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Indexed by:期刊论文

Date of Publication:2019-02-01

Journal:ENGINEERING STRUCTURES

Included Journals:SCIE、Scopus

Volume:180

Page Number:642-653

ISSN No.:0141-0296

Key Words:Concrete gravity dams; Structural health monitoring; Temperature effect; Radial basis function networks; Displacement

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.