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

Reliability assessment of engineered structures is a powerful and useful concept to estimate the structural capacity of withstanding hazardous events during their service lives. Taking into account the time variation of both structural resistance and the external load processes, the structural safety level is dependent on the duration of service period of interest, due to the accumulation of hazards by exposure in time. This paper presents an overview on the nonempirical assessment methods for time-dependent reliability of deteriorating structures. Generally, these methods can be classified into two types, namely simulation-based and analytical methods. The former is usually brute, and is especially suitable for solving high-dimensional reliability problems. Conversely, analytical solutions may improve the calculation efficiency significantly, and offer insights into the reliability problem that otherwise could be difficult to achieve through Monte Carlo simulation. Both the simulation-based and analytical methods will be reviewed in this paper. Furthermore, the application of time-dependent reliability methods in practical engineering is discussed. Recommendations for future research efforts are also presented.

Computing the cumulative failure probability for a given period of time is the main goal of a time-dependent reliability analysis. Estimating this probability is challenging for problems with nonmonotonic performance functions, especially when they are costly to evaluate and have high dimensionality. Discretizing the time interval is one main step in most of the time-dependent reliability methods. Hence, the problem can be converted into a serially connected system reliability problem. Therefore, efficient system reliability methods can be used for time-dependent reliability analysis. AK-SYS (Active learning and Kriging-based SYStem reliability method) is a Kriging-based method for system reliability assessment, including an active learning procedure for the enrichment process. In this paper, we exploit the efficiency of AK-SYS to propose a new time-dependent reliability method that is called AK-SYS-t. Two examples are used to compare the efficiency of the proposed method with competing methods, and a third example is used to highlight the opportunities offered by this method for fatigue reliability analysis. In the end, a crude approach is also proposed to provide the full curve of the cumulative failure probability.

A hole-effect autocorrelation function is one that is non-monotonic or pseudo-periodic as defined in the geostatistics literature. This paper shows that such an autocorrelation function (ACF) can influence the probability of failure of some geotechnical structures. As such, it is relevant to ask whether this hole effect does exist and is identifiable from the cone penetration test data. For this purpose, a novel hole-effect ACF model, called the cosine Whittle-Matérn (CosWM) model, is proposed to simultaneously identify the scale of fluctuation (SOF), sample path smoothness, and hole effect. Based on simulation examples and two real case histories, it is found that the hole effect is identifiable only if the hole effect is significant and the data record is of sufficient length. One real case history exhibits a significant hole effect, and this hole effect is successfully identified by the CosWM model. It is also found that if a monotonic ACF is adopted in place of the CosWM model for this case history, not only can the hole effect not be identified (as is to be expected) but also the SOF will be overestimated significantly.

Geotechnical design considers different limit states of design. Conventional deterministic design methodology uses the factor of safety approach, while reliability-based design and robust design methodology use probability distribution and statistics of properties. In this study, different design methodologies were compared. Efficient probabilistic methods and optimization techniques such as the first-order reliability method and genetic algorithm were used to arrive at a robust and optimized design. Estimation of system reliability helps in attributing importance to both the limit states involved, simultaneously. In addition, a combination of random field theory with numerical modeling in the framework of the Monte Carlo simulation method and response surface method was used in considering spatial variability. In this way, the risk involved while using conventional deterministic design methodology is brought out; it is apparent from the larger foundation sizes obtained using reliability-based and robust design methodologies. This emphasizes the importance of considering the variability of soil and the robustness of design for critical structures using probabilistic methods.

Seepage is a major concern during the construction of a riverside tunnel, because the seepage may cause both hydraulic and mechanical problems. At present, there are some well-accepted methods combining the hydraulic-mechanical coupling effect. This paper investigated a tunnel near a riverside under seepage forces using hydraulic-mechanical coupling analysis. Two cases were considered: an unlined tunnel without waterproofing, and a tunnel with a cement-treated soil surround acting as water barrier. The spatial variability of hydraulic conductivity was taken into account using random finite-element analysis. Results indicated that the analysis is able to account reasonably for the variation range of soil parameters. The arithmetic mean of hydraulic conductivity could serve as a rule of thumb estimation for a macroscale representative hydraulic conductivity. The results are likely to yield a scientific estimation of the seepage flow as well as a better understanding of the potential tunnel collapse mechanism.

