2024年9月23日 · To accurately predict the deformation trends of concrete dams, this paper introduces the ResNet-GRU-SGWO model. The model utilizes SVMD to denoise the displacement data of dams, aiming to eliminate the interference of nonlinear features within …

2024年11月1日 · To enhance the depiction of the evolving deformation response characteristics of concrete dam structures over time and to adhere to the precision criteria for analyzing dam monitoring data, a deformation monitoring model integrated with machine learning has emerged and experienced rapid advancement.

2023年6月21日 · In this study, three models, namely SSA–LSTM, BP, and LSTM, were used to construct a deformation prediction model for four measurement points of a concrete gravity dam, and the following conclusions were drawn by comparing …

2024年10月24日 · Ren et al. proposed a prediction model for concrete dam deformation by integrating encoder–decoder architecture, attention mechanism, and LSTM neural network.

2024年11月21日 · Accurate prediction of concrete dam deformation is essential for ensuring structural safety and operational efficiency. This study presents a novel approach for monitoring and predicting concrete dam deformation using observed air temperature data, intelligent optimization, and machine learning techniques.

2020年10月1日 · This paper uses open-source deep learning framework TensorFlow as the platform and uses the mature convolutional neural network technology in deep learning theory to establish the concrete dam deformation safety prediction model based on a deep learning.

2019年10月31日 · An engineering project shows that the concrete dam deformation prediction models based on RS-LSTM can quantitatively obtain the representative factors that affect dam deformation and the...

2024年5月17日 · Utilizing monitoring data from a mega concrete arch dam, we evaluate and discuss the effects of data mining, modeling accuracy for dam behavior, robustness against data pollution, and sensitivity to anomalies. Comparisons with classical benchmarks demonstrate the performance of the proposed model for the dam.

2025年1月1日 · Concrete dams, as one of the most widely used dam types in hydraulic engineering construction, account for more than 60 % of all dams over 200 m high globally [1]. Serving as crucial public infrastructures, concrete dams offer substantial economic benefits in flood control, power generation, water supply, and irrigation.

In this paper, an extreme learning machine (ELM)‐based health monitoring model is proposed for displacement prediction of gravity dams. ELM is one type of feedforward neural networks with a single layer of hidden nodes, where the weights connecting inputs to …