Published July 30, 2023 | Version CC BY-NC-ND 4.0
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A Deep Learning Based Non-Destructive Method for Estimating Concrete Strength using Continuous Wavelet Transform of Vibration Signals Acquired using A Smartphone's Accelerometer

  • 1. Department of Civil Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.


Abstract: Most non-destructive tests of concrete require sophis-ticated equipment and training; in this work we aim to develop a simple method to estimate the strength class of cylindrical con-crete samples based on vibrations signals that are collected after striking a concrete cylinder with a hammer. The vibration signals were collected by attaching a smartphone to the concrete cylinder and logging the vibrations registered via the smartphone’s built-in accelerometer. The acquired 1-D vibration signals are trans-formed to 2-D scalograms using continuous wavelet transform. Scalograms are then used to train a deep learningmodel to predict the strength class. Preliminary findings show that the model is capable of classifying the strength of concrete to low, high, or me-dium. The developed model achieved a high accuracy of 91.67%. The promising results of this work shed light into the future of smartphone-based measurements of construction materials’ properties.


Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved.



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ISSN: 2277-3878 (Online)
Retrieval Number: 100.1/ijrte.B77380712223
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Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)