In this paper, an adaptive Hermite distribution model with probability-weighted moments (PWMs) is proposed for evaluating the extreme-value distribution (EVD) of response, which serves as the basis of seismic reliability analysis of complex nonlinear structures under random seismic excitations. From the perspective of EVD, the problem formulation is first introduced. Then, an adaptive distribution model, named as the adaptive Hermite polynomial normal transformation model (A-HPNT), is established to estimate the EVD. The undetermined coefficients of A-HPNT are specified via the PWMs matching technique, in which only linear systems of equations need to be solved. To optimally determine the degree for A-HPNT, a two-step criterion is effectively established accordingly. An efficient high-dimensional sampling technique is introduced for generating samples of extreme value, estimating both the PWMs and statistical moments of EVD. When the entire distribution of EVD is recovered, one can compute the failure probability and reliability index via an integral over the EVD. Two numerical examples, a 10-story nonlinear shear frame structure and a practical 13-story reinforced concrete frame-shear wall structure driven by random seismic excitations, are presented to verify the efficacy of the proposed method for seismic reliability evaluation of complex nonlinear structures.

Tunnel roof deflection is an important measure to control the safety of excavation activity, especially for large-section tunnels using the sequential construction method. A three-dimensional (3D) model of a sequential excavation project in the Lijiaping metro tunnel, Chongqing, China, was developed and the maximum tunnel roof deflection was calculated deterministically at the end of each excavation stage. By defining the characteristics of the soft rock layers surrounding the tunnel section as the random variables, a probabilistic analysis was conducted and the problem was formulated as a reliability model. Two different approaches were used to solve the established reliability problem. One entails an artificial neural network (ANN) trained by a large data set obtained from numerical simulations and then accompanied by the Monte Carlo (MC) sampling method to calculate the probability. The other is a simplified reliability approach using a small data set to approximate the exceedance probability via a regression-based algorithm. The proposed ANN-based metamodel used in the first method could accurately predict tunnel roof deflection and replace the software simulator. Subsequently, the probabilistic results obtained from this method following MC sampling could well converge and provide the probability with enough accuracy. Interestingly, the simplified approach with more than 40 random samples can also provide acceptable results, which provides an economical approach to estimate the exceedance probability.

In recent years, the use of advanced simulation tools for modeling the behavior of flooding at a nuclear power plant has gained significant importance. The credibility of advanced simulation codes is assessed using formal approaches for verification and validation. In this manuscript, a formal risk-informed validation approach is proposed that provides a basis to quantify credibility of system-level risk assessments. The credibility of system-level validation is represented using a probabilistic metric and maturity levels. The novelty of the proposed approach lies in the integration of risk-informed validation approach with the US Nuclear Regulatory Commission’s (USNRC) Evaluation Model Development and Assessment Process (EMDAP) framework. This allows transformation of EMDAP into a risk-informed EMDAP. The applicability of the proposed framework is evaluated by application to a flooding scenario of a sunny-day dam failure. Application to the flooding scenario illustrates that the credibility of flooding simulations can be assessed using formal quantification, which is otherwise not possible in the existing EMDAP framework.

Around 15% of seismically designed Californian box-girder bridges incorporate at least one in-span hinge. This class of bridges can be further divided into two subclasses (i.e., single-frame and multiframe subclasses) according to the number of bridge spans in each bay of the bridge. The number of spans has a considerable influence on the dynamic response of box-girder bridges and, accordingly, on their seismic vulnerability. In this study, 420 analytical models for horizontally curved and straight highway bridges are developed using finite element OpenSEES software, considering geometric and mechanical uncertainties. Effects of in-span hinges, the number of spans, and the radius of the deck curvature on the seismic fragility of seismically designed concrete box-girder bridges with integral multicolumn bents and seat type abutments are investigated. Nonlinear time history analyses (NTHAs) are utilized to develop the fragility models for the bridge components and corresponding systems. Results show that the presence of in-span hinges and the number of spans have a considerable influence on the seismic vulnerability of box-girder bridges (for example, the median fragility of the straight bridges decreases by 26% when single-frame bridges change to multiframe at the moderate damage state). However, these effects can be quite variable for different deck radius at different damage states of the corresponding fragility functions.

