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

This paper provides an overview of theories and applications of structural system reliability (SSR). The paper defines SSR problems and discusses the growing needs for SSR analysis and technical challenges. Detailed literature reviews are provided for three subtopics: SSR methods for Boolean system events, SSR methods for sequential failures, and SSR-based design/topology optimization. Discussions of each subtopic define the target problem using mathematical formulations and categorize existing SSR methods in terms of the characteristics of the problems and approaches. The paper summarizes SSR methods that are considered critical in the history and have introduced notable technological developments in recent years. In each subtopic or category, the reviewed methods are compared with each other in terms of accuracy, computational efficiency, and implementation issues to allow identifying apposite methods for SSR applications. The paper concludes with remarks on future research needs and opportunities.

Emerging technologies (ETs) are increasingly becoming more accessible, and as they make their way into the field will be an integral part of the engineering community’s work that impacts future civil infrastructure. In the meantime, infrastructure resilience has become a recurring theme in government and industry discussions. ETs are expected to contribute to improving infrastructure resilience capacities, namely, absorptive, adaptive, and restorative. Through an extensive literature review, an in-depth conspectus of the state of the art of ETs in civil engineering is provided, leading to a vision for how these technologies impact infrastructure resilience. Three distinct disruptive technologies that can impact infrastructure resilience are demonstrated—specifically, smart materials, advanced construction technology, and advanced sensing technology. Such ETs will remarkably affect the well-known four characteristic elements of infrastructure resilience: redundancy, robustness, rapidity, and resourcefulness. These innovative technologies will warrant infrastructure to withstand or efficiently recover from multihazard disruptive events, leading to improved resilience. A roadmap to field implementation is presented considering the financial cycles necessary for ETs to make an impact on infrastructure resilience.

Comparison of calculated failure probabilities of technical systems with observed failure frequencies is an important part of the assessment of probabilistic calculations and can point to significant factors that are neglected in the calculations. Recent comparisons of failure probabilities and failure frequencies of nuclear power plants, bridges, and dams have shown that the calculated and observed values correspond surprisingly well. In addition, although various factors could be identified which have both positive and negative influences on the observed values, they almost cancel each other out. This study focuses on the comparison as it relates to tunnels. Extensive statistics indicate that most tunnel collapses occur during construction. Although this is also seen to a certain extent in bridges, it is not to the extent seen in tunnels. Events such as earthquakes and floods, which are the major causes of collapse of other structures, account for only about 10% to 20% of all tunnel collapses. Increasingly, tunnels are also being proven probabilistically. Based on these calculations, the available failure statistics can be compared with representative probabilistic tunnel proofs. The comparison shows large deviations between individual computations as well as between the mean value of all computations and the observed collapse frequencies.

Resilience is defined as the ability of a system to withstand and recover to a desired level of performance after the occurrence of a hazard. Community resilience has a significant socioeconomic implication for any disaster. Therefore, attempting to quantify resilience after a disaster is of utmost importance, particularly for planners, designers, and decision makers. Modern society depends on various infrastructure system networks to ensure functionality, and these infrastructure systems perform on their own and also perform interdependently with other infrastructure networks during natural hazards. For quantifying resilience, the interdependency between infrastructure systems plays a significant role; for instance, in the event of building damage, the state of damage to the roadways network is also crucial for the recovery process and ultimately in resilience. As a result, large-scale disruption of any infrastructure network increases significantly because of interdependency. In this work, an integrated geographic information system (GIS) and Bayesian belief network (BBN) framework is developed to study the resilience and effects in functionality due to interdependency among building and roadways infrastructure systems in a community. GIS is used for data collection, and BBN is adopted for computing the posterior probabilities of resilience. The framework is then implemented in a study area of Barak Valley in North-East India, and resilience is evaluated for the considered building-roadways network. Sensitivity analysis of system resilience to the critical components is performed to facilitate decision making under uncertainty. Finally, some general recommendations are given for improving flood resilience for future disasters.

