Abstract
A new intelligent hybrid method for inverse modeling (Parameter Identification) of leakage from the body and foundation of earth dams considering transient flow model has been presented in this paper. The main objective is to determine the permeability in different parts of the dams using observation data. An objective function which concurrently employs time series of hydraulic heads and flow rates observations has been defined to overcome the ill-posedness issue (nonuniqueness and instability of the identified parameters). A finite element model which considers all construction phases of an earth dam has been generated and then orthogonal design, back propagation artificial neural network and Particle Swarm Optimization algorithm has been used simultaneously to perform inverse modeling. The suggested method has been used for inverse modeling of seepage in Baft dam in Kerman, Iran as a case study. Permeability coefficients of different parts of the dam have been inspected for three distinct predefined cases and in all three cases excellent results have been attained. The highly fitting results confirm the applicability of the recommended procedure in the inverse modeling of real large-scale problems to find the origin of leakage channels which not only reduces the calculation cost but also raises the consistency and efficacy in such problems.
Highlights
One of the most well-known bio-inspired algorithms used in optimization problems is particle swarm optimization algorithm (PSO), which basically consists of a machine-learning technique loosely inspired by birds flocking in search of food or fish schooling [1]
Performing an inverse analysis in the case of large-scale geotechnical-hydrological problem does not appear to be straightforward; the present study proposed a new hybrid procedure which has benefited from orthogonal design (OD), finite element analysis (FE), artificial neural networks (ANN) and PSO algorithms
Thereafter for each combination Ki in the space K, which is determined by the orthogonal design method, the simulated problem has been performed once and the obtained results has been used to train an artificial neural network, by which the time series of response values can be obtained at each point for any desired permeability state
Summary
One of the most well-known bio-inspired algorithms used in optimization problems is particle swarm optimization algorithm (PSO), which basically consists of a machine-learning technique loosely inspired by birds flocking in search of food or fish schooling [1]. The main objective is to ascertain the values of different parameters of the problem in a way that a good agreement be established between the real on-site readings and the results obtained from calculations For this purpose, a certain objective function is introduced (e.g. the mean squared error of measured water heads and that of problem analysis by assuming a set of arbitrary input parameters) [18]. The use of steady state model in special conditions such as permeability changing due to excavation or injection, sudden changes in the upstream pool water level or other variations occur in the model's boundary conditions, may possibly not result in accurate answers [9] In these cases, employing the transient flow model in the inverse analysis might be helpful [19, 22, 29]. The results show that the identified permeability coefficients are reasonable and properly fit the real ones, the proposed method provides a new mean s of accurately detecting the overall seepage behavior of earth dams while servicing
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