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

Computation of the reliability of large technical systems is usually a very difficult problem for realistic systems, and it is generally not possible to calculate the exact reliability. There are many techniques for approximate calculations, but they are often complicated and difficult to implement. In this paper the development of a new method based on Monte Carlo simulation for efficient calculation of system reliability is described. Standard Monte Carlo simulation forms a simple and robust alternative for calculating system reliability. If one can generate large samples, the law of large numbers ensures that the estimated reliability will be accurate as well. This may, however, be a very time-consuming operation. The new method introduces a parameterized system that corresponds to the given system for a specific parameter value. By using regularity of the system reliability as a function of the introduced parameter, the system reliability for our original system can be predicted accurately from relatively small samples.

Risk identification is adversely affected by the still existing definitional and applicational discrepancy regarding risks and other related notions, such as hazards and impacts. A paradigm shift is beginning to be in effect, proposing the preliminary identification of risk sources to ameliorate the aforementioned adversities. However, apart from identifying risk sources from the outset, the bulk of the already conducted project risk-related research, from which risk sources could be derived, is still not free of discrepancies and is falling short of use. In this paper, a new linguistic clustering algorithm, using the k-means++ procedure in addition to the semantics tools of stop world removal and word stemming is developed and codified. Then, the algorithm is applied on a vast risk notions set, emanated from an exhaustive review of the relative literature. The clustered and semantically processed results of the application are then used for the deduction of risk sources. Thus, this paper provides a compact, general, and encompassing master set of risk sources, discretized among distinct overhead categories.

This paper presents a methodology for analyzing wind pressure data on cladding and components of low-rise buildings. The aerodynamic force acting on a specified area is obtained by summing up pressure time series measured at that area’s pressure taps times their respective tributary areas. This operation is carried out for all sums of tributary areas that make up rectangles with aspect ratios not exceeding four. The peak of the resulting area-averaged time series is extrapolated to a realistic storm duration by the translation method. The envelope of peaks over all wind directions is compared with current specifications. Results for one low-rise building for one terrain condition indicate that these specifications can seriously underestimate pressures on gable roofs and walls. Comparison of the proposed methodology with an alternative method for assignment of tributary areas and area averaging is shown as well.

This paper presents a quantitative method to assess the resilience and the resilience-based risk at the country level. The approach is inspired by the classical risk analysis, in which risk is a function of vulnerability, hazard, and exposure. In the proposed analysis, resilience-based risk is a function of resilience, hazard, and exposure. In the new formula, the resilience parameter is evaluated using the data provided by the Hyogo Framework for Action (HFA). HFA scores and ranks countries based on a number of equally weighted indicators. To use those indicators in the resilience assessment, the contribution of each indicator toward resilience must be determined. To do that, three methods to weight and combine the different HFA indicators are proposed. The first two methods are based on the dependence tree analysis (DTA), while the third method is based on a geometrical combination of the indicators using spider plots. The proposed methodology has been applied to a case study composed of 37 countries for which both the resilience ($R$) and the resilience-based risk ($RBR$) indexes have been determined.

In this work, the fourth-moment method and the higher-order unscented transformation is combined to calculate the failure probability. The higher-order unscented transformation is used to efficiently estimate the first four moments of the limit-state function, and then the fourth-moment method is used to estimate the failure probability. Compared with the traditional first-order and second-order reliability methods, the proposed method is not required to find the design point, and it is also not required to calculate the derivatives. Compared with the three-point estimation method, the proposed method has a good accuracy and lower computational cost. The numerical and engineering examples show the accuracy and efficiency of the proposed method.

The paper presents a methodology for the selection of an optimal set of stochastic intensity measure (IM) maps representing the regional hazard over a geographic area, which can subsequently be used for the analysis of spatially distributed infrastructure systems. A key characteristic of the proposed approach, named Hazard Quantization (HQ), is that it embraces the nature of regional IM maps as two-dimensional (2D) random fields, and therefore uses a methodology for the optimal representation of non-Gaussian and nonhomogeneous random fields with a limited number of samples. In HQ, the representation of the regional hazard is supported by proofs of optimality. In particular, HQ ensures mean-square convergence of the ensemble of representative IM maps to the complete portfolio of possible hazard events, which is a particularly important property for risk analysis. HQ does not require the use of specialized simulation techniques, such as importance sampling or hierarchical sampling of the involved parameters, making the method simple to use. Other desirable characteristics make the method robust and applicable to a variety of hazard sources. In this paper, HQ is demonstrated for the regional seismic hazard analysis of the Charleston, South Carolina, region. A small set of IM maps and their associated probabilities resulting from the application of HQ are evaluated at all points and all pairs of points, on their ability to correctly represent the hazard curve and autocorrelation. Finally, a detailed comparison of the proposed technique with other popular methodologies in the same field is presented.

