Extracting piezoresistive response of self-sensing cementitious composites under temperature effect via Bayesian blind source separation

Siqi Ding, Chi Xu, Yi Qing Ni, Baoguo Han

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


Self-sensing cementitious composite (SSCC) has been viewed as a promising sensing technology for structural health monitoring and traffic detection on account of its high sensitivity, low cost, long-term stability and compatibility with concrete structures. However, temperature variation effects in the electrical resistance measurements would impede the potential application of SSCC. It is therefore of great significance to understand the temperature effects on the piezoresistive performance of SSCC and eliminate such effects. In this study, temperature effects on the electrical and piezoresistive properties of SSCCs with different contents of carbon nanotube/nano carbon black (CNT/NCB) composite fillers are investigated under varying temperatures ranging from -20 C to 60 C and under concurrent temperature and loading variations. Experimental results show that an increase in CNT/NCB composite filler content can decrease the activation energy of SSCC and facilitate the transport of the charge carriers, thus attenuating the sensitivity of SSCC to temperature. Temperature variation has no effect on the piezoresistive repeatability of SSCC due to the stable overall distribution of conductive network in SSCC. However, temperature rise can reduce the piezoresistive sensitivity of SSCC. Aiming to diminish the effect of temperature on the piezoresistive property of SSCC, the SSCC responses to simultaneous temperature and loading excitations are then treated using a Bayesian blind source separation (BSS) method to reconstruct two independent sources. Regardless of the CNT/NCB composite filler content, the reconstructed source in relation to temperature variation always has a high correlation with the measured temperature, indicating that the proposed Bayesian BSS method can well extract and separate the electrical resistance variation induced by temperature variation from that induced by simultaneous temperature and loading excitations.

Original languageEnglish
Article number065010
JournalSmart Materials and Structures
Issue number6
Publication statusPublished - Jun 2021


  • blind source separation
  • cementitious composites
  • extraction of piezoresistive response
  • self-sensing
  • temperature

ASJC Scopus subject areas

  • Signal Processing
  • Civil and Structural Engineering
  • Atomic and Molecular Physics, and Optics
  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Electrical and Electronic Engineering

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