Consolidation assessment using Multi Expression Programming

In this study, new approximate solutions for consolidation have been developed in order to hasten the calculations. These solutions include two groups of equations, one can be used to calculate the average degree of consolidation and the other one for computing the time factor (inverse functions). C...

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Vydáno v:Applied soft computing Ročník 86; s. 105842
Hlavní autoři: Sharifi, Sohrab, Abrishami, Saeed, Gandomi, Amir H.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.01.2020
Témata:
Consolidation
Evolutionary computation
Geotechnical engineering
Multi-Expression Programming
Prediction
Multi-Expression Programming
Geotechnical engineering
Consolidation
Evolutionary computation
Prediction
ISSN:1568-4946, 1872-9681
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Shrnutí:In this study, new approximate solutions for consolidation have been developed in order to hasten the calculations. These solutions include two groups of equations, one can be used to calculate the average degree of consolidation and the other one for computing the time factor (inverse functions). Considering the complicated nature of consolidation, an evolutionary computation technique called Multi-Expression Programming was applied to generate several non-piecewise models which are accurate and straightforward enough for different purposes for calculating the degree of consolidation for each depth and its average as well for the whole soil layer. The parametric study was also performed to investigate the impact of each input parameter on the predicted consolidation degree of developed models for each depth. Moreover, the results of the consolidation test carried out on four different clays attained from the literature showed the proper performance of the proposed models. •Implicit and explicit models have been proposed by Multi Expression programming.•Generated models calculate degree of consolidation for each depth and the whole layer.•Both experimental and external validations have verified the accuracy of models.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2019.105842