This paper develops a performance-based seismic design (PBSD) procedure specifically for steel structures. Several improved steel connections were proposed to eliminate brittle fractures in welds following the Northridge earthquake of 1994. Making connections more flexible was considered to be an attractive option. One type of flexible connection considered in this study is expected to be economical and improve the behavior of steel structures, making them more seismic load–tolerant. However, the partial rigidities of connections need to be considered appropriately. To consider major sources of nonlinearity and rigidities of connections, structures are represented by finite elements. Steel structures are excited by seismic loading applied in the time domain, as required for the most sophisticated analysis. The algorithm incorporates major sources of uncertainty. To implement the PBSD concept, a novel reliability evaluation technique is proposed by integrating the first-order reliability method, the response surface method, and the advanced factorial design concept, producing compounding beneficial effects. The accuracy and efficiency of the procedure are verified with three informative examples. The study confirms the benefits of multiple deterministic analyses, as suggested in recent design guidelines. The behavior of the post-Northridge design is demonstrated to be superior to that of the pre-Northridge design, as expected. The PBSD procedure is appropriately documented for practicing engineers working on structural steel design and construction for everyday use.

There is widespread awareness in seismic-prone countries of the necessity to improve the seismic performance of old buildings and houses for disaster mitigation. To support the decision making of house owners regarding upgrades, appropriate seismic risk information should be conveyed to them; such information would allow them to compare the effects of seismic upgrade strategies on risk reduction. This study develops probabilistic damage index functions for wooden houses in Japan, taking site conditions into account. Three existing 2-story wooden houses with traditional seismic resistance elements typical of 1960s and 1970s construction are virtually upgraded or downgraded by adding or removing walls and/or braces to model houses with a range of load-carrying capacities. Then time history analyses are conducted by using simulated ground motions and the maximum interstory drift ratio to develop the damage index functions. Finally, the validity of the proposed functions is demonstrated by using field survey results.

Determining geotechnical design parameters (e.g., undrained shear strength $S_u$) is an essential step in the analysis and design of geotechnical structures at a particular site. The estimated design parameters are affected by the actual variability (i.e., inherent variability) of design parameters concerned, measurement errors due to imperfections in the measuring process, and transformation uncertainty associated with the transformation model linking measured parameters [e.g., standard penetration test (SPT) data] to design parameters (e.g., $S_u$). These uncertainties are lumped together as the total variability of design parameters. It is the actual variability resulting from natural geological factors, not the total variability, that directly affects the underlying mechanism and actual response of geotechnical structures. Proper characterization of site-specific geotechnical inherent variability is, hence, pivotal to accurately estimating the actual responses of geotechnical structures at a site. This paper develops a Bayesian sequential updating (BSU) approach for probabilistic characterization of site-specific inherent variability of $S_u$ of clay. The proposed BSU approach systematically combines prior knowledge (e.g., engineering judgment and experience) and site-specific information from both indirect (e.g., SPT data) and direct (e.g., $S_u$ values from triaxial tests) measurements to inversely infer the inherent variability of $S_u$ at a particular site. It accounts, explicitly and quantitatively, for effects of measurement errors and transformation uncertainty on the probabilistic characterization of site-specific inherent variability of $S_u$. The proposed approach is illustrated and validated using real-life and simulated data. It is shown that the proposed approach provides proper probabilistic characterization of site-specific inherent variability of $S_u$ based on available information from multiple sources. Sensitivity studies are also performed to explore effects of measurement errors on the performance of the proposed approach.