A tremendous potential exists for using probe data to support various traffic operations activities. However, limited real-time probe data, especially on arterial roads, have become a barrier to realizing the full potential of this technology. In the absence of real-time probe data, traffic speeds are estimated via prediction engines trained on historical data. The accuracy of such traditional speed estimation approaches could be significantly improved if real-time data available through nearby infrastructure-mounted (IM) sensors were incorporated in the prediction process. This paper develops a machine learning framework for generating probe-like speed data from IM sensors with the aim of improving the accuracy of probe speed data during periods of low probe penetration. The framework includes using a pattern recognition system for extracting trends from historical traffic speed data. The extracted patterns together with historical temporal traffic flow data are used to prepare a representative training set for a deep learning–based model that can transform IM sensor data into probe-like data. The proposed approach successfully generated pseudo-probe data sets from nearby IM sensors with about 4.8 and $9.6\mathrm{km}/\mathrm{h}$ mean absolute error on freeways and arterials, respectively. A comparative analysis with baseline methods proved the superiority of the methodology adopted.

The complexity of the pairwise comparison process in the analytic hierarchy process (AHP) and the limitation of consistency are challenges that need to be addressed when there are many evaluation factors. An improved AHP is established by integrating the grey correlation model (GCM) and AHP, for which the tedious pairwise comparison process can be abandoned. The improved method integrates the advantages of grey system theory (GST) and AHP, overcomes the inherent limitations of AHP, and can be used to analyze many different types of problems that were previously analyzed by AHP or GST. In this paper, the improvement process is presented in detail, and the effectiveness of the improved AHP method is verified through several existing examples. This work uses the case of the Suning Square project in Langfang, Hebei Province, China, to apply the proposed improved AHP to construct a comprehensive evaluation system. The proposed method is used to evaluate the construction risk of foundation pit excavation, thereby providing technical guidance for efficient implementation of the project.

In consideration of the high vulnerability of the built environment, the assessment of seismic behavior of existing masonry buildings is a key topic in view of their retrofitting and reuse. Because masonry’s behavior depends on complex nonhomogeneous, anisotropic, asymmetric, and nonlinear properties, the definition of suitable mechanical models is still a critical issue, especially for stone masonry. Structural analyses of existing masonry buildings in seismic-prone areas are thus significantly influenced by the adopted mechanical models and assumptions about their relevant masonry properties, which are characterized by large uncertainty. In this study, a procedure for the definition of masonry classes and probability density functions of relevant mechanical parameters, such as elastic modulus and shear modulus, is proposed. The general procedure is illustrated referring to a significant number of in situ double-flat-jack test results on stone masonry obtained by the authors during an ad hoc experimental campaign. Finally, combining information on masonry quality obtained by visual inspection with results of in situ tests, a Bayesian methodology is proposed for the updating of masonry mechanical parameters, thereby providing the basis for a more refined probabilistic assessment of the seismic risk index.

Piping is a common phenomenon of seepage failure in underground space construction. Efforts have been made in understanding the mechanism of piping due to seepage gradient force and incorporating these in prediction technologies. In this study, the characteristics of seepage and piping occurrence during the excavation of a riverside tunnel are discussed based on hydraulic-mechanical coupling analysis. Four typical cut-off wall depths are considered to evaluate the effectiveness of different seepage-control measures, and two different water depths are used to assess the impact of water depth. The spatial variability of hydraulic conductivity, which is often the case in reality, is incorporated into the program of the finite-element method. Results reveal that hydraulic conductivity is a key factor affecting the formation of potentially dangerous seepage path and the probability of piping occurrence. When the spatial randomness in hydraulic conductivity is considered, the riverside tunnel is observed as being much more susceptible to piping. By contrast, the assumption of uniformity in hydraulic conductivity may yield an inaccurate estimation of the risk in tunneling. The results make it possible to visualize the potential seepage path and provide a better explanation of the piping mechanism.

