Machine learning techniques to predict the compressive strength of concrete
Priscila F. S. Silva, Gray Farias Moita, Vanderci Fernandes Arruda
Abstract
Conventional concrete is the most common material used in civil construction, and its behavior is highly nonlinear, mainly because of its heterogeneous characteristics. Compressive strength is one of the most critical parameters when designing concrete structures, and it is widely used by engineers. This parameter is usually determined through expensive laboratory tests, causing a loss of resources, materials, and time. However, artificial intelligence and its numerous applications are examples of new technologies that have been used successfully in scientific applications. Artificial neural network (ANN) and support vector machine (SVM) models are generally used to resolve engineering problems. In this work, three models are designed, implemented, and tested to determine the compressive strength of concrete: random forest, SVM, and ANNs. Pre-processing data, statistical methods, and data visualization techniques are also employed to gain a better understanding of the database. Finally, the results obtained show high efficiency and are compared with other works, which also captured the compressive strength of the concrete.