A study of cracking modes of polypropylene fiber reinforced recycled aggregate concrete

This study systematically investigates the cracking behavior of polypropylene fiber (PPF)-reinforced recycled aggregate concrete (RAC). Specimens with coarse aggregate (CA) replacement ratios of 25% and 50% were prepared for twenty distinct test groups. Uniaxial compression tests were conducted under acoustic emission (AE) monitoring to record the peak stress and AE parameters, such as average frequency, energy, amplitude, and dominant frequency. Correlations between the mean values of AE signals and the peak stress were examined. An unsupervised machine learning technique, the k-means clustering algorithm, was employed to classify cracking modes, namely matrix cracking, fiber–matrix debonding, and fiber pull-out. The results indicate that matrix cracking exhibits low average frequency and low energy, fiber–matrix debonding is characterized by high average frequency and low energy, while fiber pull-out demonstrates high energy release. Quantitative threshold criteria for distinguishing these cracking modes are established, offering an analytical framework for identifying failure mechanisms in PPF-reinforced RAC. This analytical framework provides valuable insights for optimizing fiber-reinforced recycled concrete designs and assessing structural integrity through non-destructive monitoring.

Authors

Institutions

Publication Details

Journal
Journal of Reinforced Plastics and Composites
Published
2026-02-14
DOI
https://doi.org/10.1177/07316844261427568
Primary Topic
Recycled Aggregate Concrete Performance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A study of cracking modes of polypropylene fiber reinforced recycled aggregate concrete

Ninghui Liang, Xin Yang
Journal of Reinforced Plastics and Composites
Recycled Aggregate Concrete Performance
article

A study of cracking modes of polypropylene fiber reinforced recycled aggregate concrete

Ninghui Liang, Xin Yang
article en

Abstract

This study systematically investigates the cracking behavior of polypropylene fiber (PPF)-reinforced recycled aggregate concrete (RAC). Specimens with coarse aggregate (CA) replacement ratios of 25% and 50% were prepared for twenty distinct test groups. Uniaxial compression tests were conducted under acoustic emission (AE) monitoring to record the peak stress and AE parameters, such as average frequency, energy, amplitude, and dominant frequency. Correlations between the mean values of AE signals and the peak stress were examined. An unsupervised machine learning technique, the k-means clustering algorithm, was employed to classify cracking modes, namely matrix cracking, fiber–matrix debonding, and fiber pull-out. The results indicate that matrix cracking exhibits low average frequency and low energy, fiber–matrix debonding is characterized by high average frequency and low energy, while fiber pull-out demonstrates high energy release. Quantitative threshold criteria for distinguishing these cracking modes are established, offering an analytical framework for identifying failure mechanisms in PPF-reinforced RAC. This analytical framework provides valuable insights for optimizing fiber-reinforced recycled concrete designs and assessing structural integrity through non-destructive monitoring.

Journal of Reinforced Plastics and Composites
Chongqing University (CN), Fujian University of Technology (CN)
Sustainable cities and communities
Openalex Percentile: Top 55%
Recycled Aggregate Concrete Performance
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.