In France, a deep geological repository project is under development for radioactive waste management. The lack of knowledge from similar facilities and the large time scales involved result in uncertainties about the repository environment in operating conditions. The reliability methods are dedicated to dealing with such problematics and are used in this work to study the behavior of the overpack containing high-level radioactive waste. Its function is to isolate the waste from the environment for the first $500\mathrm{years}$. The analysis is based on a finite-element model of the system integrating the time-related degradation of the system and considers a ductile fracture criterion. The stochastic structural model includes uncertain and time dependent corrosion rates and mechanical loading. A Kriging metamodel is calibrated in order to predict the behavior of the finite-element model with a much smaller computation time. The failure probability of the system is estimated using Monte Carlo simulations based on the results of both the finite-element model and the Kriging metamodel. The behavior of the system and the parameters having a first order influence on its performances are investigated through a sensitivity analysis.

Systems-level analysis of an engineered structure demands robust scientific and statistical protocols to assess model-driven conclusions that are often nontraditional and causal in their content. The formal mathematical, statistical, and philosophical foundations of causal inference on which such protocols are based are, nevertheless, not widely understood. The aims of this article are to (1) communicate the essentials of graph-based causal inference to the civil engineering community, (2) demonstrate how rigorous causal conclusions—and formal quantification of uncertainty regarding those conclusions—may be obtained in a typical engineered system application, and (3) discuss the value of this approach in the context of engineered system assessment. The concepts are illustrated via a river-weir ecosystem case study as an example of decision making for engineered systems in the built environment. In this setting, it is demonstrated how rigorous predictions can be made about the outcome of decisions that take a lack of prior knowledge about the system into account. The findings highlight to end users the value in applying this approach in providing quantitative probabilistic outputs that counter decision uncertainty at system level.

Highly dynamic loading conditions on clutch motors used in four-wheeled passenger vehicles cause them to fail quite often. The current diagnostic tools have proven to be inefficient to detect the onset of system degradation. This paper presents a degradation model to exhibit the state of health of the clutch. A novel condition indicator (CI) and a threshold for conditionally independent noisy signal from the motor subjected to cumulative degradation have been established. A dominating feature characterizing the motor health was discerned to be spectral entropy kurtosis which was identified while analyzing the time-series signal composed of agglomeration of different frequencies that produce higher octaves. Tests for monotonocity and trendability metrics affirmed that spectral entropy kurtosis is a distinguishing CI. Principal component analysis (PCA) allowed the fusion of features for the selection of the best-performing CI. The proposed CI was used in an exponential degradation model to predict the remaining useful life (RUL) of the motor with improved accuracy.

The target reliability index is pivotal to the design, assessment, inspection, and maintenance of structures. It is commonly determined by cost optimization. However, depending on the method of quantifying fatality cost, cost optimization may lead to inconsistent target reliability indices, causing inadequate or unnecessary investment for safety improvement. A new approach to determine the target reliability index is proposed by considering cost efficiency and human safety separately. In this approach, an optimal reliability index is determined through cost optimization, circumventing the quantification of fatality cost in failure consequences. The human safety requirement is fulfilled by adding a constraint to the cost optimization problem based on different acceptance criteria for fatality risk, including individual risk criteria, societal risk criteria, and marginal lifesaving cost principle. The reliability index associated with this constraint is termed herein as an acceptable reliability index. The target reliability index is determined as the maximum between the optimal and acceptable reliability indices. A parametric study is conducted to investigate the effects of various factors on the target reliability index, including the probability density distributions and uncertainties of structural demand as well as the number of potential fatalities.

