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Title: Structural modal identification and health monitoring of building structures using self-sensing cementitious composites
Authors: Ding, S 
Wang, YW 
Ni, YQ 
Han, BG
Issue Date: May-2020
Source: Smart materials and structures, May 2020, v. 29, no. 5, 055013, p. 1-18
Abstract: Recently self-sensing cementitious composite has demonstrated its strong potentiality for structural health monitoring of civil infrastructures because of its low-cost, long-term stability and compatibility with concrete structures. In this paper, we propose novel hybrid nanocarbon materials engineered cement-based sensors (HNCSs) with high-sensitivity, which are fabricated with self-sensing cementitious composites containing electrostatic self-assembled CNT/NCB composite fillers. The mechanical property and sensing performance of the HNCSs are pre-characterized under static and dynamic compressive loadings. The HNCSs are then integrated into a five-story building model via custom-made clamps to verify the feasibility for dynamic response measurements. Results show that the developed sensors have satisfactory mechanical property and excellent pressure-sensitive reproducibility and stability. With clamps holding on the building model, the HNCSs perform satisfactorily under sinusoidal excitations in the frequency range from 2 to 40 Hz. In addition, the modal frequencies and their changes of the building model caused by 'damage' simulated through adding additional masses identified by the HNCSs are favorably consistent with the counterparts acquired by accelerometers and strain gauges, indicating that the developed HNCSs have great potential for structural modal identification and damage detection applications.
Keywords: Self-sensing
Cement
Sensor
Carbon nanotube
Modal identification
Structural health monitoring
Publisher: Institute of Physics Publishing
Journal: Smart materials and structures 
ISSN: 0964-1726
EISSN: 1361-665X
DOI: 10.1088/1361-665X/ab79b9
Rights: © 2020 The Author(s)
Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (https://creativecommons.org/licenses/by/4.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
The following publication Siqi Ding et al 2020 Smart Mater. Struct. 29 055013 is available at https://dx.doi.org/10.1088/1361-665X/ab79b9
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