Journal ArticleDOI
Performance-improved TSVR-based DHM model of super high arch dams using measured air temperature
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TLDR
In this paper , a measured air temperature-based Hydrostatic-thermal-time (HTT) displacement health monitoring (DHM) model of super high arch dams is proposed to fully explore the complex nonlinearity between dam displacement and its explanatory variables and to improve the predictive accuracy of the model.About:
This article is published in Engineering Structures.The article was published on 2022-01-01. It has received 15 citations till now. The article focuses on the topics: Displacement (psychology) & Hydrostatic equilibrium.read more
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Deformation similarity characteristics-considered hybrid panel model for multi-point deformation monitoring of super-high arch dams in operating conditions
TL;DR: In this article , the authors developed a hybrid model for multi-point deformation monitoring of super-high arch dams in operating conditions, and the confidence ellipsoid criteria are established by applying multivariate statistic and principle of small probability event.
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Segmented modeling method of dam displacement based on BEAST time series decomposition
TL;DR: In this article , a principal component analysis is used to extract the comprehensive displacement of multiple measuring points; then, the comprehensive displacements are decomposed into seasonal and trend parts by using the Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) method, and the change of its change law is quantitatively analyzed.
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Prediction for the Settlement of Concrete Face Rockfill Dams Using Optimized LSTM Model via Correlated Monitoring Data
TL;DR: A method based on an optimized long short-term memory (LSTM) model is proposed to predict the settlement of CFRDs, modeling multiple monitoring data series with strong correlation relationships simultaneously, indicating that the proposed method has a better prediction performance compared with the LSTM model, the back propagation neural network (BPNN) model, and the HST with single monitoring point.
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A mathematical-mechanical hybrid driven approach for determining the deformation monitoring indexes of concrete dam
TL;DR: In this article , a method for quantifying the uncertainty of mechanical parameters of the concrete dam based on prototype measured data, and puts forward a novel method for determining deformation monitoring indexes based on the quantification results of the uncertainty caused by deformation causes.
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Multi-arch dam safety evaluation based on statistical analysis and numerical simulation
TL;DR: In this article , the authors evaluated the dam safety by analyzing the measured displacements and simulating stresses in the concrete, and the results indicated that the dam is currently in an elastic state after the last reinforcement, temperature contributes the most to the displacement, and drastic fluctuation of temperature is the disadvantage factor for multi-arch dam safety.
References
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Grey Wolf Optimizer
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
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The Whale Optimization Algorithm
Seyedali Mirjalili,Andrew Lewis +1 more
TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.
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Twin Support Vector Machines for Pattern Classification
TL;DR: A binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems, each of which is smaller than in a conventional SVM, which shows good generalization on several benchmark data sets.
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How effective is the Grey Wolf optimizer in training multi-layer perceptrons
TL;DR: The statistical results prove the GWO algorithm is able to provide very competitive results in terms of improved local optima avoidance and a high level of accuracy in classification and approximation of the proposed trainer.
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TSVR: An efficient Twin Support Vector Machine for regression
TL;DR: A novel regressor that determines a pair of -insensitive up- and down-bound functions by solving two related SVM-type problems, each of which is smaller than that in a classical SVR.