Effect of model architecture and input parameters to improve performance of artificial intelligence models for estimating concrete strength using SonReb

Abstract

The use of Artificial Intelligence (AI) with the non-intrusive SonReb method, which combines Ultrasonic Pulse Velocity (UPV) and Rebound Number (RN) to predict concrete compressive strength, has attracted increasing attention in recent years. This study introduces a novel approach to improve AI models for predicting concrete strength, making them more suitable for future practical applications. One of the key challenge in AI models is the number of input parameters; while more inputs often improve accuracy, they are typically impractical for most applications dealing with existing structures (e.g., requiring detailed concrete mix design information that is often unavailable). SonReb AI-based models which use only two input parameters (UPV and RN) have shown reasonable accuracy, but their general use is limited by adoption of different testing standards which precludes the development of large databases. This study aims to improve two-parameter SonReb-based AI models through the addition of a binary input variable that represents the type of the specimen geometry (cube or cylinder) and investigates the effect of model architecture by comparing three different AI algorithms: Artificial Neural Networks (ANN), Deep Neural Networks (DNN), and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). Six AI models were developed using 514 data points from experimental tests and collected data, and an unbiased data splitting method was applied for training and testing. The results showed that including specimen geometry improved model accuracy across all AI algorithms. The results of this study show that regardless of AI architecture, the proposed novel approach not only improves the accuracy of models, but also enables the use of larger databases containing both cubic and cylindrical specimens.