While resilience metrics have been proposed and studied given a functionality recovery curve, they have not emphasized enough on accounting for the uncertainties in multihazard occurrences and intensities. Moreover, these resilience metrics are not risk-based (i.e., they do not express the system’s resilience loss as a frequency of exceedance), leading to inconsistencies in system performance description when compared to performance-based engineering frameworks. A risk-based tool termed the dysfunctionality hazard curve is proposed to assess the resilience of systems subjected to single hazards or multihazards with interevent dependencies. The dysfunctionality hazard curve expresses system resilience performance as frequency of exceedance of time to full functionality. In doing so, it characterizes system recovery as a sequence of repair activities and also considers the uncertainties in the multihazard occurrences and intensities. The dysfunctionality hazard curve is demonstrated for a residential building susceptible to earthquake and hurricane hazards. Results indicate that the dysfunctionality hazard curve for earthquakes is greater than that for hurricane winds under single hazards. Under multihazards, considering interevent dependencies during system recovery rather than ignoring them leads to a larger dysfunctionality hazard curve. Finally, the concept of the dysfunctionality hazard curve is also extended to a system-of-systems consisting of residential and commercial buildings.

Line sampling (LS) is a robust and powerful simulation technique to reduce the computational burden provided by Monte Carlo simulation (MCS) for the reliability analysis of engineering structures. However, when dealing with highly nonlinear and implicit limit-state functions, LS yields instable results as nonconvergence or divergence. In this study, a novel framework that integrates the LS method with the slime mold algorithm (LS-SMA) is proposed to solve complex structural reliability problems. SMA is a new metaheuristic population-based algorithm inspired by the behavior and morphological changes in slime molds that can well solve multivariable optimization problems. In the proposed method, the determination of the important direction of LS is formulated as an unconstrained optimization problem according to the LS theory. Then SMA is employed to solve this optimization problem to decrease the computational cost. Thus, the LS-SMA is able to overcome the drawbacks of LS such as the local convergence and divergence. Seven numerical problems were utilized to investigate the LS-SMA applicability, where its performance was compared with MCS, subset simulation (SS), importance sampling (IS), LS, first-order reliability method (FORM), and first-order control variate method (FOCM). The results demonstrate that the proposed LS-SMA can be applied with high efficiency for solving the reliability problems that involve highly nonlinear or dimensional and complex implicit limit-state functions.

Flooding due to intensive precipitation poses a major threat to lives and property. To deal with the resulting risks, information about flood-prone areas regarding water levels and flow velocities is needed. The flood probability of a certain point in the landscape depends on the one hand on the occurrence probability of a surface runoff generating–rainfall event and on the other hand on the flow- and runoff-determining properties of the terrain, e.g., the surface morphology and the hydraulic roughness. Simulation models for the flow of surface water are common tools for assessing the dynamics of flooding caused by intensive precipitation events. Major input parameters for such simulation tools are digital elevation models, surface roughness datasets, as well as data on the precipitation input. In order to make informed decisions that take the uncertainties of modeling results into account and to get an idea of the probability space, it is important to quantify the effects of different alternative model parameter sets regarding data sources as well as the spatial and temporal resolution of the input data. We evaluated the effects of different parameter sets for the hydronumeric computational fluid dynamics model HiPIMS on flow velocities and water levels.

This study evaluates the seismic safety of reinforced concrete moment-resistant (RC–MR) buildings designed with two different methods, i.e., direct displacement based design (DDBD) and conventional force based design (FBD), through a reliability-based probabilistic approach. Two groups of buildings with irregular but symmetric configuration of frames and different heights are designed based on the aforementioned seismic design approaches. After validation of the model developed in OpenSees version 2.5.0 software, all buildings are subjected to incremental dynamic analysis. The results indicate that the irregularity of the configuration of the frames has challenged the seismic performance of the buildings designed with the DDBD approach, whereas changing the height does not significantly affect the seismic performance. Comparison of the two seismic design approaches shows that by changing the approach from DDBD to FBD, the structures behave in a highly vulnerable and unreliable way, exhibiting poor seismic performance. Finally, the evaluation of various probabilistic methods indicates that the use of both aleatory and epistemic uncertainties increases the mean annual frequency of exceedance, resulting in a more conservative level of damage.

