396e3d57186c1f28fcb4b05 20230720034421282 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 07 30 2023 12 2 Department of Civil Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia. Dr. Saleh J. Alghamdi https://orcid.org/0000-0003-1020-4704 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 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 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. 07 30 2023 47 53 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true International Journal of Recent Technology and Engineering (IJRTE) No, We did not receive. No conflicts of interest to the best of our knowledge. No, the article does not require ethical approval and consent to participate with evidence. Yes, it relevant to Availability of Data and Material. Data can be provided upon a rea-sonable request. I am only the sole author of the article. 10.35940/ijrte.B7738.0712223 https://www.ijrte.org/portfolio-item/B77380712223/