SMART SELF-SENSING CONCRETE BEAMS: REAL-TIME CRACK DETECTION AND STRUCTURAL PERFORMANCE UNDER DYNAMIC LOADS
Integration of self-sensing materials into civil infrastructures is a novel approach to structural health monitoring (SHM) that has the capability to detect damage and performance deterioration in real time. This study explores the use of graphite powder (GP) and its synergistic blends with silica fume (SF), carbon fibers (CF), and carbon nanotubes (CNT) in self-sensing smart concrete beams under dynamic loading. There was an organized literature survey of experiments performed keeping in view GP content 0–20% by weight of the binder, SF content 0.75% by volume of the cement, CF content 0.2%, and CNT content 0.4%. Main performance parameters such as compressive strength and electrical resistivity were considered for measuring mechanical strength as well as sensing performance. Results demonstrate that GP-based composites exhibit excellent enhancement of electrical conductivity for the early detection of cracks, with satisfactory performance in structures. GP blends with SF, CF, and CNT also displayed enhanced strain sensitivity and durability, especially for columns and beams, with potential use in historic masonry buildings. Although promising, work is needed in long-term durability testing, harsh environmental cycle performance, and bridge and metallic structural member applications. The work points the way to optimal conductive additive content optimization for achieving mechanical performance and sensing effectiveness balance, paving the way to reliable, smart infrastructure systems.
Authors
- Anton Emmanuel (ORCID: https://orcid.org/0000-0002-0935-1128)
- AKEGBEYALE SEMIU OMOTOLA
- CONFIDENCE ADIMCHI CHINONYEREM
- YUUSUF OYELOLA OLADIPO
- Adeoti Shuaib Olamilekan
Institutions
- Abia State University (NG)
- University of Ilorin (NG)
- Abia State Polytechnic
- Obafemi Awolowo University (NG)
- University of Benin (NG)
Publication Details
- Journal
- International Journal of Engineering Research and Technology
- Published
- 2025-09-24
- DOI
- https://doi.org/10.70382/tijert.v09i5.014
- Primary Topic
- Structural Health Monitoring Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00