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Treatment of conflicting expert opinion in probabilistic analysis
Published in Marc A. Maes, Luc Huyse, Reliability and Optimization of Structural Systems, 2020
In real engineering problems the designer typically makes decisions using incomplete information. If Y and Θ represent the inherent random variables and epistemic (statistical/model) uncertainty respectively the reliability problem can be reformulated as: pF=∫Θ∫g(y,θ)<0fY|Θ(y,θ)dyfΘ(θ)dθ
Introduction
Published in Oscar Osvaldo Marquez-Calvo, Advancing Robust Multi-Objective Optimisation Applied to Complex Model-Based Water-Related Problems, 2020
Further, engineers often work not only with incomplete information, but also with incorrect information. This incorrect information could be due to human error; or it could be due to incorrect reading of the variable result of failure, miscalibration, or lack of resolution of the measuring device, and all these aspects increase uncertainty.
Engineering: Making Hard Decisions Under Uncertainty
Published in John X. Wang, Decision Making Under Uncertainty, 2002
Incomplete information results in uncertainties for engineering judgment and decision making. To minimize the uncertainty and thus improve the accuracy of judgments and decisions, engineers need to combine information from current observations and prior knowledge from historic data. Accurate assessment is the basis of effective engineering decisions.
Handling incomplete and missing data in water network database using imputation methods
Published in Sustainable and Resilient Infrastructure, 2020
Golam Kabir, Solomon Tesfamariam, Jordi Hemsing, Rehan Sadiq
The potable water network databases often have missing values especially for the small to medium-sized utilities because of the scarcity of technological, and financial resources, limited experience and analysis or input errors. This missing or incomplete information often limit the applicability and the interpretation of data, which may lead to severe loss of information or introduction of biases in decision-making. In this research, the advanced methods were applied to treat missing values in water network databases and to support the utility managers or authorities for effective decision-making. To impute missing values of multivariate pipe characteristics data from WDN, three single imputation methods: mean imputation, median imputation, and linear regression-based and three multiple imputation methods: IRMI, AMELIA, and IMPSEQ were compared. The performance of these single and multiple imputation methods was evaluated using MSE, MAE, RMSE, and PBIAS and visual inspection techniques.
A new multi-criteria method based on DEMATEL, ANP and grey clustering for quality sorting of incoming cores in remanufacturing systems under epistemic uncertainty: a case study of heavy-duty equipment
Published in Cogent Engineering, 2022
Udisubakti Ciptomulyono, Mohamad Imron Mustajib, Putu Dana Karningsih, Dewanti Anggrahini, Satrio Samudro Aji Basuki
In the literature, the term uncertainty has been used to refer to situations in which incomplete information. To better define the uncertainty, Y. Yang et al. (2019) categorised this as being two different groups: subjective and objective. An interpretation, such as linguistic descriptions, are frequently tend to be subjective uncertainty. This type of uncertainty also called as epistemic uncertainty, which may be caused by imperfect information or imprecise data collection. The most prevalent examples of incomplete information are when data regarding system elements (parameters) is missing/unavailable (S. Liu et al., 2016a). This is because humans’ inability to correctly measure and model the physical environment (Akbari et al., 2020). Epistemic uncertainty is sometimes also called functional uncertainty, limited information or informative uncertainty (Y. Li et al., 2013). On the other hand, objective uncertainty or aleatory uncertainty is caused by inherent uncertainty or randomness of nature as the object itself, not by the description. Stochastic uncertainty and natural variability are another term for aleatory uncertainty (Y. Li et al., 2013). Dealing with various categories of uncertainties, many methods have been developed to handle these, such as (Liu et al., 2016a): probability statistics, fuzzy sets, rough sets and grey systems theory (GST). A classic example of a decision model under uncertainty is probabilistic model. This is exemplified in the work undertaken by Gavidel and Rickli (2015). However, building probabilistic model of uncertainty requires a lot of data than GST based model. Researchers have recently made significant efforts in the field of uncertainty quantification to address issues where there is a lack of knowledge as limited data availability. By way of illustration, Liu et al. (2016) recommended GST in modelling and data analysis. In view of the GST is a superior method for handling uncertainty that specializes at mathematical analysis of systems with uncertain data. It is illustrated in Figure 1 that with a limited amount of data, the grey system can generate possible outcomes.