Concrete arch dam behavior prediction using kernel-extreme learning machines considering thermal effect

Xi, Liu; Kang, Fei; Ma, Chuanbo; Zhang, Dongliang

doi:10.1007/s13349-020-00452-x

Cited by 18 publications

(9 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the physical properties of the sensitive grating materials used to make strain gauges, even if the strain gauges are mounted on a specimen without any external forces, the resistance value changes when the ambient temperature changes; this is called the temperature effect. When the measured specimen is subjected to both load and temperature, the output value of the strain gauge is not only related to the deformation of the measured specimen but also to the temperature, that is, the resistance change of the strain gauge is a function of temperature (T) and strain (ε), which can be expressed by Equation (10):…”

Section: Coupled Thermo-electro-elasticity Equationsmentioning

confidence: 99%

See 1 more Smart Citation

Calibration Experiment and Temperature Compensation Method for the Thermal Output of Electrical Resistance Strain Gauges in Health Monitoring of Structures

Jin

Fan

et al. 2023

Symmetry

View full text Add to dashboard Cite

Electrical resistance strain gauges are widely used in asymmetric structures for measurement and monitoring, but their thermal output in changing temperature environments has a significant impact on the measurement results. Since thermal output is related to the coefficient of thermal expansion of the strain gauge’s sensitive grating material and the measured object, the temperature self-compensation technique of strain gauges fails to eliminate the additional strain caused by temperature because it cannot match the coefficient of thermal expansion of various measured objects. To address this problem, in this study, the principle of the thermal output of electrical resistance strain gauges was analyzed, a calibration experiment for thermal output in the case of a mismatch between the coefficient of linear expansion of the measured object and the strain gauge grating material was conducted, and the mechanism for temperature influence on thermal output was revealed. A method was proposed to obtain the thermal output curves for different materials by using thermostats with dual temperatures to conduct temperature calibration experiments. A linear regression method was used to obtain a linear formula for the thermal output corresponding to each temperature. The thermal output conversion relationship was derived for materials with different coefficients of linear expansion. An in situ temperature compensation technique for electrical resistance strain gauges that separates the measured strain into thermal and mechanical strains was proposed. The results showed that the thermal output curve for the measured object can be calibrated in advance and then deducted from the measured strain, thus reducing the influence of temperature-induced additional strain on the mechanical strain. In addition, a new method was provided for the calculation of the thermal output among materials with similar coefficients of linear expansion, providing a reference for the health monitoring of asymmetric structures.

show abstract

Section: Coupled Thermo-electro-elasticity Equationsmentioning

confidence: 99%

“…If these temperature effects are not controlled or eliminated, especially over large temperature ranges, then the thermal output error may completely mask the true measured value. Therefore, it is necessary to use reasonable and effective temperature compensation measures to eliminate their effects [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Calibration Experiment and Temperature Compensation Method for the Thermal Output of Electrical Resistance Strain Gauges in Health Monitoring of Structures

Jin

Fan

et al. 2023

Symmetry

View full text Add to dashboard Cite

show abstract

“…Fig. 1 Burgers rheology model small probability method, confidence interval method, limit state method, structural analysis method and other methods [18,49,50]. In actual engineering, when using the abovementioned methods to formulate the monitoring indexes for rockfill dams and perform numerical simulations, the physical and mechanical parameters of the rockfill materials are mostly treated as fixed values.…”

Section: Monitoring Hybrid Index Determinationmentioning

confidence: 99%

“…Deterministic models can clearly reflect the working behaviour of the dam structure, but it is difficult to popularize such models in practical engineering due to a large number of structural calculations [15,16]. Dam safety monitoring models based on intelligent algorithms have the characteristics of high computational efficiency, strong adaptability and nonlinear mapping fitting ability, but they have the defects of local minimization, overfitting, and high parameter sensitivity and cannot explain the mechanisms of dam structural state changes; therefore, such models are not widely used in practical engineering [17,18]. In contrast, a hybrid model combines structural analysis and mathematical statistics.…”

Section: Introductionmentioning

confidence: 99%

A hybrid monitoring model of rockfill dams considering the spatial variability of rockfill materials and a method for determining the monitoring indexes

Ran

Yang

Zhang³

et al. 2022

J Civil Struct Health Monit

View full text Add to dashboard Cite

To further enhance the prediction accuracy of the monitoring model and improve the rationality of the method for determining the monitoring indexes, this paper considers the impact of the spatial variability of rockfill materials on the displacement of the dam. Based on the stochastic finite element method (SFEM), a new rockfill dam displacement monitoring hybrid model is established, and a method to determine the hybrid index for rockfill dam displacement monitoring is proposed by multiindex amalgamation. First, the SFEM is used to simulate the spatial variability of the rockfill mechanical parameters in the process of calculating the water pressure component and the time-dependent component. On this basis, the expression of each component is fitted to construct a stochastic finite element method hybrid model (SFEMH model) of the rockfill dam. Subsequently, by predicting the displacement of the dam and separating each displacement component, the displacement monitoring hybrid index of the rockfill dam is determined by combining the confidence interval method and the most unfavourable components. Finally, the SFEMH model and hybrid index are applied to analyse the actual monitoring data of a rockfill dam. The results show that the proposed model and the method for determining the displacement monitoring hybrid index are scientific and reasonable. The prediction accuracy and effect of the proposed novel model outperform those of other methods in terms of many evaluation indicators, and the reliability of the monitoring indexes is significantly improved. The proposed hybrid model and hybrid index provide a new method for the efficient operation management and accurate safety performance evaluation of rockfill dams.

show abstract

“…Yang et al [36] presented an intelligent singular value diagnostic method based on convolutional neural network for concrete dam deformation monitoring. Liu et al [37] investigated the applicability of the kernel-extreme learning machines-based model considering the thermal effect on the behavior prediction of concrete arch dams. Chen et al [38] builded the structural health monitoring framework of concrete dam displacement and mined the effects of hydrostatic, seasonal and irreversible time components on dam deformation by combining with relevance vector machine, multi-kernel technique, hydrostatic-season-time statistical model and parallel Jaya algorithm.…”

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

View full text Add to dashboard Cite

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.

show abstract

Concrete arch dam behavior prediction using kernel-extreme learning machines considering thermal effect

Cited by 18 publications

References 39 publications

Calibration Experiment and Temperature Compensation Method for the Thermal Output of Electrical Resistance Strain Gauges in Health Monitoring of Structures

Calibration Experiment and Temperature Compensation Method for the Thermal Output of Electrical Resistance Strain Gauges in Health Monitoring of Structures

A hybrid monitoring model of rockfill dams considering the spatial variability of rockfill materials and a method for determining the monitoring indexes

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

Contact Info

Product

Resources

About