A Deep Learning Based Non-Destructive Method for Estimating Concrete Strength using Continuous Wavelet Transform of Vibration Signals Acquired using A Smartphone’s Accelerometer
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Abstract
Most non-destructive tests of concrete require sophisticated equipment and training; in this work we aim to develop a simple method to estimate the strength class of cylindrical concrete 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 learning model to predict the strength class. Preliminary findings show that the model is capable of classifying the strength of concrete to low, high, or medium. 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.
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