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Research Papers

ASME J. Risk Uncertainty Part B. 2018;4(4):041001-041001-7. doi:10.1115/1.4039243.

As a common type system, multistate weighted k-out-of-n system is of great importance in reliability engineering. The components are usually treated as independent from each other. It is usually not that case in real life and the components are dependent. On the other hand, the performance of the components degrades over time, leading to the change of the components' weight at the same time. As a result, the present paper provides a method to evaluate the dynamic reliability of multistate weighted k-out-of-n: G system with s-dependent components. The degradation of the components follows a Markov process and the components are nonrepairable. Copula function is used to model the s-dependence of the components. The LZ-transform for a discrete-state continuous-time Markov process is combined, and the explicit expression for the survival function and the mean time to failure (MTTF) of the system is obtained. A small electricity generating system is studied based on our method in the illustration, and detailed comparison result is made for dependent case and independent case. Dynamic reliability with varied levels of electricity generation conforming to the actual situation for this generating system is also calculated.

Commentary by Dr. Valentin Fuster
ASME J. Risk Uncertainty Part B. 2018;4(4):041002-041002-12. doi:10.1115/1.4039149.

Bayesian networks (BNs) are being studied in recent years for system diagnosis, reliability analysis, and design of complex engineered systems. In several practical applications, BNs need to be learned from available data before being used for design or other purposes. Current BN learning algorithms are mainly developed for networks with only discrete variables. Engineering design problems often consist of both discrete and continuous variables. This paper develops a framework to handle continuous variables in BN learning by integrating learning algorithms of discrete BNs with Gaussian mixture models (GMMs). We first make the topology learning more robust by optimizing the number of Gaussian components in the univariate GMMs currently available in the literature. Based on the BN topology learning, a new multivariate Gaussian mixture (MGM) strategy is developed to improve the accuracy of conditional probability learning in the BN. A method is proposed to address this difficulty of MGM modeling with data of mixed discrete and continuous variables by mapping the data for discrete variables into data for a standard normal variable. The proposed framework is capable of learning BNs without discretizing the continuous variables or making assumptions about their conditional probability densities (CPDs). The applications of the learned BN to uncertainty quantification and model calibration are also investigated. The results of a mathematical example and an engineering application example demonstrate the effectiveness of the proposed framework.

Commentary by Dr. Valentin Fuster
Select Articles from Part A: Civil Engineering

Technical Papers

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2017;4(1):. doi:10.1061/AJRUA6.0000936.
Abstract 

Abstract  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.

Topics:
Structures , Wind pressure , Cladding systems (Building)
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2017;4(1):. doi:10.1061/AJRUA6.0000938.
Abstract 

Abstract  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.

Topics:
Algorithms , Semantics , Risk , Hazards

Case Studies

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2017;4(1):. doi:10.1061/AJRUA6.0000935.
Abstract 

Abstract  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.

Topics:
Maintenance , Reliability , Hot water

Technical Papers

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2018;4(1):. doi:10.1061/AJRUA6.0000949.
Abstract 

Abstract  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.

Topics:
Uncertainty quantification
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2017;4(1):. doi:10.1061/AJRUA6.0000948.
Abstract 

Abstract  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.

Topics:
Gravity (Force) , Dams , Concretes , Polishing equipment , Risk assessment , Hydroelectric power stations

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