Browsing by Author "Mohammadi, Mirali"

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Citation - WoS: 9
Citation - Scopus: 10
A collaborative numerical simulation-soft computing approach for earth dams first impoundment modeling
(ELSEVIER SCI LTD, 2023) Behzad Shakouri; Mirali Mohammadi; Mir Jafar Sadegh Safari; Mohammad Amin Hariri-Ardebili; Hariri-Ardebili, Mohammad Amin; Shakouri, Behzad; Safari, Mir Jafar Sadegh; Mohammadi, Mirali
Uncertainty quantification plays a crucial role in the design monitoring and risk assessment of earth dams. To reduce the computational burden we employ a combination of finite difference method and soft computing techniques to investigate material uncertainties in earth dams during the initial impoundment stage. The findings of sensitivity analysis with the Tornado diagram indicate that key material properties such as dry density elasticity modulus friction angle and Poisson's ratio significantly influence the displacements and stress analysis. In our study we explore four variants of extreme learning machines (ELMs): the standalone ELM hybridized versions with the improved grey wolf optimizer algorithm ant colony optimization for continuous domains and artificial bee colony. These methods are assessed across various training sizes to predict multiple parameters including horizontal and vertical displacements stresses and the factor of safety (FoS). The hybridized ELM with the improved grey wolf optimizer algorithm emerges as the superior choice for most of the response variables. A minimum of 200 numerical simulations is required to establish a stable and accurate meta-model with an average prediction error of less than 3% for responses and the FoS.
Citation - WoS: 31
Citation - Scopus: 35
Sediment transport modeling in open channels using neuro-fuzzy and gene expression programming techniques
(IWA Publishing 12 Caxton Street London SW1H 0QS, 2019) Katayoun Kargar; Mir Jafar Sadegh Safari; Mirali Mohammadi; Saeed Samadianfard; Kargar, Katayoun; Samadianfard, Saeed; Safari, Mir Jafar Sadegh; Mohammadi, Mirali
Deposition of sediment is a vital economical and technical problem for design of sewers urban drainage irrigation channels and in general rigid boundary channels. In order to confine continuous sediment deposition rigid boundary channels are designed based on self-cleansing criteria. Recently instead of using a single velocity value for design of the self-cleansing channels more hydraulic parameters such as sediment fluid flow and channel characteristics are being utilized. In this study two techniques of neuro-fuzzy (NF) and gene expression programming (GEP) are implemented for particle Froude number (Frp) estimation of the non-deposition condition of sediment transport in rigid boundary channels. The models are established based on laboratory experimental data with wide ranges of sediment and pipe sizes. The developed models’ performances have been compared with empirical equations based on two statistical factors comprising the root mean square error (RMSE) and the concordance coefficient (CC). Besides Taylor diagrams are used to test the resemblance between measured and calculated values. The outcomes disclose that NF4 as the precise NF model performs better than the best GEP model (GEP1) and regression equations. As a conclusion the obtained results proved the suitable accuracy and applicability of the NF method in Frp estimation. © 2019 Elsevier B.V. All rights reserved.