Debiao Meng^1,*, Abílio Manuel Pinho de Jesus², Zeng Meng³

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 1-5, 2024, DOI:10.32604/cmes.2024.056319 - 20 August 2024

Abstract This article has no abstract. More >

Zhihui Xu¹, Shuwen Shang², Yuntong Pu³, Xiaoyan Su^2,*, Hong Qian², Xiaolei Pan²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2597-2617, 2024, DOI:10.32604/cmes.2023.031247 - 15 December 2023

Abstract Cognitive Reliability and Error Analysis Method (CREAM) is widely used in human reliability analysis (HRA). It defines nine common performance conditions (CPCs), which represent the factors that may affect human reliability and are used to modify the cognitive failure probability (CFP). However, the levels of CPCs are usually determined by domain experts, which may be subjective and uncertain. What’s more, the classic CREAM assumes that the CPCs are independent, which is unrealistic. Ignoring the dependence among CPCs will result in repeated calculations of the influence of the CPCs on CFP and lead to unreasonable reliability More > Graphic Abstract

C. Jiang^1,2, X. Han¹, D. Li³

CMC-Computers, Materials & Continua, Vol.27, No.3, pp. 275-304, 2012, DOI:10.3970/cmc.2011.027.275

Abstract For optimization or decision-making problems with interval uncertainty, the interval comparison relation plays a very important role, as only based on it a better or best decision can be determined. In this paper, a new kind of interval comparison relation termed as reliability-based possibility degree of interval is proposed to give quantitative evaluations on "how much better" of one interval than another, which is more suitable for engineering reliability analysis and numerical computation than the existing relations. In the new relation, the range of the comparing values is extended into the whole real number field, and the precise comparison More >

B. Amaziane¹, A. Naji², D. Ouazar³, A. H.-D. Cheng⁴

CMC-Computers, Materials & Continua, Vol.2, No.2, pp. 85-96, 2005, DOI:10.3970/cmc.2005.002.085

Abstract In this paper the optimization of groundwater pumping in coastal aquifers under the threat of saltwater intrusion is investigated. The aquifer is inhomogeneous and contains several hydraulic conductivities zones. The aquifer data such as the hydraulic conductivities are uncertain, but with their expected mean and standard deviation values given. A stochastic boundary element method based on the perturbation technique is employed as the simulation tool. The stochastic optimization is handled by the chance-constrained programming. Genetic algorithm is selected as the optimization tool. Numerical examples of deterministic and stochastic problems are provided to demonstrate the feasibility More >