Abstract
Seepage is one of the most challenging issues in some procedures such as design, construction, and operation of embankment or earth fill dams. The purpose of this research is to develop a new solution based on governing equations to solve the seepage problem in an effective way. Therefore, by implementing the equations in the programming environment, more than 24,000 models were designed to be applicable to different conditions. Input data included different parameters such as slopes in upstream and downstream, embankment width, soil permeability coefficient, height, and freeboard. With the use of this big data, a new process was developed to provide simple mathematical models for the seepage rate analysis. The study first used intelligent models to simulate the seepage behavior. Finally, the accuracy of the models was optimized using a new metaheuristic algorithm. This led to the ultimate flexibility of the final model presented as a new solution capable of evaluating different conditions. Finally, using the best model, new mathematical relationships were developed based on this methodology. This new solution can be used as a proper alternative to the governing equations of seepage rate estimation. Another advantage of the proposed model is its high flexibility that can be well applied to engineering design in this field, which was not possible using the initial equations.
Highlights
In geotechnical engineering, free surface seepage is one of the main concerns
The present paper develops a new hybrid model: invasive weed optimization (IWO)-Artificial Neural Network (ANN), for the purpose of predicting seepage rate
The new solution has been able to offer high potential as an alternative to the equations governing for seepage rates in the defined condition
Summary
(FDM) [1] and Finite Element Method (FEM) [2,3,4] These techniques are based on adaptive meshing. After considering all approaches proposed in the literature to solve the free surface seepage problems, one of the operative approaches, i.e., extended pressure (EP), was selected to be used in this study. This technique is of a high effectiveness since it reduces inequalities of variability into the simpler equalities based on Darcy’s law [14]. The numerical techniques are not used in the engineering field, in the soil mechanics area, mainly because they require rather problematical sources and performances. It means that the experimental tests in the field provide more natural databases compared to the numerical analysis
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