Abstract
Using linear Chouquet integral algorithm to design optimal time-cost model in large construction projects
Highlights
Given the evolving trend of different industries and the rise of new development plans and the rise of various industrial and business projects, the innovation-driven economy, the development of skills and knowledge and the motivation of the workforce and the ability to intelligently respond to change An economy-driven environment must have the proper planning and management based on a systematic approach to the planning, control, and execution of projects in terms of time and cost [1]
Validation is computed using Chouquet integrals taking into account the interaction between criteria
The use of the Chouquet integral method has been considered for a robust model in which the metrics work together and has been used in various cases [14]
Summary
Given the evolving trend of different industries and the rise of new development plans and the rise of various industrial and business projects, the innovation-driven economy, the development of skills and knowledge and the motivation of the workforce and the ability to intelligently respond to change An economy-driven environment must have the proper planning and management based on a systematic approach to the planning, control, and execution of projects in terms of time and cost [1]. The physical and information linkage between the activities of the various stages of the project as well as the existence of these relationships between the activities of a single stage limit the processes at that stage These constraints are often expressed in terms of the length of time that an activity is performed. Cheng et al (2010) used a fast turbulence genetic algorithm and a support vector machine to construct a model called the evolved support vector machine (ESIM) model This model is able to estimate the cost of completing construction projects with greater accuracy than EVM relationships. Narbio and Di Marco (2014) propose a new approach by integrating a progressive model and acquired scheduling method (ES) to improve the predictive accuracy of the costpredictive relationships of completion of construction projects in the value-added method. The results show that the prediction accuracy of the two models of unit cost and cross-sectional price methods are in good and medium order, respectively [13]
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More From: American Research Journal of Civil and Structural Engineering
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