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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

SMART SELF-SENSING CONCRETE BEAMS: REAL-TIME CRACK DETECTION AND STRUCTURAL PERFORMANCE UNDER DYNAMIC LOADS

Anton Emmanuel, AKEGBEYALE SEMIU OMOTOLA, CONFIDENCE ADIMCHI CHINONYEREM, YUUSUF OYELOLA OLADIPO et al.
International Journal of Engineering Research and Technology
Structural Health Monitoring Techniques
article

SMART SELF-SENSING CONCRETE BEAMS: REAL-TIME CRACK DETECTION AND STRUCTURAL PERFORMANCE UNDER DYNAMIC LOADS

Anton Emmanuel, AKEGBEYALE SEMIU OMOTOLA, CONFIDENCE ADIMCHI CHINONYEREM, YUUSUF OYELOLA OLADIPO, Adeoti Shuaib Olamilekan
article en

Abstract

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.

International Journal of Engineering Research and Technology
Abia State University (NG), University of Ilorin (NG), Abia State Polytechnic, Obafemi Awolowo University (NG), University of Benin (NG)
Openalex Percentile: Top 73%
Structural Health Monitoring Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.