads have been carried out before, either using physics-based structural analysis methods or data-driven methods. However, fragility analyses of the entire pole-wire system are still challenging due to the lack of detailed information for dimensions, materials, soil conditions of pole-wire systems, the inconsistent wind loads applied on the systems at different geographic locations, and high computational costs. In the present study, a new local system modeling method considering the effects of neighboring spans for the resilience assessment of overhead power distribution systems under strong winds is proposed. The effects of neighboring spans are investigated to predict the failure probability of pole-wire systems with lower computational costs. Soil springs are applied to model the boundary support of poles to provide a more reliable estimation of the structural behaviors under large wind loads. Various simple pole-wire systems with different numbers of poles are studied to investigate the effects of neighboring spans. The number of neighboring spans that might affect the structural behaviors of the studied poles is investigated. The effects of lateral lines on the structural behaviors of the studied poles are also investigated. Based on the results, a case study is conducted to validate the accuracy of the proposed method. The reliability of a hypothetical pole-wire system calculated by the structural analysis method and the proposed method is compared. The accuracy of the proposed method is validated. Additionally, the effects of the degradation of neighboring spans on the structural behaviors of the studied poles are studied. For a local region with complex geographic terrain, wind loads applied on a pole-wire system could vary at different locations. Fragility curves of poles calculated with the proposed method under different wind directions are obtained to include the wind direction effects. The proposed method could help consider interactions between individual pole nodes with acceptable computational costs.

This work addresses the use of the probability transformation method (PTM) and of some of its extensions for solving mechanical and structural systems for which the response is modeled as random fields or variables that cannot be well-approximated as Gaussian. In particular, the static and dynamic stochastic analyses of linear structural systems excited by non-Gaussian excitations were considered. Moreover, the stochastic structures, the geometric and/or material properties of which are random, were analyzed by coupling the PTM with another approach introduced by one of the authors, the approximated principal deformation mode (APDM) method. Some of the results were reported in other works, and some results are shown here for the first time. This work gathered all the typologies of stochastic structural analyses in which the PTM can be advantageously applied, both in terms of accuracy and in terms of efficiency.

The different phases of a structure’s life-cycle are managed by different teams with little interaction. Correspondingly, the optimization of the individual phases is isolated and does not necessarily result in optimal life-cycle decisions. This motivates the treatment of structural optimization from a broader life-cycle perspective. A framework to enhance the design of structural systems by considering the operation and maintenance phase in the decision process is proposed in this article. The framework focuses on fatigue prone details, but it can be extended to consider other deterioration mechanisms. A hierarchical influence diagram is proposed as an efficient way to represent the probabilistic decision problem while considering system effects, such as the correlation of the deterioration among hot spots. A simple example is presented to illustrate the implementation of the framework. Challenges and potential applications are discussed.

The premovement time has been considered a critical factor of safety in building fire evacuations because the casualty of fire disasters is strongly associated with delayed evacuation. In the present study, an immersive virtual environment is employed to explore the effect of fire visibility, fire alarm voices, and individual characteristics (gender, occupation/major, and experience of fire safety training) on individuals’ fire evacuation premovement time in underground commercial buildings. A regression analysis of relevant time distribution is then provided. According to the results, the distribution of occupants’ premovement time varies in different underground commercial buildings. Overall fitting error is small in the Burr distribution and Weber distribution of a common regression model. Additionally, it proves that fire visibility exerts significant influence on occupants’ premovement time while other factors are not significantly influential in the case of predictable fire. Excluding their experience of fire prevention and safety training, factors such as fire alarm content, individual’s gender, and occupation (major) have a significant effect on premovement time when fires are not predictable. Moreover, the obtained findings could contribute to improving the current evaluation procedures of underground commercial building evacuation and relevant policies of fire prevention and safety.

In comparison to contemporary masonry structures, the material properties of existing masonry often show very high variability. Furthermore, the limited number of material tests in the assessment of existing structures introduces statistical uncertainties, which have to be adequately considered. The safety formats for the design of new masonry structures may therefore be inappropriate. To fill this gap, a method for determining characteristic values and partial safety factors for the compressive strength of existing masonry is developed. First, testing data regarding unit and mortar strength from the solid clay brick masonry of 72 existing buildings is evaluated in order to model prior distributions for the typical variance of unit and mortar strength. Second, the model for estimating masonry compressive strength based on unit and mortar strength according to Eurocode 6 is analyzed, and the corresponding model uncertainty is quantified. Finally, the proposed method is presented. The influence of prior information is demonstrated, and diagrams are provided, which enable a practice-oriented way of determining characteristic values and partial safety factors for a specific building.

Monte Carlo simulation is used to quantify and characterize uncertainty in a variety of applications, such as cost and engineering economic analysis and project management. The dependence or correlation between the random variables modeled can also be simulated to add more accuracy to simulations. However, there exists a difference between how correlation is most often estimated from data (linear correlation) and the correlation that is simulated (rank correlation). In this research an empirical methodology is developed to estimate the difference between the specified linear correlation between two random variables and the resulting linear correlation when rank correlation is simulated. It is shown that in some cases there can be relatively large differences. The methodology is based on the shape of the quantile-quantile plot of two distributions, the maximum possible linear correlation between two distributions, and the estimated level of correlation between the two random variables. This methodology also gives users the ability to estimate the rank correlation that, when simulated, generates a desired linear correlation. This methodology enhances the accuracy of simulations with dependent random variables while utilizing existing simulation software tools.

