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A hybrid meta-heuristic optimization method is introduced to efficiently find the optimal shape of concrete gravity dams including dam-water-foundation rock interaction subjected to earthquake loading. The hybrid meta-heuristic optimization method is based on a hybrid of gravitational search algorithm (GSA) and particle swarm optimization (PSO), which is called GSA-PSO. The operation of GSA-PSO includes three phases. In the first phase, a preliminary optimization is accomplished using GSA as local search. In the second phase, an optimal initial swarm is produced using the optimum result of GSA. Finally, PSO is employed to find the optimum design using the optimal initial swarm. In order to reduce the computational cost of dam analysis subject to earthquake loading, weighted least squares support vector machine (WLS-SVM) is employed to accurately predict dynamic responses of gravity dams. Numerical results demonstrate the high performance of the hybrid meta-heuristic optimization for optimal shape design of concrete gravity dams. The solutions obtained by GSA-PSO are compared with those of GSA and PSO. It is revealed that GSA-PSO converges to a superior solution compared to GSA and PSO, and has a lower computation cost.

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This study focuses on the shape optimization of concrete gravity dams considering dam–water–foundation interaction and nonlinear effects subject to earthquake. The concrete gravity dam is considered as a two–dimensional structure involving the geometry and material nonlinearity effects. For the description of the nonlinear behavior of concrete material under earthquake loads, the Drucker–Prager model based on the associated flow rule is adopted in this study. The optimum design of concrete gravity dams is achieved by the hybrid of an improved gravitational search algorithm (IGSA) and the orthogonal crossover (OC), called IGSA–OC. In order to reduce the computational cost of optimization process, the support vector machine approach is employed to approximate the dam response instead of directly evaluating it by a time–consuming finite element analysis. To demonstrate the nonlinear behavior of concrete material in the optimum design of concrete gravity dams, the shape optimization of a real dam is presented and compared with that of dam considering linear effect.

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Reliable and accurate streamflow forecasting plays a crucial role in water resources systems (WRS) especially in dams operation and watershed management. However, due to the high uncertainty associated WRS components and nonlinear nature of streamflow generations, the realistic streamflow forecasts is still one of the most challenging issue in WRS. This paper aimed to forecast one-month ahead streamflow of Karun river (Iran) by coupling an artificial neural network (ANN) with an improved binary version of gravitational search algorithm (IBGSA), named ANN- IBGSA. To this end, the best lag number for each predictor at Poleshaloo station was firstly selected by auto-correlation function (ACF). The ANN-IBGSA was used to minimize the sum of RMSE and R2 and to identify the optimal predictors. Finally, to characterize the hydro-climatic uncertainties associated with the selected predictors, an
implicit approach of Monte-Carlo simulation (MCS) was applied. The ACF plots indicated a significant correlation up to a lag of two months for the input predictors. The ANN-IBGSA identified the Tmean (t-1), Q(t-1) and Q(t) as the best predictors. Findings demonstrated that the ANN-IBGSA forecasts were considerably better than those previously carried out by researchers in 2013. The average improvement values were 9.91%, 11.85% and 9.13% for RMSE, R2 and MAE, respectively. The Monte-Carlo simulations demonstrated that all of forecasted values lie within the 95% confidence intervals.
 

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Presently, the introduction of intelligent models to optimize structural problems has become an important issue in civil engineering and almost all other fields of engineering. Optimization models in artificial intelligence have enabled us to provide powerful and practical solutions to structural optimization problems. In this study, a novel method for optimizing structures as well as solving structure-related problems is presented. The main purpose of this paper is to present an algorithm that addresses the major drawbacks of commonly-used algorithms including the Grey Wolf Optimization Algorithm (GWO), the Gravitational Search Algorithm (GSA), and the Particle Swarm Optimization Algorithm (PSO), and at the same time benefits from a high convergence rate. Also, another advantage of the proposed CGPGC algorithm is its considerable flexibility to solve a variety of optimization problems. To this end, we were inspired by the GSA law of gravity, the GWO's top three search factors, the PSO algorithm in calculating speed, and the cellular machine theory in the realm of population segmentation. The use of cellular neighborhood reduces the likelihood of getting caught in the local optimal trap and increases the rate of convergence to the global optimal point. Achieving reasonable results in mathematical functions (CEC 2005) and spatial structures (with a large number of variables) in comparison with those from GWO, GSA, PSO, and some other common heuristic algorithms shows an enhancement in the performance of the introduced method compared to the other ones.