IGOA-MLP dynamic prediction model for simulation parameters of high core rockfill dam construction under transfer learning framework

For construction simulation of high core rockfill dam, the parameters are the key to ensuring its accuracy. However, existing parameter prediction methods used historical data and ignore the differences between the construction processes of different layers, and there is often insufficient or missing data at the beginning of a new layer. In addition, the parameters are affected by many factors such as meteorological conditions and operating state of the machine. To solve the above problems, this paper takes advantage of the transfer learning' s capability of modeling with small samples through knowledge transfer and considers the quantitative influence of various factors. An improved multi- layer perceptron dynamic prediction model (1GOA-MLP) is proposed for construction simulation parameters of high core rockfill dam under the framework of transfer learning. Firstly, the IGOA-MLP prediction model is established that considering the influence of multiple factors. The grasshopper optimization algorithm is improved(1GOA) by nonlinear reduction factor and Cauchy-Gaussian hybrid mutation mode, and the efficient global optimal search capability of IGOA is utilized to optimize the hyperparameters of multi—layer perceptron (MLP). Secondly, the transfer learning strategy is introduced to realize the knowledge transfer between the historical and new conditions and solve the problem of insufficient or missing data in the new conditions. The training set is divided into source domain and target domain, and an adaptive layer is added to the hidden layer of MLP to represent the difference between source domain data and target domain data. A case study shows that compared with other machine learning methods such as MLP model and IGOA-MLP model without transfer learning, the mean absolute percentage error(MAPE) of the proposed method is reduced by 54.68% and 40.57%, respectively. It is proved that the proposed model can predict the parameters of construction simulation more accurately and provide a reliable data basis for simulation. © 2023 International Research and Training Center on Erosion and Sedimentation and China Water and Power Press. All rights reserved.