Prediction of arch dam deformation via correlated multi-target stacking

Chen, Siyu; Gu, Chongshi; Lin, Chaoning; Hariri-Ardebili, Mohammad Amin

doi:10.1016/j.apm.2020.10.028

Cited by 56 publications

(41 citation statements)

References 40 publications

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“…e single-point LSTM performs the worst in terms of the processing effect because it considers only the pattern of changes in measurement values while ignoring the correlation between the measurement points. In addition, it can be seen from Table 3 that the absolute errors of the outcome of PDS-FCM-LSTM treatment proposed in this study are all less significant than that specified for the water project (29), which demonstrates the effectiveness of the missing data processing method proposed in this study. Mathematical Problems in Engineering Mathematical Problems in Engineering…”

Section: E Data Processingmentioning

confidence: 69%

Processing Method of Missing Data in Dam Safety Monitoring

Wei

2021

Mathematical Problems in Engineering

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A large amount of data obtained by dam safety monitoring provides the basis to evaluate the dam operation state. Due to the interference caused by equipment failure and human error, it is common or even inevitable to suffer the loss of measurement data. Most of the traditional data processing methods for dam monitoring ignore the actual correlation between different measurement points, which brings difficulties to the objective diagnosis of dam safety and even leads to misdiagnosis. Therefore, it is necessary to conduct further study on how to process the missing data in dam safety monitoring. In this study, a data processing method based on partial distance combining fuzzy C-means with long short-term memory (PDS-FCM-LSTM) was proposed to deal with the data missing from dam monitoring. Based on the fuzzy clustering performed for the measurement points of the same category deployed on the dam, the membership degree of each measurement point to cluster center was described by using the fuzzy C-means clustering algorithm based on partial distance (PDS-FCM), so as to determine the clustering results and preprocess the missing data of corresponding measurement points. Then, the bidirectional long short-term memory (LSTM) network was applied to explore the pattern of changes of measurement values under identical clustering conditions, thus processing the data missing from monitoring effectively.

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Section: E Data Processingmentioning

confidence: 69%

Processing Method of Missing Data in Dam Safety Monitoring

Wei

2021

Mathematical Problems in Engineering

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“…Year Publishers Breskvar and Džeroski [19] 2021 IEEE Access Chen et al [4] 2021 Applied Mathematical Modelling Santana et al [5] 2021 Chemometrics and Intelligent Laboratory Systems Mastelini et al [2] 2020 Applied Soft Computing Pliakos and Vens [20] 2020 BMC Bioinformatics Wu and Lian [21] 2020 Proceedings of the International Joint Conference on Neural Networks Adıyeke and Baydoğan [13] 2020 Pattern Recognition Bessadok et al [22] 2020 Lecture Notes in Computer Science Liu et al [23] 2020 IEEE Access Mignone et al [24] 2020 Nature Scientific Reports Liu et al [25] 2020 Machine Learning for Pharma and Healthcare Applications RQ1: What are the latest forms of side information in MTP?…”

Section: Publicationsmentioning

confidence: 99%

“…As interactions between entities in the real world become increasingly complex, prediction tasks must be adept to handle such complexity without compromising performance. Recent applications of MTP published include prediction of protein functions in bioinformatics [3], prediction of arch dam deformation in mathematical modelling [4], prediction of soil properties in agriculture [5], prediction of cognitive decline in Alzheimer patients [6] prediction of wheat flour quality parameters [7], prediction of identifying learning styles [8], prediction of drug toxicity [9], prediction of cervical cancer [10] and prediction of wine category [11]. These studies discovered that using MTP instead of STP improves performance.…”

Section: Introductionmentioning

confidence: 99%

Utilising Target Adjacency Information for Multi-target Prediction

Maskat¹,

Musa

Ardi

et al. 2021

IJEEI

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“…Han et al [33] predicted the horizontal displacement of concrete-face rockfill dams using the statistically optimized back-propagation neural network model, which can overcome the shortcomings of the statistical model and back-propagation neural network model. Chen et al [34] proposed a novel deformation prediction model of arch dam via correlated multi-target stacking. Shi et al [35] developed a safety monitoring model for concrete face rockfill dam seepage with cracks considering the lagging effect using the radial basis function neural network.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Deformation Prediction Models of Wuqiangxi Concrete Gravity Dam Based on Monitoring Data

Yang¹,

Han²,

Kuang³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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The deformation prediction models of Wuqiangxi concrete gravity dam are developed, including two statistical models and a deep learning model. In the statistical models, the reliable monitoring data are firstly determined with Lahitte criterion; then, the stepwise regression and partial least squares regression models for deformation prediction of concrete gravity dam are constructed in terms of the reliable monitoring data, and the factors of water pressure, temperature and time effect are considered in the models; finally, according to the monitoring data from 2006 to 2020 of five typical measuring points including J 23 (on dam section 24 # ), J 33 (on dam section 4 # ), J 35 (on dam section 8 # ), J 37 (on dam section 12 # ), and J 39 (on dam section 15 # ) located on the crest of Wuqiangxi concrete gravity dam, the settlement curves of the measuring points are obtained with the stepwise regression and partial least squares regression models. A deep learning model is developed based on long short-term memory (LSTM) recurrent neural network. In the LSTM model, two LSTM layers are used, the rectified linear unit function is adopted as the activation function, the input sequence length is 20, and the random search is adopted. The monitoring data for the five typical measuring points from 2006 to 2017 are selected as the training set, and the monitoring data from 2018 to 2020 are taken as the test set. From the results of case study, we can find that (1) the good fitting results can be obtained with the two statistical models; (2) the partial least squares regression algorithm can solve the model with high correlation factors and reasonably explain the factors; (3) the prediction accuracy of the LSTM model increases with increasing the amount of training data. In the deformation prediction of concrete gravity dam, the LSTM model is suggested when there are sufficient training data, while the partial least squares regression method is suggested when the training data are insufficient.

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Prediction of arch dam deformation via correlated multi-target stacking

Cited by 56 publications

References 40 publications

Processing Method of Missing Data in Dam Safety Monitoring

Processing Method of Missing Data in Dam Safety Monitoring

Utilising Target Adjacency Information for Multi-target Prediction

Comparative Study on Deformation Prediction Models of Wuqiangxi Concrete Gravity Dam Based on Monitoring Data

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