Shape optimal design of arch dams using an adaptive neuro-fuzzy inference system and improved particle swarm optimization

• Published in: Applied mathematical modelling Vol. 34; no. 6; pp. 1574 - 1585
• Main Authors: Hamidian, D., Seyedpoor, S.M.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Inc 01.06.2010; Elsevier
• Subjects: Adaptive neuro-fuzzy inference system; Adaptive systems; Applied sciences; Arch dam; Arch dams; Artificial neural networks; Back propagation; Buildings. Public works; Dams and subsidiary installations; Exact sciences and technology; Fundamental areas of phenomenology (including applications); Hydraulic constructions; Improved particle swarm optimization; Inference; Mathematical models; Neural networks; Optimization; Physics; Shape optimal design; Solid mechanics; Structural and continuum mechanics; Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...); Improved particle swarm optimization; Arch dam; Adaptive neuro-fuzzy inference system; Shape optimal design; Geometrical shape; Vibration; Adaptive system; Fluid structure interaction; Neural network; Modeling; Particle swarm optimization; Geometrical model; Optimal design; Fuzzy logic; Backpropagation algorithm; Time domain method; Arch dams; Earthquakes; Swarm intelligence; Effective medium model
• ISSN: 0307-904X
• DOI: 10.1016/j.apm.2009.09.001

An efficient methodology is proposed to find the optimal shape of arch dams including fluid–structure interaction subject to earthquake ground motion. In order to reduce the computational cost of optimization process, an adaptive neuro-fuzzy inference system (ANFIS) is built to predict the dam effective response instead of directly evaluating it by a time-consuming finite element analysis (FEA). The presented ANFIS is compared with a widespread neural network termed back propagation neural network (BPNN) and it appears a better performance generality for estimating the dam response. The optimization task is implemented using an improved version of particle swarm optimization (PSO) named here as IPSO. In order to assess the effectiveness of the proposed methodology, the optimization of a real world arch dam is performed via both IPSO–ANFIS and PSO–BPNN approaches. The numerical results demonstrate the computational advantages of the proposed IPSO–ANFIS for optimal design of arch dams when compared with the PSO–BPNN approach.