Electromechanical and Rheological Properties of Self-Sensing Mortars Containing Red Mud for Concrete Beam Monitoring

The growing demand for sustainable construction practices has driven research into self-sensing materials incorporating recycled waste for smart SHM (Structural Health Monitoring) systems. However, previous works did not investigate the influence of rheological behavior and piezoresistive properties of sustainable cementitious sensors containing red mud (RM) on the strain monitoring of concrete beams. To address this gap, this study presents an experimental analysis of the rheological, mechanical, and self-sensing performance of mortars incorporating carbon black nanoparticles (CBN) and varying levels of RM (25–100% sand replacement by volume), followed by their application in monitoring strain in a reinforced concrete beam under dynamic loading. The results showed that increasing RM content led to higher viscosity and yield stress, with a 60% reduction in consistency index. Compressive strength increased by up to 80%, while mortars with RM content higher than 50% showed high electrical conductivity and reversible resistivity changes under load cycles. Mortars containing 50–100% RM demonstrated improved piezoresistive response, with a 23% increase in gauge factor, and the best-performing sensor embedded in a concrete beam exhibited stable and reversible fractional changes in resistivity, closely matching strain gauge data during dynamic loading conditions. These findings highlight the potential of RM-based smart mortars to enhance sustainability and performance in SHM applications.

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Publication Details

Journal
Buildings
Published
2025-11-13
DOI
https://doi.org/10.3390/buildings15224085
Primary Topic
Smart Materials for Construction
Type
article
Field-Weighted Citation Impact
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article

Electromechanical and Rheological Properties of Self-Sensing Mortars Containing Red Mud for Concrete Beam Monitoring

José Maria Franco de Carvalho, José Carlos Lopes Ribeiro, Flávio Antônio Ferreira, Ricardo André Fiorotti Peixoto et al.
Buildings
Smart Materials for Construction
article

Electromechanical and Rheological Properties of Self-Sensing Mortars Containing Red Mud for Concrete Beam Monitoring

José Maria Franco de Carvalho, José Carlos Lopes Ribeiro, Flávio Antônio Ferreira, Ricardo André Fiorotti Peixoto, Henrique Oliveira, Leonardo Gonçalves Pedroti, Gustavo Emílio Soares de Lima, Ariel Miranda de Souza, Gustavo Henrique Nalon, Diôgo Silva de Oliveira
article en

Abstract

The growing demand for sustainable construction practices has driven research into self-sensing materials incorporating recycled waste for smart SHM (Structural Health Monitoring) systems. However, previous works did not investigate the influence of rheological behavior and piezoresistive properties of sustainable cementitious sensors containing red mud (RM) on the strain monitoring of concrete beams. To address this gap, this study presents an experimental analysis of the rheological, mechanical, and self-sensing performance of mortars incorporating carbon black nanoparticles (CBN) and varying levels of RM (25–100% sand replacement by volume), followed by their application in monitoring strain in a reinforced concrete beam under dynamic loading. The results showed that increasing RM content led to higher viscosity and yield stress, with a 60% reduction in consistency index. Compressive strength increased by up to 80%, while mortars with RM content higher than 50% showed high electrical conductivity and reversible resistivity changes under load cycles. Mortars containing 50–100% RM demonstrated improved piezoresistive response, with a 23% increase in gauge factor, and the best-performing sensor embedded in a concrete beam exhibited stable and reversible fractional changes in resistivity, closely matching strain gauge data during dynamic loading conditions. These findings highlight the potential of RM-based smart mortars to enhance sustainability and performance in SHM applications.

BuildingsVol. 15(22)
Universidade Federal de Juiz de Fora (BR), Universidade Federal de Ouro Preto (BR), Universidade Federal de Viçosa (BR)
Universidade Federal de Viçosa
Openalex Percentile: Top 73%
Smart Materials for Construction
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