Non-Destructive Prediction of Concrete Compressive Strength Using Neural Networks

• Published in: Procedia computer science 2017, Vol.108, p.2358-2362
• Main Authors: Khashman, Adnan, Akpinar, Pinar
• Format: Article
• Language: English
• Subjects: concrete compressive strength; high performance concrete mixes; Intelligent classification; neural networks; Prediction; quality control; training-to-testing ratio
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2017.05.039

Our thirst for progress as humans is reflected by our continuous research activities in different areas leading to many useful emerging applications and technologies. Artificial intelligence and its applications are good examples of such explored fields with varying expectations and realistic results. Generally, artificially intelligent systems have shown their capability in solving real-life problems; particularly in non-linear tasks. Such tasks are often assigned to an artificial neural network (ANN) model to arbitrate as they mimic the structure and function of a biological brain; albeit at a basic level. In this paper, we investigate a newly emerging application area for ANNs; namely civil engineering. We design, implement and test an ANN model to predict and classify the compressive strength of different concrete mixes into low, moderate or high strength. Traditionally, the performance of concrete is affected by many non-linear factors and testing its strength comprises a destructive procedure of concrete samples. Numerical results in this work show high efficiency in correctly classifying the compressive strength, thus making it possible to use in real-life applications.