Smart nano-engineered cementitious composite sensors for vibration-based health monitoring of large structures

Cement composites, generally with high resistivity, when incorporated with conductive nanoparticles, are found to be capable of reflecting strain state due to the change in electrical resistance of the matrix. These unique properties of cement-based nanocomposites (CNC) allow the development of smart cement sensors for continuous health monitoring of large concrete structures. Due to the limitation of strain-based sensing (for local behaviour), vibration-based sensing is gaining considerable importance. The performance of cement-based nanocomposites, unlike under the static load or slow varying load, for dynamic response sensing is very scanty. In the present study, an attempt has been made to develop robust smart cement sensors for capturing the multiple natural frequencies of a structure. First, a systemic way of fabrication of sensors (smart cement sensors) using cement matrix and nano-inclusions of carbon nanotubes is presented. Three different types of multi-walled nanotubes (MWCNTs) like pristine MWCNTs (P-MWCNTs), carboxyl acid (-COOH) MWCNTs and hydroxyl (-OH) MWCNTs with four different weight percentage (0.1-0.75 wt.%) of cement matrix are considered for the study. Strain sensibility of the developed sensors is explored under cyclic compression load. Further, the efficacy of the developed sensor for dynamic sensing is investigated using a large reinforced concrete bridge girder where instrumented impulse hammer is used to dynamically excite the bridge girder. It is found that the developed CNC is capable of capturing the natural frequencies (even up to the third mode) which are found to be sufficient for vibration-based health monitoring of structures. The performance of the developed sensors is compared with off-the-shelf accelerometers and is found to be in good agreement. The study provides a demonstrable technology for using the CNC as a vibration sensor for continuous health monitoring and assessment of structures, just by using ambient vibration information. (C) 2020 Elsevier B.V. All rights reserved.