Tensile Strength Estimation of UHPFRC Based on Predicted Cracking Location Using Deep Learning
Xin Luo, Takashi Matsumoto
TL;DR
This paper presents a deep learning method to estimate the tensile strength of UHPFRC by predicting where cracks will form based on fiber characteristics.
Contribution
A novel deep learning approach using YOLOv11 to predict cracking locations and estimate tensile strength in UHPFRC.
Findings
The deep learning model achieved a mean Average Precision ([email protected]) of 0.87 in predicting defective fiber distribution regions.
Strain-hardening specimens showed an average experiment-estimation error of 5.72% and a theory-estimation error of 3.34%.
Strain-softening specimens had significantly higher errors, with 43.09% experiment-estimation and 15.73% theory-estimation errors.
Abstract
Ultra-high-performance fiber-reinforced concrete (UHPFRC) exhibits exceptional tensile properties, but its tensile strength is highly dependent on fiber distribution, orientation, and count, making accurate strength estimation challenging. This study introduces a novel approach in which tensile strength estimation is achieved by analyzing fiber characteristics at predicted cracking locations using deep learning. Using X-ray computed tomography (CT) and image analysis techniques, the fiber orientation factor (μ0) and average efficiency factor ((μ1)−) were determined at predicted cracking locations. A deep learning model (YOLOv11) was trained to identify regions with a defective distribution, achieving a mean Average Precision ([email protected]) of 0.87, demonstrating its high reliability in predicting cracking locations. The overall cracking location prediction success rate was 73% for…
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Taxonomy
TopicsInnovative concrete reinforcement materials · Infrastructure Maintenance and Monitoring · Structural Behavior of Reinforced Concrete