Law and Early Warning of Vertical Sluice Cluster Displacements in Soft Coastal Soil

Yang, Xingtuan; Yuan, Cong; Hou, Miao; Zhou, Chengyang; Ju, Yan; Qi, Fei

doi:10.1007/s12205-022-0113-6

Cited by 2 publications

(2 citation statements)

References 44 publications

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“…In order to improve the accuracy and robustness of the statistical model, scholars introduced some artificial intelligence methods [10,11], including artificial neural networks [12,13], support vector machines [14,15], and deep learning model [16,17], into the analysis of dam deformation. However, because it is a low-head hydraulic structure, the safety of the sluice is easily overlooked without adequate attention [18]. Although the deformation analysis method of a concrete dam can be referenced for the sluice, there exist some significant differences in their deformation characteristics.…”

Section: Introductionmentioning

confidence: 99%

A Deformation Analysis Method for Sluice Structure Based on Panel Data

Ma,

Lou,

Shen

et al. 2024

Water

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Deformation, as the most intuitive index, can reflect the operation status of hydraulic structures comprehensively, and reasonable analysis of deformation behavior has important guiding significance for structural long-term service. Currently, the health evaluation of dam deformation behavior has attracted widespread attention and extensive research from scholars due to its great importance. However, given that the sluice is a low-head hydraulic structure, the consequences of its failure are easily overlooked without sufficient attention. While the influencing factors of the sluice’s deformation are almost identical to those of a concrete dam, nonuniform deformation is the key issue in the sluice’s case because of the uneven property of the external load and soil foundation, and referencing the traditional deformation statistical model of a concrete dam cannot directly represent the nonuniform deformation behavior of a sluice. In this paper, we assume that the deformation at various positions of the sluice consist of both overall and individual effects, where overall effect values describe the deformation response trend of the sluice structure under external loads, and individual effect values represent the degree to which the deformation of a single point deviates from the overall deformation. Then, the random coefficient model of panel data is introduced into the analysis of sluice deformation to handle the unobservable overall and individual effects. Furthermore, the maximum entropy principle is applied, both to approximate the probability distribution function of individual effect extreme values and to determine the early warning indicators, completing the assessment and analysis of the nonuniform deformation state. Finally, taking a project as an example, we show that the method proposed can effectively identify the overall deformation trend of the sluice and the deviation degree of each measuring point from the overall deformation, which provides a novel approach for sluice deformation behavior research.

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Section: Introductionmentioning

confidence: 99%

A Deformation Analysis Method for Sluice Structure Based on Panel Data

Ma,

Lou,

Shen

et al. 2024

Water

View full text Add to dashboard Cite

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“…He et al [27] proposed an integrated variable fuzzy evaluation model to evaluate the social and environmental impact of dam breaks. Yang et al [28] presented a systematic approach for analyzing the law and early warning of vertical displacements in sluice clusters located in coastal soft soil.…”

Section: Introductionmentioning

confidence: 99%

Vibration Prediction and Evaluation System of the Pumping Station Based on ARIMA–ANFIS–WOA Hybrid Model and D-S Evidence Theory

Wang

Zhang

Yin

2023

Water

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Research on the vibration response prediction and safety early warning is of great significance to the operation and management of pumping station engineering. In the current research, a hybrid prediction method was proposed to predict vibration responses of the pumping station based on a single model of autoregressive integrated moving average (ARIMA), a combined model of the adaptive network-based fuzzy inference system (ANFIS) and whale optimization algorithm (WOA). The performance of the developed models was studied based on the effective stress vibration data of the blades in a shaft tubular pumping station. Then, the D-S evidence theory was adopted to perform safety early warning of the operation state by integrating the displacement, velocity, acceleration and stress indicators of the vibration responses of the pumping station. The research results show that the proposed prediction model ARIMA–ANFIS–WOA exhibited better accuracy in obtaining both linear and nonlinear characteristics of vibration data than the single prediction model and hybrid model with different optimization algorithms. The D-S evidence fusion results quantitatively demonstrate the safe operation state of the pumping station. This research could provide a scientific basis for the real-time analysis and processing of data in pumping station operation and maintenance systems.

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Law and Early Warning of Vertical Sluice Cluster Displacements in Soft Coastal Soil

Cited by 2 publications

References 44 publications

A Deformation Analysis Method for Sluice Structure Based on Panel Data

A Deformation Analysis Method for Sluice Structure Based on Panel Data

Vibration Prediction and Evaluation System of the Pumping Station Based on ARIMA–ANFIS–WOA Hybrid Model and D-S Evidence Theory

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