Timely completion of dam and hydroelectric power plant (HEPP) projects is indispensable for the countries constructing them due to their economic, political, and social impacts. Robust and stable schedules should be created at the beginning of these projects in order to realistically estimate project durations considering uncertainties and variations. This paper proposes a buffer sizing methodology based on fuzzy risk assessment which can be used to calculate time buffers accurately for concrete gravity dam and HEPP projects by considering the vulnerability of activities to various risk factors as well as their interdependencies. A generic schedule is developed and 89 potential causes of delay/risk factors are identified for the concrete gravity dam and HEPP projects. Risk assessment is conducted at the activity level. The inputs of the model are frequency and severity of risk factors, and the output is estimated time buffer as a percentage of original duration. Implementation of the model is illustrated by an example project. Results show that outputs of the model can be used for scheduling, estimation of time buffers, and risk management of concrete gravity dam and HEPP projects. Although the model and its outputs are specific for concrete gravity dams, the buffer sizing methodology based on fuzzy risk assessment can easily be adapted to other types of construction projects.

The effectiveness of the Florida Building Code (FBC) against the impacts of wind speed, duration of strong winds, and wind directional change is quantified. For seven historical hurricanes that impacted Florida during 2004 and 2005, wind speed, duration, and directional change are significantly correlated with insured wind loss. Loss increases loglinearly with wind speed, loss has a step-function relationship with directional change, and duration effects are only important for minor hurricanes. Loss is most sensitive to major hurricane wind speeds, followed closely by minor hurricane wind speeds, and is less sensitive to duration and directional change. A multiple regression analysis finds that homes built after the implementation of a statewide FBC in the early 2000s experience significantly lower losses than homes built in the previous decade, in agreement with previous literature. The total effect of the FBC in reducing losses appears to be effective against wind speed, wind duration, and wind directional change effects. Understanding the importance of different wind parameters in driving loss, combined with assessments of how building codes perform against those parameters, may inform effective building code development.

Health care facilities should be able to quickly adapt to catastrophic events such as natural and human-made disasters. One way to reduce the impacts of extreme events is to enhance a hospital’s resilience. Resilience is defined as the ability to absorb and recover from hazardous events, containing the effects of disasters when they occur. The goal of this paper is to propose a fast methodology for quantifying disaster resilience of health care facilities. An evaluation of disaster resilience was conducted on empirical data from tertiary hospitals in the San Francisco Bay area. A survey was conducted during a 4-month period using an ad hoc questionnaire, and the collected data were analyzed using factor analysis. A combination of variables was used to describe the characteristics of the hidden factors. Three factors were identified as most representative of hospital disaster resilience: (1) cooperation and training management; (2) resources and equipment capability; and (3) structural and organizational operating procedures. Together they cover 83% of the total variance. The overall level of hospital disaster resilience (R) was calculated by linearly combining the three extracted factors. This methodology provides a relatively simple way to evaluate a hospital’s ability to manage extreme events.

This study investigates the use of big data analytics in uncertainty quantification and applies the proposed framework to structural diagnosis and prognosis. With smart sensor technology making progress and low-cost online monitoring becoming increasingly possible, large quantities of data can be acquired during monitoring, thus exceeding the capacity of traditional data analytics techniques. The authors explore a software application technique to parallelize data analytics and efficiently handle the high volume, velocity, and variety of sensor data. Next, both forward and inverse problems in uncertainty quantification are investigated with this efficient computational approach. The authors use Bayesian methods for the inverse problem of diagnosis and parallelize numerical integration techniques such as Markov-chain Monte Carlo simulation and particle filter. To predict damage growth and the structure’s remaining useful life (forward problem), Monte Carlo simulation is used to propagate the uncertainties (both aleatory and epistemic) to the future state. The software approach is again applied to drive the parallelization of multiple finite-element analysis (FEA) runs, thus greatly saving on the computational cost. The proposed techniques are illustrated for the efficient diagnosis and prognosis of alkali-silica reactions in a concrete structure.

This study investigates the availability-based reliability-centered maintenance scheduling of domestic (building-integrated) hot water (DHW) of HVAC systems. The keeping system availability (KSA) method is adopted, which provides maintenance scheduling by incorporating the effect of the maintenance activities. This method has been developed for maintenance scheduling in power plants in which the continual ability to generate power is a critical issue. This approach is applied to the case of the DHW system of HVACs, which is also a critical system in provision of hot water in buildings during the long cold seasons in Canada. The mean time to failure (MTTF) and mean time to repair (MTTR) are used to measure the availability of the DHW system. Components with different maintenance timings are sorted according to the effect of maintenance on availability of the system. At the end, a combination of maintenance schedules for the components of the DHW system is provided to ensure its availability while avoiding overmaintenance.