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The evaluation of seismic slope performance during earthquakes is important, because the failure of slope (such as an earth dam, natural slope, or constructed earth embankment) can result in significant financial losses and human. It is important, therefore, to be able to forecast such displacements induced by earthquake. However, the traditional forecasting methods, such as empirical formulae, are inaccurate because most of them do not take into consideration all the relevant factors. In this paper, new intelligence method, namely relevance vector regression (RVR) optimized by dolphin echolocation (DE) and grey wolf optimizer (GWO) algorithms is introduced to forecast the earthquake induced displacements (EID) of slopes. The DE and GWO algorithms is combined with the RVR for determining the optimal value of its user-defined paramee RVR. The performances of the proposed predictive models were examined according to two performance indices, i.e., coefficient of determination (R²) and mean square error (MSE). The obtained results of this study indicated that the RVR-GWO model is a reliable method to forecast EID with a higher degree of accuracy (MSE= 0.0160 and R²= 0.9955).

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Passive systems are preferred tools for seismic control of buildings challenged by probabilistic nature of the input excitation. However, other types of uncertainty still exist in parameters of the control device even when optimally tuned. The present work concerns optimal design of multiple-tuned-mass-damper embedded on a shear building by a number of meta-heuristics. They include well-known genetic algorithm and particle swarm optimization as well as more recent gray wolf optimizer and its hybrid method embedding swarm intelligence. The study is two-fold: first, optimal designs by different meta-heuristics are compared concerning their reduction in structural seismic responses; second, the effect of uncertainty in Multi-Tuned-Mass-Damper parameters, is studied offering new reliability-based curves. Monte Carlo Simulation is employed to evaluate failure probabilities. A variety of structural responses are assessed against seismic excitation including maximal displacement, velocity and acceleration. It is declared that the best algorithm for efficiency and effectiveness has not coincided the best based on the reliability traces. Such traces also show that in a specific range of limit-states, algorithm selection has a serious effect on the reliability results. It was found even more than 35% and depends on the response type.  

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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.