Real-time assessment of rebar intervals using a computer vision-based DVNet model for improved structural integrity

Bubryur Kim, K. R. Sri Preethaa, Yuvaraj Natarajan, Danushkumar V, Jinwoo An, Dong Eun Lee

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Structural durability is critical for building and civil engineering safety, wherein the arrangement and distribution of reinforcing bar (rebar) is crucial. Improperly aligned rebar impacts bearing capacity, whereas uniform spacing optimally distributes loads, reducing stress. We introduce a computer-vision based Deep Vision Net (DVNet) model for real-time evaluation of rebar placement. A customized dataset is prepared in an environmental setup and augmented to address overfitting issues. This research conducts a comparative analysis of the learning performance exhibited by the proposed DVNet model against several other pre-trained models, such as Mask-RCNN and YOLOv5. The proposed DVNet model is built on a customized DeepCNN architecture, achieving a commendable precision of 88.6 % and recall of 89.3 %. Utilizing the DVNet model, the real-time assessments of rebar placements were performed at various spacing intervals. Experimental results demonstrate that the DVNet-based model excels at ensuring the structural arrangements of the rebar intervals.

Original languageEnglish
Article numbere03707
JournalCase Studies in Construction Materials
Volume21
DOIs
StatePublished - Dec 2024

Keywords

  • Convolution neural network
  • Deep neural network
  • Rebar
  • Segmentation
  • Structural health

Fingerprint

Dive into the research topics of 'Real-time assessment of rebar intervals using a computer vision-based DVNet model for improved structural integrity'. Together they form a unique fingerprint.

Cite this