A novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm

Chen, Youliang; Zhang, Xiangjun; Karimian, Hamed; Xiao, Gang; Huang, Jingsong

doi:10.2166/hydro.2021.178

Cited by 23 publications

(12 citation statements)

References 48 publications

(37 reference statements)

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“…Besides and variables, each hazard contains a list of potentially affected DRS’ subsystems. This attribute is assessed using historical data if there are documented historical failures, and/or detailed numerical and theoretical analyses of the DRSs behavior (Rehamnia et al 2020 ; Chen et al 2021 ; Rakić et al 2022 ; Nafchi et al 2021a , b ; Tang et al 2022 ). It should be noted that the hazard database contains an event to describe normal conditions (no hazard), which has the highest occurrence probability.…”

Section: Methodsmentioning

confidence: 99%

Failure Conditions Assessment of Complex Water Systems Using Fuzzy Logic

Milašinović

Ivetić

Stojković³

et al. 2023

Water Resour Manage

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Climate change, energy transition, population growth and other natural and anthropogenic impacts, combined with outdated (unfashionable) infrastructure, can force Dam and Reservoir Systems (DRS) operation outside of the design envelope (adverse operating conditions). Since there is no easy way to redesign or upgrade the existing DRSs to mitigate against all the potential failure situations, Digital Twins (DT) of DRSs are required to assess system’s performance under various what-if scenarios. The current state of practice in failure modelling is that failures (system’s not performing at the expected level or not at all) are randomly created and implemented in simulation models. That approach helps in identifying the riskiest parts (subsystems) of the DRS (risk-based approach), but does not consider hazards leading to failures, their occurrence probabilities or subsystem failure exposure. To overcome these drawbacks, this paper presents a more realistic failure scenario generator based on a causal approach. Here, the novel failure simulation approach utilizes fuzzy logic reasoning to create DRS failures based on hazard severity and subsystems’ reliability. Combined with the system dynamics (SD) model this general failure simulation tool is designed to be used with any DRS. The potential of the proposed method is demonstrated using the Pirot DRS case study in Serbia over a 10-year simulation period. Results show that even occasional hazards (as for more than 97% of the simulation there were no hazards), combined with outdated infrastructure can reduce DRS performance by 50%, which can help in identifying possible “hidden” failure risks and support system maintenance prioritization. Graphical Abstract

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Section: Methodsmentioning

confidence: 99%

Failure Conditions Assessment of Complex Water Systems Using Fuzzy Logic

Milašinović

Ivetić

Stojković³

et al. 2023

Water Resour Manage

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“…Kang et al [13] presented a dam health monitoring model based on kernel extreme learning machines. Chen et al [14] combined the advantages of extreme learning machines and elastic networks for predicting dam deformation. Su et al [15] used rough set theory and a support vector machine to build the early-warning models of dam safety.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Monitoring Model for Displacement of the Concrete Dam

Cui

et al. 2023

Sustainability

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For the structural health diagnostic of concrete dams, the mathematical monitoring model based on the measured deformation values is of great significance. The main purpose of this paper is to reconstruct the ageing component and the temperature component in the traditional Hydraulic-Seasonal-Time (HST) hybrid model by combining the measured values. On the one hand, a better mathematical model for the ageing displacement of concrete dams is proposed combined with the Burgers model to separate the instantaneous elastic hydraulic deformation and the hysteretic hydraulic deformation, and then it subsumes the latter into the ageing deformation to describe its reversible component. According to the Burgers model, the inverted elastic modulus of the Jinping-Ⅰ concrete dam is 46.5 GPa, which is closer to the true value compared with the HST model. On the other hand, the kernel principal component analysis (KPCA) method is used to extract the principal components of the dam thermometers for replacing the period harmonic thermal factor. A multiple linear regression (MLR) model is established to fit the measured displacement of the concrete arch dam and to verify the accuracy of the proposed hybrid model. The results show that the proposed model reaches higher accuracy than the traditional HST hybrid model and is helpful to improve the interpretation of the separated displacement components of the concrete dams.

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“…With the development of computer technology, machine learning algorithms such as support vector machine [13], extreme learning machine [14], artificial neural network [15,16] are widely used in deformation prediction models. However, these machine learning algorithms have various shortcomings, such as over fitting, easy to fall into local extremum and difficult to determine model super parameters.…”

Section: Introductionmentioning

confidence: 99%

LSTM-Based Deformation Prediction Model of the Embankment Dam of the Danjiangkou Hydropower Station

Wang¹,

Yang²,

Chen³

et al. 2022

Water

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The Danjiangkou hydropower station is a water source project for the middle line of the South-to-North Water Transfer Project in China. The dam is composed of riverbed concrete dam and earth rock dam on both banks, with a total length of 3442 m. Once the dam is wrecked, it will yield disastrous consequences. Therefore, it is very important to evaluate the dam safety behavior in time. Based on the long-term and short-term memory (LSTM) network, the deformation prediction models of the embankment dam of the Danjiangkou hydropower station are constructed. The models contain two LSTM layers, adopt the rectified linear unit function as the activation function and determine the super parameters of the models with Bayesian optimization algorithm. According to the settlement monitoring data of LD12ZT01 measuring point (dam crest 0 + 648) on the left bank of the embankment dam of the Danjiangkou hydropower station from July 2013 to March 2022, the LSTM and bidirectional LSTM models are constructed. In total, 80% of the monitoring data are taken as the training set data and 20% of the monitoring data are taken as the test set data. The mean absolute error, root mean square error and mean square error for the test set are 0.42978, 0.56456 and 0.31873 for partial least squares regression (PLSR), 0.35264, 0.47561 and 0.22621 for LSTM and 0.34418, 0.45400 and 0.20612 for bidirectional LSTM, respectively. The results show that the bidirectional LSTM model can obtain better deformation prediction value than the LSTM model and the PLSR. Then, the bidirectional LSTM model is used to predict the settlement value of LD16YT01 measuring point (dam crest 0 + 658) on the right bank, and the mean absolute error, root mean square error and mean square error for the test set are 0.5425, 0.66971 and 0.4520, respectively. This shows the bidirectional LSTM model can effectively predict the settlement value of the embankment dam of the Danjiangkou hydropower station.

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A novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm

Cited by 23 publications

References 48 publications

Failure Conditions Assessment of Complex Water Systems Using Fuzzy Logic

Failure Conditions Assessment of Complex Water Systems Using Fuzzy Logic

A New Hybrid Monitoring Model for Displacement of the Concrete Dam

LSTM-Based Deformation Prediction Model of the Embankment Dam of the Danjiangkou Hydropower Station

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