The reliability of reinforced concrete (RC) bridge columns strengthened with externally bonded, steel-fiber reinforced polymer (SFRP) fabric subjected to blast loads was investigated. Columns were modeled with a nonlinear finite element approach that considers material damage, fracture, and separation. Different concrete strengths, longitudinal reinforcement ratios, and gravity and blast load levels were considered, whereas uncertainties in material strength and stiffness parameters, as well as load characteristics, were incorporated in the probabilistic analysis. The use of SFRP was found to allow significant increases in blast load while maintaining the same degree of column reliability.

Adding energy dissipation devices to reinforced concrete (RC) frame structures is a commonly adopted structural enhancement in seismically active regions. In this paper, a state-of-the-art seismic performance assessment is applied to a typical fixed-base RC frame structure designed based on the current Chinese code. Two additional structural enhancements, including equipping buckling restrained braces (BRB) and designing as a base-isolated structure, are investigated. Nonlinear response history analyses were performed to assess the structural performance parameters under four different seismic hazard levels (i.e., frequently occurring earthquake, design level earthquake, maximum considered earthquake, and very rare earthquake). The seismic performance is quantified in terms of repair times, repair costs, and casualties. The intensity-based assessment method is used, and a comparative analysis is performed for each performance measure. The results show that a base-isolated structure and BRB frame structure can each effectively reduce the repair cost and repair time compared with a fixed-base frame, as the repair mostly come from nonstructural components. In addition, the casualties in the base-isolated structure are the lowest of the three structures. It should be noted that the acceleration response of the BRB frame structure is the largest among the three structures, and that its acceleration-sensitive components are seriously damaged, causing the most casualties.

Besides robustness, a crucial aspect of power grid resilience is the postdisruption restoration of transmission capacity. Conventionally, grid repair planning is initiated when damage assessment is complete. With the current communication bandwidth and the role of drones in inspection, damage assessment is an increasingly dynamic process. Early damage estimates can serve preliminary repair planning. Subsequent replanning is then performed as updated damage assessments come in, thus mitigating the impact of restoration uncertainties. The present work examines the gains from starting grid recovery using preliminary damage estimates and replanning repair. A receding horizon approach, model predictive control (MPC), is applied to the IEEE-39 bus system. The benefits are expressed by the integral loss of service (ILOS), measuring the power demand not served over time. In the baseline, repair planning is not performed before definitive repair estimates are delivered. In this study, MPC reduces the maximum ILOS by up to 57%. In terms of computation, three prediction steps are sufficient for the receding horizon to decrease the maximum ILOS by at least 37%.

A new technique is proposed for determining the response of multi-degree-of-freedom nonlinear systems with singular parameter matrices subject to combined stochastic and deterministic excitations. Singular matrices in the governing equations of motion potentially account for the presence of constraint equations in the system. They also appear when a redundant coordinates modeling is adopted to derive the equations of motion of complex multibody systems. Since the system is subject to both stochastic and deterministic excitations, its response also has two components, namely a deterministic and a stochastic component. Therefore, using the harmonic balance method to treat the deterministic component leads to an overdetermined system of equations to be solved for computing the associated coefficients. Then the generalized statistical linearization method for deriving the stochastic response of nonlinear systems with singular matrices, in conjunction with an averaging treatment, are utilized to determine the stochastic component of the response. The validity of the proposed technique is demonstrated by pertinent numerical examples.

A main challenge for risk assessment on geotechnical systems is the computational effort required when stochastic sampling methods are used. Because the deterministic models used for geotechnical systems are often complicated and highly nonlinear, it is time-consuming to perform the deterministic analysis for each stochastic sample. The computational effort would become quite demanding, and even unrealistic, if direct Monte Carlo simulation (MCS) is used. To tackle this challenge, this study develops an efficient response surface method (RSM) that significantly improves computational efficiency and achieves the accuracy simultaneously. The proposed method is based on a novel sampling strategy called Bayesian compressive sensing (BCS). The proposed method is able to accurately reconstruct a highly nonlinear response surface from a small number of sampling points. Equations for the proposed RSM method are derived, and the attention is paid to extending the existing BCS method that deals only with low-dimensional data [e.g., one, two, or three-dimensional (1D, 2D, or 3D)] to high-dimensional data in RSM. The proposed method is illustrated using a highly nonlinear analytical function and a slope reliability analysis problem with consideration of spatial variability in soil properties. The results show that the proposed response surface method performs well and outperforms other response surface methods (e.g., response surface methods based on the kriging method or polynomial chaos expansion), particularly when sampling data are sparse.

