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The original version of this article unfortunately contained a mistake in the acknowledgement statement. It should read as follow "This paper presents work funded by the National Natural Science Foundation of China (No. 61203179), Excellent Youth Foundation of Science & Technology Innovation of Henan Province (No. 184100510001), and Key scientific research projects of Henan Province (No. 182102210457). Also, we would like to thank the Collaborative Innovation Center for Aviation Economy Development of Henan Province.". The oricd of the corresponding author is revised to http://orcid.org/0000-0001-9143-2922. Also the e-mail of the corresponding author is revised to zgh_hust@qq.com. [ABSTRACT FROM AUTHOR]

The mechanical parameter inversion model is an essential part of ensuring dam health; it provides a parametric basis for assessing the safe operational behavior of dams using numerical simulation techniques. Due to the complicated nonlinear mapping relationship between the roller compacted concrete (RCC) dam's mechanical parameters and various environmental quantities, as well as conventional statistical models, machine learning methods, and neural networks fail to consider the inputs of fuzzy uncertainty factors. Therefore, the accuracy, efficiency, and stability of inversion models are usually affected by their modeling methods. In this paper, a novel hybrid model for mechanical parameter inversion of an RCC dam is proposed, which uses a radial basis function neural network (RBFNN) to establish the nonlinear mapping relationship between the dam mechanical parameters and the environmental quantities, and the modified particle swarm optimization (PSO) algorithm is used to find the optimal parameters of the model. The modified PSO algorithm makes the inertia weight ω dynamically adjust with the number of iterations to improve the randomness and diversity of the particle population, and population crossover and mutation are introduced to improve the global search ability and convergence speed of the algorithm. The proposed hybrid model is verified and comparatively analyzed by four typical mathematical test functions, and the results show that the proposed model exhibits good performance in parameter inversion accuracy, convergence speed, stability and robustness. Finally, the model is applied to the mechanical parameter inversion analysis of an RCC gravity dam in Henan Province in China. The results show that the proposed model is feasible and reasonable for practical engineering applications, and the relative error between the results obtained by inputting the inverted parameters into the numerical model and monitoring data was within 10%. The methodology derived from this study can provide technical support and a reference for the mechanical parameter inversion analysis of similar dam projects. [ABSTRACT FROM AUTHOR]

For enhancing the prediction accuracy of power load forecasting, a support vector machine (SVM) prediction model based on wavelet transform and the mutant fruit fly parameter optimization intelligent algorithm (WT-MFOA-LSSVM) was presented. The load data were pretreated by wavelet transform, and the load curves were decomposed into different scales, in order to strengthen the regularity and randomness of historical data. Aiming at overcoming the problems of low convergence precision and easily relapsing into local extreme in basic fruit fly optimization algorithm (FOA), judge whether the intelligent algorithm was trapped in local extreme from the fitness variance of the population and the current optimal. Then, it was conducted by optimal individual perturbation and Gauss mutation operation and the mutant fruit flies were second times optimized, which made the accuracy of prediction model be obviously enhanced. The next few days of historical load data of a certain area of Henan Province, China, in 2015 were predicted by using WT-MFOA-LSSVM, and then the prediction results were compared with the results predicted by the SVM model and by the SVM model based on particle swarm optimization model. The results showed that WT-MFOA-LSSVM had high precision in short term load forecasting, and it had a very good practical significance. [ABSTRACT FROM AUTHOR]