This paper presents a novel Gibbs sampling approach for structural health monitoring (SHM) with detection of structural changes/damages using incomplete complex modal data measured with a limited number of sensors. The usual difficulty with the availability of sensors in SHM practices and enforcing data acquisition in multiple setups is thoroughly addressed. Structural modeling incorporated with damping is considered in this proposed inverse problem exercise to calibrate damping parameters along with the stiffness and mass parameters facilitating SHM. Both proportional and nonproportional viscous damping are adopted in structural modeling. Detailed formulations on the probabilistic detection of changes/damages are presented in detail. Moreover, a Gibbs sampling technique is introduced to quantify uncertainties of the various sets of uncertain parameters, where samples of the conditional probability density function of a parameter set are obtained iteratively. The proposed approach retains the typical advantage of the nonrequirement of mode-matching. A validation exercise is performed using a three-dimensional building structure (attached with supplementary viscous dampers) and a laboratory steel structure considering multiple damage cases and different sensor placements. The proposed methodology is observed to be efficient for SHM using incomplete complex modal data measured with a limited number of sensors.

A shield tunnel may suffer various structural defects during its operational period. Different factors can contribute to defects on site, such as cracks, spalling, leakage, or offset, and a rational understanding of the failure path as a function of these defects is not clear at present. This paper aims to identify the cause of defects in shield tunnels using a new data fusion–based dynamic diagnosis. A literature review indicated that three main causes are abnormal load, installation error, and structure decay. These factors were taken into consideration in the proposed method. Both continuous and discrete Bayesian networks were constructed to integrate different types of data and to develop a reliable explanation for the occurrence of the tunnel defects. With in situ real-time monitoring data, the probability distributions for tunnel deformation and internal force were calculated using a continuous Bayesian network. A dynamic diagnosis of the defects was done by updating the monitoring data nodes and defect information in a discrete Bayesian network. A case study of the diagnosis of defects illustrated the method. Tunnel defect occurrence and the effects of multiple defects and changes in monitoring data had different influences on the diagnostic result. Based on the case study, it was concluded that the data fusion diagnosis method provides an efficient method for engineers to find and quantify the main causes of tunnel defects.

A major challenge in a time-dependent reliability analysis is to find a good balance between computational effort, accuracy, and comprehension of the analysis. In this contribution, computational efforts are reduced, and comprehension is increased by changing the perspective from a classical time-dependent probabilistic model to an equivalent parameter state model. The model maps all time-dependent information to one stochastic variable, allowing one to analyze the original problem by analyzing changes in the distribution of this single variable. This clear-cut structure allows for analyzing and visualizing dependencies in detail as well as a novel perspective on reliability estimation, even in complex settings. Furthermore, it just needs computational effort as low as that for a static reliability estimation in many cases. Our approach utilizes, but is not restricted to, Subset Simulation and is therefore especially designed for complex engineering structures of high dimension. To illustrate the model and its computational efficiency, an illustrative example in the time-dependent reliability analysis of concrete structures is presented, and it is shown how to analyze complex networks potentially.

Measuring cost variance is indispensable for understanding project status, monitoring the financial risks involved, and improving the project performance. Positive cost variance, denoted as cost overrun, is the key indicator of construction performance. Although the number of studies on cost overrun factors is growing substantially, many consider the causes of cost overrun factors as independent, and a regression model for forecasting is therefore developed. There is no consideration of the behavioral pattern of cost overrun factors. Hence, there is a need for a novel approach to managing cost risks that considers the characteristics of cost overrun factors. Construction cost overrun factors identified from extensive literature review were obtained through use of a questionnaire survey concerning building construction projects in India and analyzed through cluster analysis. A taxonomy was developed statistically with five groups of cost overrun factors: price-related, quality-related, design-related, execution-related, and professional skill-related. This classification illustrates the idiosyncrasy of cost overrun factors, thereby identifying the stakeholders that are best positioned to manage the cost risks. Thus, decision makers can understand cost overrun behavior and can effectively plan mitigation actions in project control practices. A case study is delineated to explain the applicability of the taxonomy of cost overrun factors.