This paper proposes the architecture of a real-time slope monitoring system based on the Internet of Things (IoT), which consists of a sensing layer, data acquisition layer, cloud computing layer, and client layer. The slope monitoring system was deployed in a large-scale slope in Shenzhen, China, which was estimated to be stable using slope stability analysis. However, a locally shallow landslide had occurred on the slope, meaning that the slope stability analysis method failed to indicate the slope failure. Conversely, through the monitoring results, it is found that the slope body is still under active movement. Besides, the advantages of a real-time slope monitoring system in risk communication among various parties are demonstrated by the case study. Wherein, the proposed monitoring system can produce more convincing, transparent, and temporal information regarding the state of slope safety than those by the manual monitoring technique. Furthermore, the monitoring data reflect the importance of detecting the internal displacement, as the indices for the facing displacement may be unable to indicate the actual movement of soils. That is particularly significant when considering a slope with supporting structures.

The calculation of failure probabilities is one of the basic problems in structural reliability. But to understand the causes of failure, the pure calculation of probabilities is not sufficient. It is attempted to explain that this can be done using the concept of design points. Some important mathematical aspects of the subset simulation method are studied in detail. In this context, it is outlined that this approach is merely a Monte Carlo (MC) style numerical approximation attempt for finding the neighborhoods of the design points, i.e., a disguised importance sampling method. New methods based on first/second-order reliability methods (FORM/SORM) improved by suitable importance sampling methods are introduced. This approach combines the simplicity of the analytic concept with the flexibility of additional MC estimates. So the structuralist view based on design points, which was given up in favor of pure probability estimates by MC, is reintroduced.

This paper presents a novel vector-based seismic vulnerability assessment methodology for deteriorating highway bridges by uniquely accounting for realistic deterioration of key structural components. The proposed framework offers notable enhancement over the state-of-the-art procedures that assume simplified and often unrealistic deterioration models of bridge components and promote inflexible unidimensional fragility curves for vulnerability assessment of aging bridge structures. Based on available data from past field investigation reports and laboratory experiments, this study proposes improved deterioration models that specifically encompass pitting corrosion of embedded reinforcing bars in columns under chloride attacks and necking failure of bearing anchor bolts. These deterioration models are incorporated within the finite element modeling of aging bridge components to develop multidimensional seismic demand estimates, capacity limit states, and parameterized seismic fragility functions using modern statistical learning algorithms. As a case study example, such vector-based fragility functions conditioned on a multitude of parameters are developed for the popular multi-span continuous steel girder highway bridge class of Central and Southeastern US. Twofold findings from the case -study reveal (1) the criticality of incorporating realistic deterioration modeling of critical bridge components for seismic vulnerability assessment, and (2) convenient utilization of parameterized seismic fragility functions by stakeholders and bridge engineers for prompt retrofit and rehabilitation decisions of aging highway bridges within the transportation infrastructure. Demonstrative examples reveal 25% and 10% overestimation of complete damage state median fragility for 75 year old bridge column and system, respectively, using the conventional modeling techniques when compared to the proposed improved deterioration models.

Imprecise probabilities have gained increasing popularity for quantitatively modeling uncertainty under incomplete information in various fields. However, it is still a computationally challenging task to propagate imprecise probabilities because a double-loop procedure is usually involved. In this contribution, a fully decoupled method, termed as active learning–augmented probabilistic integration (ALAPI), is developed to efficiently estimate the failure probability function (FPF) in the presence of imprecise probabilities. Specially, the parameterized probability-box models are of specific concern. By interpreting the failure probability integral from a Bayesian probabilistic integration perspective, the discretization error can be regarded as a kind of epistemic uncertainty, allowing it to be properly quantified and propagated through computational pipelines. Accordingly, an active learning probabilistic integration (ALPI) method is developed for failure probability estimation, in which a new learning function and a new stopping criterion associated with the upper bound of the posterior variance and coefficient of variation are proposed. Based on the idea of constructing an augmented uncertainty space, an imprecise augmented stochastic simulation (IASS) method is devised by using the random sampling high-dimensional representation model (RS-HDMR) for estimating the FPF in a pointwise stochastic simulation manner. To further improve the efficiency of IASS, the ALAPI is formed by an elegant combination of the ALPI and IASS, allowing the RS-HDMR component functions of the FPF to be properly inferred. Three benchmark examples are investigated to demonstrate the accuracy and efficiency of the proposed method.

