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Date:2019-09-24

Indexed by:Journal Papers

Date of Publication:2019-07-15

Journal:ENGINEERING STRUCTURES

Included Journals:EI、SCIE

Volume:191

Page Number:162-178

ISSN:0141-0296

Key Words:Structural health monitoring; Piezoceramic transducer; Electro-mechanical impedance; Gravity dam; Earthquake; Damage monitoring

Abstract:A rare but remarkable accident is the dam failure due to earthquake that can trigger catastrophes on economic and life losses. It is essential to monitor the presence and development of seismic damage of dam structures in the early stage of emergence of damage and take steps to prevent the full structural failure. In this research, only seismic damage and dynamic performances of a roller compacted concrete (RCC) dam were investigated using an electromechanical impedance (EMI)-based structural health monitoring (SHM) technique enabled by lead-zirconate-titanate (PZT) ceramic sensors network. The finite element model of the PZT sensor and damage monitoring method were verified by comparing the scanning conductance signals obtained from the experimental tests. A quasi 3D finite element model of the RCC dam using concrete damaged plasticity (CDP) constitutive model was built and assumed to be subjected to different degrees of earthquake excitations to initiate the dam damage due to tension. The conductance signals were measured by the PZT sensors network which are excited by inputting unit harmonic voltage. In addition, the normalized root-mean-square deviation (RMSD) was used as a damage index to quantify and evaluate the variations in conductance signals measured at the PZT sensors. Furthermore, two damage indices, namely the RMSD based damage volume ratio (DVR) and RMSD based displacement, were proposed to predict the damage levels and maximum horizontal displacement at the crest of dam under seismic loading. It is clearly demonstrated that the EMI technique based PZT sensors network can keenly detect the seismic damage of dam and the proposed damage indices can effectively quantify the damage levels and predict the dynamic response performances.