Displacement behavior interpretation and prediction model of concrete gravity dams located in cold area

The measured displacement-driven structural health monitoring (SHM) model is a significant approach to interpreting the influencing mechanism of environmental loads on dam displacement and to predicting the future operating behavior. Considering the large ambient temperature difference in cold area...

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Veröffentlicht in:Structural health monitoring 2023-07, Vol.22 (4), p.2384-2401
Hauptverfasser: Yuan, Dongyang, Gu, Chongshi, Wei, Bowen, Qin, Xiangnan, Gu, Hao
Format: Artikel
Sprache:eng
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Zusammenfassung:The measured displacement-driven structural health monitoring (SHM) model is a significant approach to interpreting the influencing mechanism of environmental loads on dam displacement and to predicting the future operating behavior. Considering the large ambient temperature difference in cold area and the insufficient interpretive performance of the hydrostatic-seasonal-time (HST) model on thermal displacement behavior caused by the short-term dynamic fluctuations of ambient temperature, a hydrostatic-thermal-time (HTT) model considering hysteresis effect of boundary temperature is proposed to quantitatively analyze the influencing mechanism of environmental loads on displacement behavior of concrete gravity dams in cold area. Meanwhile, to improve the predictive performance of the proposed HTT model, whale optimization algorithm (WOA)-optimized gated recurrent unit (GRU) network is employed to effectively excavate the complex nonlinear relationship between dam displacement and its explanatory variables. A roller compacted concrete (RCC) gravity dam in cold area is taken as an example, and the cause of the disharmonious horizontal displacement behavior of an overflow dam crest is explained via the proposed HTT model and numerical simulation method. The conclusion is conducive for better understanding the displacement behavior of concrete dams in cold area. Meanwhile, engineering examples show that the proposed HTT model can reasonably interpret the thermal displacement behavior caused by the ambient temperature variations, and the WOA-optimized GRU network-based HTT model exhibits excellent fitting and predicting capabilities. A novel technique is provided for the accurate prediction of dam displacement.
ISSN:1475-9217
1741-3168
DOI:10.1177/14759217221122368