A new method is presented to quantify the uncertainty in early-stage assessment of surface structure damage caused by tunneling-induced ground movements. The method employs the Monte Carlo simulation technique and a numerical model that simulates soil-structure interaction with an elastoplastic interface. The assessment method results in a probabilistic prediction of the level of structural damage. The sources of uncertainty in tunneling-induced ground movements are quantified using field monitoring data, and the uncertainties in soil and structure properties are characterized from published experimental tests and empirical analyses. The proposed assessment method is then demonstrated by two case studies. Variance-based sensitivity analysis and factor mapping analysis are conducted to demonstrate how the method can identify dominant sources of damage assessment uncertainty. For the both case studies, volume loss was found to contribute most to the uncertainty in building damage prediction; the contribution of other parameters varies significantly for different tunneling scenarios. However, the factor mapping analysis shows that the uncertainty of building properties and ground movements are almost equally responsible for the uncertainty in structural damage. More generally, the results lay the foundation for a performance-based design method to evaluate potential structural damage and to plan mitigation procedures in future tunneling projects.

Vibration-based damage detection for civil structures has been actively investigated. Many acceleration-based damage detection methods have been developed, among which the subspace-based system identification theory has been well established and has been a popular way to identify the structure’s modal parameters, having the advantage of being non-iterative and computationally tractable. Nevertheless, traditional modal parameters, including modal frequencies, mode shapes, and damping ratios, are not always sensitive to damage in actual civil structures. In this paper, a series of static loading tests and nondestructive hammer tests were applied to a prestressed concrete (PC) box girder. After analyzing the structure by the traditional modal analysis method, a technique based on null subspace analysis is proposed to monitor the health state of the PC box girder, and an index reflecting the damage levels is proposed. Compared with traditional modal parameters, the proposed damage index shows higher sensitivity and efficient precision to distinguish different structural damage levels. What is more, the damage caused by fracturing of prestressing tendons, which only slightly affect changes in modal frequencies, can also be obviously observed by this method.

The reliable performance of composite tubes conveying nanofluid is an essential issue for robust design under dynamic loads. Further, the frequency failure mode of these structures is a considerable performance function to provide a balance between mass and stiffness of the structure. The ability of the first-order reliability method (FORM)-based finite-step adaptive length (FAL) is discussed for reliability analysis of composite tubes under frequency failure mode. The performances for efficiency and robustness to evaluate the failure probability FORM formulas are discussed. Therefore, the capability of FAL using the steepest descent sensitivity vector is compared with three FORM formulations that are extended by the steepest descent search direction for nanocomposite tubes conveying nanofluid. In this current work, the Navier scheme is employed for calculating the frequency failure mode. Furthermore, the robustness and efficiency related to the steepest descent FORM formulas of HL-RF, directional stability transformation method (DSTM), and finite-step length (FSL) are compared with FAL. Based on this discussion, FAL contains efficient formula in comparison with other FORM formulas. Moreover, it is found that FSL formulas as well as FAL formulas are more robust than HL-RF and DSTM. This structure’s failure probability rises as the fluid velocity and pipe diameter increase.

Nonstationarity of wind regimes has an impact on design maps and on the reliability of built structures. This paper addressed the updating of design charts for wind loads, the nonstationary forecasting of maximum annual wind speeds for the next 30 years, and the evaluation of the impact of changing wind regimes on the structural reliability of built structures. The study object was the Brazilian territory, the design wind chart for which has been outdated for over 40 years. An updated design chart is proposed based on an extensive compilation of wind speed data measured at Brazilian (and neighboring) airports. The interpolation of wind speeds for intermediate locations was discussed based on polynomial and kriging response surfaces. The trend analysis for maximum annual wind speeds was carried out using Holt’s method. The impact of changing wind speeds was evaluated based on Monte Carlo analysis of a multistory steel building; structural collapse was determined using finite-element models that consider geometrical and material nonlinear analysis with imperfections. Results indicate that urgent attention should be given to changing wind speeds in the design of building structures.

