Dam Safety Evaluation Based on Interval-Valued Intuitionistic Fuzzy Sets and Evidence Theory

Shu, Xiaosong; Bao, Tengfei; Li, Yangtao; Zhang, Kang; Wu, Bangbin

doi:10.3390/s20092648

Cited by 16 publications

(8 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many experts and scholars at home and abroad have conducted in-depth research and discussion on the dam body stability of wet-draining tailings ponds, and their research focuses mainly on three aspects: seepage stability, dam body static stability, and dam body dynamic stability [3]. In the context of strengthening water conservancy construction, improving the safety of hydraulic structures, especially improving the level of dam safety monitoring, and ensuring the safety of reservoirs and dams are the top priorities related to national interests and social stability [4]. The establishment of an automatic monitoring system for dam safety can shorten the data collection cycle, improve the work efficiency of dam observation, and reduce labor intensity [5].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Uncertainty Analysis of Key Influencing Factors on Stability of Tailings Dam Body

Qian

Kepeng

2023

Journal of Sensors

View full text Add to dashboard Cite

With the continuous expansion of the mining scale of mineral resources, many tailings are produced. In order to avoid the impact of harmful substances in the tailings on the residents around the tailings pond, it is necessary to explain the stability improvement. The dam safety monitoring system is mainly composed of observation sensors, telemetry data acquisition module, industrial control network, and automatic monitoring system. Through the work of the computer, the dam observation data can be automatically collected, processed, analyzed, and calculated. This paper adopts the automated system that makes preliminary judgments and graded alarms on whether the dam’s behavior is normal or not to provide early safety warning reports for monitoring objects. In this paper, combined with a specific example of a tailings reservoir dam body, the comprehensive uncertainty method is used to analyze factors such as the height of the tailings dam, the height of the wetting line, the cohesion of the tailings soil layer, and the internal friction angle, and the stability of the dam body is calculated. The results show that the sensitivity order of the dam stability safety factor K to each factor is as follows: internal friction angle of the tailing silt layer > the height of the wetting line > dam body height > the cohesion of the tailing silt layer. The most dangerous slip surface will jump at the 19-level subdam of the tailings pond. The remediation measures are of great significance for maintaining the ecology around the tailings pond and ensuring the personal safety of residents.

show abstract

Section: Introductionmentioning

confidence: 99%

[Retracted] Uncertainty Analysis of Key Influencing Factors on Stability of Tailings Dam Body

Qian

Kepeng

2023

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…Despite numerous researches in this area, there are still many unanswered questions: which method is the best, how to automate all processes, and so on. According to the application type, there are two main directions of technical diagnostics: monitoring and diagnostics of rotating machinery, [1][2][3][4] and structural health monitoring (SHM), which is defined as a process of implementing a damage detection and characterisation strategy for various engineering structures. [5][6][7][8] According to the used technique, diagnostic methods can be divided into: destructive technique when maximum load or fatigue test of samples are performed 9,10 ; and non-destructive technique when the state of the structure is evaluated without causing damage to it.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is necessary to diagnose dam behavior, identify the anomaly signals, and find out the potential risks accurately. 3 In the dam monitoring system, many kinds of monitoring sensors are embedded to monitor the working condition of the dam. To reflect the dam behavior comprehensively, these monitoring sensors are embedded in different locations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unsupervised dam anomaly detection with spatial–temporal variational autoencoder

Shu

Bao

Zhou

et al. 2022

Structural Health Monitoring

Self Cite

View full text Add to dashboard Cite

The anomaly detection and health monitoring of dams have attracted increasing attention. To detect the temporal and spatial anomalies of the dam, a novel spatial–temporal variational autoencoder is proposed. The proposed model is based on the sequential variational autoencoder, and its backbone is fulfilled by the recurrent neural network and graph convolutional network to capture the temporal and spatial features in both the generative and inference models. To obtain a normal pattern, we made an assumption that the normal values should be temporally smooth and spatially similar. Then, the smoothness and similarity-inducing operations are used in the framework of the proposed model. Through the addition of smoothness and similarity losses in sequential variational autoencoder, the proposed model can obtain a temporally smooth and spatially similar pattern. For verification, an arch dam is taken as an example. Through comparison among six baseline models, the proposed model detects the temporal and spatial anomalies accurately and stably.

show abstract

“…In the aspect of information fusion, uncertainty theories such as evidence theory, granular computing, and rough set theory have been employed. Shu et al 11 combined the Dempster's combination rule (DST) and interval-valued intuitionistic fuzzy set (IVIFS) for dam safety assessment. In Shu's method, the IVIFS is employed to integrate the homologous information.…”

Section: Introductionmentioning

confidence: 99%

Dam safety assessment through data-level anomaly detection and information fusion

Zhou

Shu

Bao

et al. 2022

Structural Health Monitoring

Self Cite

View full text Add to dashboard Cite

The anomaly detection and safety assessment of dams have attracted increasing attentions. To assess the safety of dams, a novel dam safety assessment model is proposed. The safety assessment model consists of data-level anomaly detection and information fusion. In the anomaly detection, a novel model based on the generative adversarial network and variational autoencoder is proposed. Through the stepwise training, the anomaly scores with reconstruction and discrimination losses can be obtained. Considering the multi-source and complexity of information system, a two-step fusion model is proposed. In the multi-source and multi-attribute fusion, the neighborhood rough set model is employed to construct the neighborhood granular structure. Through the idea of granular computing, the uncertainty of these sources and attributes is measured. Then the Sup-Inf fusion functions and fusion weights are employed to integrate the information. Finally, the anomaly scores with interval form are obtained. For verification, an arch dam is taken as an example. Through the analysis of detection and fusion results, the proposed model can detect the abnormal signals accurately and have the comprehensive assessment of the dam.

show abstract

Dam Safety Evaluation Based on Interval-Valued Intuitionistic Fuzzy Sets and Evidence Theory

Cited by 16 publications

References 25 publications

[Retracted] Uncertainty Analysis of Key Influencing Factors on Stability of Tailings Dam Body

[Retracted] Uncertainty Analysis of Key Influencing Factors on Stability of Tailings Dam Body

Unsupervised dam anomaly detection with spatial–temporal variational autoencoder

Dam safety assessment through data-level anomaly detection and information fusion

Contact Info

Product

Resources

About