Reliability Analysis of Concrete Gravity Dams Based on Least Squares Support Vector Machines with an Improved Particle Swarm Optimization Algorithm

The parameters of gravity dams and foundation materials objectively exhibit spatial variability due to environmental and load influences, which significantly affect the safety status of dam structures. Therefore, a safety risk analysis method for a gravity dam–foundation system based on random field theory is proposed in this paper. Spatial variabilities in materials are particularly considered by using the finite element method. Then, composite response surface equations for the performance function (PF) of strength and stability failure are established, and then, the system failure risk is obtained using the Monte Carlo method. The proposed method solves the problem wherein the effect of spatial variability on failure risk cannot be reflected accurately by the performance function of multi-element sliding paths, and the difficulties in solving the failure risk of the series–parallel system due to multiple failure paths and their complex correlations. The application of a gravity dam shows that the developed method overcomes the disadvantages of the traditional method, such as the homogenization of the spatially random characteristics of parameters and the overestimation of failure risk in the system due to large variance estimation.

Section: Introductionmentioning

confidence: 99%

A Method for Evaluating Systematic Risk in Dams with Random Field Theory

Ran,

Zhou,

Pei

et al. 2024

“…Figure 3 show the classification schematics of the same set of two-dimensional data under the one-vs-one and one-vs-all methods, respectively. Under the premise of classification by using the one-vs-all method, there exist two classes of transition areas between hyperplanes [12,13]. For these areas, some discriminative methods are proposed, such as a "voting" scheme, in which the area is marked as the category with the highest number of points [14].…”

Section: Introductionmentioning

confidence: 99%

Research on Tower Mechanical Fault Classification Method Based on Multiclass Central Segmentation Hyperplane Support Vector Machine Improvement Algorithm

Han

Wang

et al. 2023

In this paper, a classification recognition algorithm for tower mechanical faults is proposed, and a multiclass central segmentation hyperplane support vector machine (CSH-SVM) is proposed to improve the existing multiclass support vector machine for problems in which a certain sample satisfies multiple hyperplanes at the same time. The tilt angle change and wind direction data were extracted using the tilt sensors and anemometers attached to the tower, and the temperature and humidity sensors, as well as real-time rainfall and water accumulation information, were combined to construct a sample of the original dataset during the operation of the tower. The unbalanced samples were improved using the synthetic minority oversampling technique (SMOTE) algorithm to construct a balanced dataset suitable for machine learning and improve the prediction accuracy of machine learning. At the same time, the support vector machine hyperplane under the one-vs-all classification principle was additionally computed, and the new hyperplane was computed via the existing hyperplane not only to solve the classification problem of the transition area under the one-vs-all classification so that the samples located in this area no longer meet two hyperplane equations at the same time, but also to reduce the probability of incorrect classification to a certain extent. Through verification, CSH-SVM can classify 15 out of 77 misclassified samples into the correct category with slightly higher computational power than the traditional one-vs-all classification SVM, which can improve the classification prediction accuracy for unbalanced tower mechanical failure datasets and make an accurate judgment on the current state of the tower through the tower data as to when the tower may generate mechanical failure, thus reducing economic loss and personal safety threats.

“…The earthquake safety of those structures is an important issue, since damaged large dams have an extremely negative impact on social and economic systems across a sizable region, and often those damages are irrecoverable. Especially, the possibility of sliding during an earthquake is important in the safety assessment of a concrete gravity dam, in that sliding can cause the total failure of a dam [1,2].…”

Section: Introductionmentioning

confidence: 99%

A Fracture Model for Dynamic Sliding Safety Evaluation of a Concrete Dam Subjected to Seismic Excitation

Lee,

Lim

2023

The Sliding Safety Factor (SSF) is a crucial criterion for the sliding stability evaluation of concrete dam structures. A concrete gravity dam subjected to strong earthquakes undergoes progressive fractures, in addition to pre-existing fractures, at the dam–foundation interface, which causes a reduction in the shear strength against sliding. In this study, a new SSF is suggested to take account of the progressive fractured area at the dam–foundation interface. A contact and sliding model for the dam–foundation system is also suggested to compute the dynamically varying normal forces and sliding motions for the suggested SSF. To investigate the effect of the progressively fractured area on the sliding safety evaluation, the conventional, improved, and newly suggested SSFs are compared using the dynamic seismic analysis results of a concrete gravity dam. The conventional formulation of the SSF, in which the fractured area is not represented, yields extremely overestimated sliding safety judgements when a dam is subjected to strong earthquakes. On the other hand, the newly suggested SSF with the proposed contact–sliding model provides more realistic and conservative sliding safety evaluation results than the others.