The probability of unsatisfactory performance (PUP) is taken into consideration during the design of the US Army Corps of Engineers hydraulic structures retaining earth. These structures possess multiple limit states. A reliability analysis of a structure is used to determine the PUP, given the variability in loads and resistances. An adaptive polar numerical integration procedure is being proposed that uses specified numerical precision and Gaussian normal distribution properties to optimize reliability calculations for PUP, providing good spatial coverage. This method uses the properties of the normalized Gaussian variable hyperspace to adjust the number of integration steps taken at each hyperspherical shell based on the overall probability of the hyperspherical shell and the desired PUP precision. Results from an example earth retaining structure problem show the normalized Gaussian adaptive hyperspherical integration method provides more accurate PUP values compared to a simulation method.

Geological heterogeneity is a result of the natural deposit process of strata with a highly irregular and uncertain distribution of soil mass in the real world. However, compared with the effect of inherent spatial variability of soil properties on geostructures, the effect of geological heterogeneity has received less attention so far. In view of this limitation, this paper adopts a Markov random field model to stochastically simulate the geological heterogeneity for assessing the bearing capacity of a shallow foundation with quantified uncertainty. By mapping the generated stratigraphic realizations in the commercial software FLAC3D version 7.0, an ensemble of numerical models is generated. Computational results show that geological heterogeneity has a significant influence on the estimated capacities. Information entropy is used to characterize the geological uncertainty. It is not surprising that the increase of boreholes could reduce the average information entropy, and thus reduce the standard deviation of bearing capacities. But the effect of increasing the borehole numbers on the uncertainty reduction exhibits a clear nonlinear form with a potential to select an optimal borehole number. Results from the proposed stochastic analysis provide a range of calculated capacities, which can be used to mitigate the risk of insufficient bearing capacity.

In this paper, a new method for durability life prediction of reinforced concrete structures in a chlorine-eroded environment is presented based on the Gamma process. The Gamma process model is established based on the simulation data of chloride ion concentration on the surface of reinforced concrete structures, and the life of reinforced concrete structures is predicted. First, according to the chloride diffusion theory, through using the finite difference chloride diffusion model proposed in this paper under the action of multiple factors, a group of reinforced concrete reinforcement surface chloride concentration data are simulated using MATLAB. Similarly, the durability life data of reinforced concrete structures are simulated using the Monte Carlo method. Furthermore, a Gamma process model is developed based on the simulation data, and the life of a reinforced concrete structure is predicted based on the Gamma process. Finally, the correctness of the proposed method is verified through comparing the curve diagram of probability density function of the durability life of the Gamma process and the frequency histogram of the durability life obtained by the Monte Carlo simulation.

In the reliability assessment of a bridge in operation with regards to its ultimate limit state, it should be noted that the strengths of members and effects of traffic load vary continuously over its lifespan due to the structural deterioration and changes of the traffic environment, respectively. Therefore, it is essential to collect relevant data, and incorporate the extracted information into a time-dependent reliability analysis. However, previous studies have not considered both traffic load effects and material deterioration models in time-dependent reliability assessment for prestressed concrete (PSC) box girder bridges. To overcome this limitation and assess the reliability efficiently, this paper presents a comprehensive framework for time-dependent reliability assessment of post-tensioned concrete box girder bridges that can consider the traffic environment change and the corrosion in steel strands. The proposed framework employs probabilistic models and methods to estimate the flexural strengths and traffic load effects of a PSC box girder over time based on (1) corrosion-related data collected in the current practice of structure safety inspection, and (2) the traffic environment data obtained through weigh-in-motion (WIM) devices and traffic investigation. The time-dependent reliability is then computed using structural reliability methods based on the estimated strengths and load effects. As a numerical example, the time-dependent reliability of the PSC box girder of an actual cable-stayed bridge, Hwayang-Jobal Bridge in South Korea, is evaluated using the proposed framework. It is demonstrated that the framework can effectively assess the time-dependent reliability of a bridge in operation based on the uncertainties in the traffic environment and corrosion, and update it through data obtained by monitoring and inspection.

