Abstract
Modeling and analyzing the dynamic behavior of fluid-soil-structure interaction problems are crucial in structural engineering. The solution to such coupled engineering systems is often not achievable through analytical modeling alone, and a numerical solution is necessary. Generally, the Finite Element Method (FEM) is commonly used to address such problems. However, when dealing with coupled problems with complex geometry, the finite element method may not precisely represent the geometry, leading to errors that impact solution quality. Recently, Isogeometric Analysis (IGA) has emerged as a preferred method for modeling and analyzing complex systems. In this study, IGA based on Non-Uniform Rational B-Splines (NURBS) is employed to analyze the seismic behavior of concrete gravity dams, considering fluid-structure-foundation interaction. The performance of IGA is then compared with the classical finite element solution. The computational efficiency of IGA is demonstrated through case studies involving simulations of the reservoir-foundation-dam system under seismic loading.
Key Words
finite element analysis; fluid-soil-structure interaction; gravity dam; isogeometric analysis; seismic behavior; NURBS
Address
Abdelhafid Lahdiri: Laboratory of Solid Mechanics and Systems LMSS, Department of Civil Engineering, University of Boumerdes, Avenue de independance, Boumerdes, 35000, Algeria
Mohammed Kadri: Laboratory of Solid Mechanics and Systems LMSS, Department of Civil Engineering, University of Boumerdes, Avenue de independance, Boumerdes, 35000, Algeria/ Laboratory of Geomaterials, Environment and Planning (LGEA), UMMTO, Tizi Ouzou, 15000, Algeria
Abstract
In this paper, we proposed a new class of stochastic neutral neural networks with uncertain and deterministic coefficients. Made the Sigmund activation and Lipschitz activation functions less conditional. The Lyapnov-Krasovskii functional is constructed. The linear matrix inequality (LMI) is constructed using Schur's lemma, and new criteria for the global asymptotic stability and global asymptotic robust stability of neural networks are obtained. Furthermore, we have verified that the method is effective and feasible through numerical examples.
Key Words
delay; deterministic and uncertain coefficients; neutral neural networks; robust stability; stochastic
Address
Xiaoqi Sun and Ling Zhang: School of Mathematics and Statistics, Qingdao University, Qingdao 266071, China
