Trial inspection of exterior‐tile wall specimens with a prototyped tile‐debonding diagnostic device

Soeta, Tomomi; Ito, Shutaro; Fujinuma, Tomohiro; Mikami, Takamasa

doi:10.1002/2475-8876.12181

Cited by 2 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the recent research literature, there are many studies that focus on non-destructive methods of debonded tiles inspection. Soeta et al [1] developed a tile-debonding diagnostic device to identify articially debonded areas of twelve exterior tile specimens. Their device consisted of an impact force sensor and four microphones, each having its output detection parameters.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Inspection of Tile Debonding by DWT and MFCC of Tile-Tapping Sound with Machine versus Deep Learning Models

Panyavaraporn,

Horkaew

2024

ECTI-CIT Transactions

View full text Add to dashboard Cite

One of the essential processes of construction quality control is tile bonding inspection. Hollows beneath tile tessellation can lead to unbounded or completely broken tiles. An interior inspector typically used a hollowsounding technique. However, it relies on skill and judgment that greatly vary among individuals. Moreover, equipment and interpretation are difficult to calibrate and standardize. This paper addresses these issues by employing machine-learning strategies for tile-tapping sound classification. Provided that a tapping signal was digitally acquired, the proposed method was fully computerized. Firstly, the signal was analyzed and its wavelets and MFCC were extracted. The corresponding spectral features were then classified by SVM, k-NN, Naïve Bayes, and Logistic Regression algorithm, in turn. The results were subsequently compared against those from a previous works that employed a deep learning strategy. It was revealed that when the proposed method was properly configured, it required much less computing resources than the deep learning based one, while being able to distinguish dull from hollow sounding tiles with 93.67% accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%