Embedded in the Malaysian oil and gas sectors’ experience, this study assesses the impact of external risk factors, which include political, economic, social, and environmental risk factors, and government support on the success of oil and gas construction projects in Malaysia using 61 employees of the oil and gas firms using a questionnaire survey. Structural equation modeling (SEM) results revealed that all the exogenous variables (political risk, economic risk, social risk, and environmental risk factors as well as government support) have a significant positive effect on project success. SEM results show that government support has no significant moderating effect on the impact of political, economic, social, and environmental risk factors on project success. The study developed an all-inclusive framework that can assist stakeholders in the sector in mitigating external risk factors in ensuring the success of projects. Policy implications and future study directions are also considered.

Corrosion deterioration is the predominant factor for a large number of cast iron (CI) water main breaks. The reliability of CI pipelines decreases overtime as corrosion growth on pipe walls significantly reduces the strength of pipeline materials. To predict the reliability of water pipelines accurately, this study used a finite-element analysis (FEA) approach to determine the maximum pipeline stress at various stages of the pipeline’s life. A series of FEAs was performed to consider uncertainties in parameters associated with stress analysis. The circumferential stress was found to be the most critical stress for pipelines. Time-variant circumferential stress was obtained by accounting for the time-dependent corrosion pit growth on the pipeline wall. The time-variant failure probability of pipelines was determined by comparing the circumferential stress and the tensile failure strength of the pipeline at the burst limit state. The FEA model developed in this study was validated with the results obtained from previous studies. Monte Carlo simulation was performed to generate the fragility curves in a probabilistic manner. This study also compared the fragility curves obtained utilizing FEA and conventional equations (elastic ring theory and the Spangler formula). The results showed that analytical equations are too conservative and provide a higher failure probability of pipeline. A sensitivity analysis was performed to identify the relative importance of parameters contributing to the pipeline’s performance. Additionally, the reliability of the water distribution network (WDN) was calculated based on the minimum cut set (MCS) and graph decomposition methods. The results showed that the two methods provide similar outcomes, however, MCS provides a greater safety margin. The proposed approach was illustrated for an example WDN.

This paper reports on the reliability-based assessment of light timber frame (LTF) and cross-laminated timber (CLT) shear walls. The outcomes of cyclic tests on 17 timber shear wall specimens calibrate the parameters of a modified Bouc-Wen model [extended energy-dependent generalized Bouc-Wen (EEGBW)] obtained from the extension of the generalized Bouc-Wen (GBW) model. The EEGBW model, which is an alternative to the Bouc-Wen-Baber-Noori (BWBN) one, accurately, simulates the essential features of timber connections and structural systems. The EEGBW model, representative of the global response of the shear wall, expresses the resisting term of a single-degree-of-freedom dynamic system, which describes the seismic response of a lumped mass supported by the shear walls. The results of truncated incremental dynamic analysis in terms of maximum displacement lead to the failure probabilities associated with increasing intensity measures. The resulting failure probabilities, fitted by a lognormal probability function, deliver the so-called fragility functions of the 17 structural archetypes by assuming three different mass values. The failure probabilities return the estimation of the reliability indexes, which quantitatively assess the seismic reliability of the considered structures. Additionally, the authors discuss the role of the top mass and its effects upon the shear walls’ seismic performance by comparing the LTF and CLT structural systems.

Bayesian updating of the reliability of deteriorating engineering structures based on inspection data has been attracting a lot of attention recently because it can provide more accurate estimates of the structural reliability as the number of inspection data increases. However, in the process of updating the reliability of deteriorating structures, it is not a trivial work to obtain the posterior distribution of the random variable of interest due to its multidimensional parameter integral space and complex integral function. This paper presents a new effective method for obtaining the explicit posterior distribution for the random variable of interest and evaluating time-variant reliability combined with all updated random variables. In the proposed method, the Smolyak-type quadrature formula is first applied to obtain the first three posterior moments of the uncertain parameters, and the three-parameter lognormal distribution is used to approximate their posterior probability distributions. Then, the two-layer Smolyak-type sparse grid is adopted to estimate the first three posterior moments of the random variable of interest, and its explicit posterior distribution can also be approximated by the three-parameter lognormal distribution. Finally, the time-variant reliability analysis considering Bayesian updating is conducted using all updated random variables. Numerical examples demonstrate that the proposed method requires less computational cost, but the results provided are almost the same as those of the Markov chain Monte Carlo simulation.