The first-year corrosion rate ($K$) values of metals measured using standard protocols are indicative of the relative vulnerability of metal infrastructure to atmospheric corrosion at a given location. The Dose–Response Functions (DRFs) given by ISO can be used to estimate $K$ for steel, zinc, aluminum, and copper if data for temperature, relative humidity, sulfur dioxide, and chloride deposition rates are available. This paper evaluates the efficiency of the ISO DRFs as a classification tool for atmospheric corrosivity in India. The best performing DRF was found to be that for steel and was subsequently combined with gridded datasets of $1°\times 1°$ ($\mathrm{latitude}\times \mathrm{longitude}$) resolution to develop a spatially continuous corrosivity map for steel infrastructure in India. A gridded sulfur dioxide dataset was specifically constituted and used in conjunction with duly validated gridded meteorological records to develop the map. The map reveals that 10% of the Indian mainland has very strong corrosivity, and many grids classified under low and moderate categories exhibit increasing trends. The gridded time of wetness map shows fair conformity to the corrosivity trends.

Resilience is defined as the capacity of a system to withstand a natural hazard and to regain desirable performance after the occurrence of such disasters. Natural hazards, such as floods, earthquakes, hurricanes, and tsunamis, have devastating effects on infrastructure systems. Such high-consequence events create the need for building resilient infrastructure for sustainable development. However, resilience-based infrastructure design is a challenging task, primarily due to factors such as lack of appropriate data for quantifying infrastructure resilience, and robustness of resilience models. Hence, there is a definite need to build resilience models based on realistic data and to validate such models. This paper developed a hierarchical Bayesian network (BN) model for flood resilience of housing infrastructure, and used the variable elimination (VE) method to quantify flood resilience. A study area in Barak Valley of Northeast India was selected because frequent high consequence flood events have occurred in this region. Relevant data were collected by performing an extensive field survey in various places of the valley, and were used to quantify two major factors—reliability and recovery—on which housing infrastructure resilience quantification depends. The main advantages of the proposed resilience model are that (1) it gives a realistic scenario of the infrastructure system robustness and its restoration after damage, (2) the proposed BN-based data-driven resilience model can be updated as and when more data are available, and (3) it helps planners, designers, policymakers, and stakeholders to make resilience-based decisions for sustainable communities.

Many researchers have developed bridge weigh-in-motion (BWIM) technology, mainly focusing on the representative value of the estimated axle weights. However, the estimation of the probabilistic distribution of axle weights is also important for understanding the ill conditioning of BWIM formulations and the uncertainty of estimation. Bayesian updating provides a coherent framework for assimilating data into models. Here, Bayesian bridge weigh-in-motion (BBWIM), which combines Bayesian updating and BWIM, is proposed. BBWIM can estimate not only the representative value of axle weights but also the uncertainty of the estimated value and the correlation among estimates. Uncertainties in estimated axle weight are quantitatively discussed with a simple two-axle problem. It is shown that the estimated weights of closely spaced axles have large uncertainty. BBWIM is applied to the measured data for an actual bridge. It is shown that additional information, in the form of a weak constraint on axle weight, namely, that closely spaced axles have similar weights, can reduce the uncertainty of estimated axle weights.

Because most of the numerical approaches are intensive in terms of computational effort and time, researchers have resorted to the use of surrogate models. For example, surrogate models for predicting the response of flexible pavement under given traffic and environmental conditions rely on the conventional approach of relating covariates with the response through simplified models. Usually, these covariates are chosen based on experience and data availability. Further, the form of the model is finalized based on statistical indicators and goodness-of-fit values. Thus, the concept of uncertainty in selecting the model is completely ignored, often leading to overconfident results and an increased risk in the prediction. Under these circumstances, Bayesian model averaging (BMA) could be a potential model building tool. The current study presents a BMA-based approach to choose influencing variables and quantify the uncertainty associated with linear regression models used to predict strain in a four-layered pavement structure. Initially, modulus and thickness of individual layers were used as input into a surrogate model building exercise. Out of 128 possible models, the best 100 models were used in conjunction with the BMA technique to rank various models and variables. Further, model uncertainty was represented by plotting the marginal density function of the coefficients, coefficient of variation, and normalized uncertainty range. BMA indicated that modulus (asphaltic layer and binder layer) and thickness of the asphaltic layer accounted for the majority of variability (up to 88%) associated with tensile strain in the asphaltic layer. Similarly, the thickness of the asphaltic layer and modulus of subgrade affected vertical compressive strain prediction models up to 38%. These variables also had lower uncertainty indicators when compared to other variables. Also, ranking based on the posterior inclusion probability can be used as an alternative for traditional sensitivity analysis.

To reduce the computational cost of assembled stochastic linear structural dynamic systems, a three-staged reduced order model-based framework for forward uncertainty propagation was developed. First, the physical domain was decomposed by constructing an equivalent reduced order numerical model that limited the cost of a single deterministic simulation. This was done in two phases: (1) reducing the system matrices of the subcomponents using component mode synthesis and (2) solving the resulting reduced system with the help of domain decomposition in an efficient manner. Second, functional decomposition was carried out in the stochastic space by employing a multioutput machine learning model that reduced the number of eigenvalue analyses to be performed. Thus, a multilevel framework was developed that propagated the dynamic response from the subcomponent level to the assembled global system level efficiently. Subsequently, reliability analysis was performed to assess the safety level and failure probability of linear stochastic dynamic systems. The results achieved by solving a two-dimensional (2D) building frame and a three-dimensional (3D) transmission tower model illustrated good performance of the proposed methodology, highlighting its potential for complex problems.