10th International Congress on Information and Communication Technology

Authors - ThiTuyetNga Phu, HongGiang Nguyen
Abstract - Inspecting the compressive strength of buildings' concrete is essential for ensuring the safety of households. This paper examined the study samplers collected using the nondestructive testing (NDT) method combined with Ultrasonic Pulse Velocity (UPV) and Rebound Hammer (RH) tests to check the beams of some apartments over 30 years old. Firstly, research samples were deployed to analyze the level of data variation using the exploratory data analysis (EDA) method to assess the reliability and correlation of data samples. Next, the study focused on the prediction of concrete compressive strength deploying five functions of activation (AF) (tanhLU, tanh, leakyLU, reLU, and sigmoid) by using two deep learning models as long short-term memory (LSTM) and gated recurrent unit (GRU). Lastly, the experimental results showed that the GRU model combined with two kinds of hybrid AFs gave a fairly accurate prediction level; in contrast, the remaining AF showed acceptable results.