Evolutionary design of a generalized polynomial neural network for modelling sediment transport in clean pipes. Issue 10 (2nd October 2016)

• Record Type: Journal Article
• Authors: Ebtehaj, Isa; Bonakdari, Hossein; Khoshbin, Fatemeh
• Is Part Of: Engineering optimization. Volume 48:Issue 10(2016)
• Page Start: 1793
• Page End: 1807
• Publication Date: 2016-10-02
• Subjects: sediment transport -- bed load -- clean pipe -- genetic algorithm -- Pareto optimization; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2015.1137567
• Languages: English
• ISSNs: 0305-215X

Abstract

Abstract : To determine the minimum velocity required to prevent sedimentation, six different models were proposed to estimate the densimetric Froude number (Fr). The dimensionless parameters of the models were applied along with a combination of the group method of data handling (GMDH) and the multi-target genetic algorithm. Therefore, an evolutionary design of the generalized GMDH was developed using a genetic algorithm with a specific coding scheme so as not to restrict connectivity configurations to abutting layers only. In addition, a new preserving mechanism by the multi-target genetic algorithm was utilized for the Pareto optimization of GMDH. The results indicated that the most accurate model was the one that used the volumetric concentration of sediment ( CV ), relative hydraulic radius ( d / R ), dimensionless particle number ( D gr ) and overall sediment friction factor ( λs ) in estimating Fr. Furthermore, the comparison between the proposed method and traditional equations indicated that GMDH is more accurate than existing equations.