Structural health monitoring of concrete dams using long-term air temperature for thermal effect simulation. (1st February 2019)

• Record Type: Journal Article
• Authors: Kang, Fei; Li, Junjie; Zhao, Sizeng; Wang, Yujun
• Is Part Of: Engineering structures. Volume 180(2019)
• Page Start: 642
• Page End: 653
• Publication Date: 2019-02-01
• Subjects: Concrete gravity dams -- Structural health monitoring -- Temperature effect -- Radial basis function networks -- Displacement; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2018.11.065
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: This paper presents a dam health monitoring model based on long-term air temperature. Radial basis function networks are adopted to mine the temperature effect on the dam displacement. The proposed dam health monitoring model is verified on a real concrete gravity dam. Results show that the proposed approach can obtain better results than models using harmonic sinusoidal functions. Abstract: Dam health monitoring is usually achieved by using the hydrostatic-season-time model that simulates the temperature effect through harmonic sinusoidal functions. The model is convenient, but does not take into account the effect of temperature variations in different years on dam response. This paper presents a dam health monitoring model using long-term air temperature for thermal effect simulation. In the model, harmonic sinusoidal functions are replaced by long-term air temperature to simulate the temperature effect on the dam response. The machine learning technique RBFN is adopted to mine the temperature effect from long-term air temperature series. The proposed dam health monitoring model was verified using monitoring data of a real concrete gravity dam. Results show that the nonlinear RBFN model utilizing long-term air temperature achieves better results than the model using harmonic sinusoidal functions.