Dam Deformation Interpretation and Prediction Based on a Long Short-Term Memory Model Coupled with an Attention Mechanism

Su, Yan; Weng, Kailiang; Lin, Chih-Hong; Chen, Zeqin

doi:10.3390/app11146625

Cited by 17 publications

(8 citation statements)

References 26 publications

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“…If two words are in a similar context, their semantics are also similar. erefore, the distributed representation of the vector is different from the discrete representation, which is a vector representation that uses a neural network to convert words into continuous values [13]. Researchers can represent the semantic similarity between…”

Section: Distributed Representationmentioning

confidence: 99%

Automatic Scoring Model of Japanese Interpretation Based on Semantic Scoring

Wang

2022

Mathematical Problems in Engineering

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In order to improve the current level of Japanese teaching and the difficulty of non-standard Japanese spoken language, the author proposes a method for the study of the automatic scoring model of Japanese ants for scoring. The author introduces a semantic scoring model that integrates the long short-term memory neural network and self-attention mechanism, which can be applied to keyword scoring and sentence semantic scoring. The scoring principle of the model is as follows: firstly, extract the word and sentence features and represent them in a vectorized form, then use a bidirectional long short-term memory neural network to optimize the feature vector, and then use the self-attention mechanism to obtain the semantic features of the word or sentence. Finally, the semantic score is calculated by a simple neural network. Experiments show that compared with the semantic scoring model based on a stretchable recursive autoencoder that performs better in semantic scoring, the average correlation between this model and the original score is 0.444; the lowest rate of agreement with the original score is 95%; and the highest rate of agreement with adjacent ones is 74%. The automatic scoring model for Japanese interpreting with semantic scoring is proved to be practical and has excellent results.

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Section: Distributed Representationmentioning

confidence: 99%

Automatic Scoring Model of Japanese Interpretation Based on Semantic Scoring

Wang

2022

Mathematical Problems in Engineering

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“…In recent years, a variety of machine learning architectures have been used in the field of dam safety monitoring, such as the autoregressive integrated moving average (ARIMA) algorithm [8], the support vector machine (SVM) algorithm [9], the artificial neural network (ANN) algorithm [10][11][12], and the random forest (RF) algorithm [13,14], etc. These algorithms can predict dam displacement with reasonable accuracy; among them, the ANN algorithm illustrates superior performance in dealing with nonlinear problems [15,16].…”

Section: Introductionmentioning

confidence: 99%

Variation Trend Prediction of Dam Displacement in the Short-Term Using a Hybrid Model Based on Clustering Methods

Lin,

Zou,

Lai

et al. 2023

Applied Sciences

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The deformation behavior of a dam can comprehensively reflect its structural state. By comparing the actual response with model predictions, dam deformation prediction models can detect anomalies for effective advance warning. Most existing dam deformation prediction models are implemented within a single-step prediction framework; the single-time-step output of these models cannot represent the variation trend in the dam deformation, which may contain important information on dam evolution during the prediction period. Compared with the single value prediction, predicting the tendency of dam deformation in the short term can better interpret the dam’s structural health status. Aiming to capture the short-term variation trends of dam deformation, a multi-step displacement prediction model of concrete dams is proposed by combining the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, the k-harmonic means (KHM) algorithm, and the error minimized extreme learning machine (EM-ELM) algorithm. The model can be divided into three stages: (1) The CEEMDAN algorithm is adopted to decompose dam displacement series into different signals according to their timing characteristics. Moreover, the sample entropy (SE) method is used to remove the noise contained in the decomposed signals. (2) The KHM clustering algorithm is employed to cluster the denoised data with similar characteristics. Furthermore, the sparrow search algorithm (SSA) is utilized to optimize the KHM algorithm to avoid the local optimal problem. (3) A multi-step prediction model to capture the short-term variation of dam displacement is established based on the clustered data. Engineering examples show that the model has good prediction performance and strong robustness, demonstrating the feasibility of applying the proposed model to the multi-step forecasting of dam displacement.

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“…Salazar et al [25] detected abnormal values early by an enhanced regression tree, applying to a 100 m high arch dam. Su et al [26] used the density-based spatial clustering of applications with noise (DBSCAN) to identify and filter abnormal values of monitoring equipment and reduce random errors. Cheng et al [27] identified abnormal uplift pressure data of the gravity dam by the kernel density estimation method.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Anomaly Identification of Uplift Pressure Monitoring Data and Structural Diagnosis of Concrete Dam

Zhao

et al. 2022

Applied Sciences

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As an essential load of the concrete dam, the abnormal change of uplift pressure directly threatens the safety and stability of the concrete dam. Therefore, it is of great significance to accurately and efficiently excavate the hidden information of the uplift pressure monitoring data to clarify the safety state of the concrete dam. Therefore, in this paper, density-based spatial clustering of applications with noise (DBSCAN) method is used to intelligently identify the abnormal occurrence point and abnormal stable stage in the monitoring data. Then, an application method of measured uplift pressure is put forward to accurately reflect the spatial distribution and abnormal position of uplift pressure in the dam foundation. It is easy to calculate and connect with the finite element method through self-written software. Finally, the measured uplift pressure is applied to the finite element model of the concrete dam. By comparing the structural behavior of the concrete dam under the design and measured uplift pressure, the influence of abnormal uplift pressure on the safety state of the concrete dam is clarified, which can guide the project operation. Taking a 98.5 m concrete arch dam in western China as an example, the above analysis ideas and calculation methods have been verified. The abnormal identification method and uplift pressure applying method can provide ideas and tools for the structural diagnosis of a concrete dam.

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Dam Deformation Interpretation and Prediction Based on a Long Short-Term Memory Model Coupled with an Attention Mechanism

Cited by 17 publications

References 26 publications

Automatic Scoring Model of Japanese Interpretation Based on Semantic Scoring

Automatic Scoring Model of Japanese Interpretation Based on Semantic Scoring

Variation Trend Prediction of Dam Displacement in the Short-Term Using a Hybrid Model Based on Clustering Methods

Intelligent Anomaly Identification of Uplift Pressure Monitoring Data and Structural Diagnosis of Concrete Dam

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