Devising a computational model based on data mining techniques to predict concrete compressive strength

Abstract

Abstract Predicting the compressive strength of concrete is an essential task in the construction process, since a prior knowledge on such information helps enhancing speed and quality of the process. Recently, many computational methods and techniques have been developed to predict distinct properties of concrete. However, a practical use of these solutions requires a high degree of engineering expertise and programming skills. Alternatively, this work advocates that software packages with off-the-shelf data mining algorithms can empower researchers and engineers on this task, while demanding less effort. In this direction, we present a detailed study on the use of Weka, evaluating different regression algorithms for predicting the compressive strength of concrete. Using the most complete dataset available at the UCI dataset repository, we demonstrate that most of the techniques available in Weka produces results close to the best ones reported in the literature. For instance, most of the evaluated predicting models generates a Mean Absolute Error (MAE) inferior to 10, while the best result found is 8. Moreover, by fine-tuning the parameters of the regression algorithm Bagging with REPTree, we achieved a MAE value inferior to 3.3 for the evaluated dataset. Hence, the process considered in this study is also useful as a guideline to devise new computational models based on off-the-shelf data mining algorithms.