Data Mining Techniques for Prediction of Concrete Compressive Strength (CCS)
DOI:
https://doi.org/10.33977/2106-000-003-006Keywords:
Data Mining, Concrete Compressive Strength (CCS), K-means, EM Algorithm, Kohonen’s Self-Organizing Map (KSOM), Clustering.Abstract
The main aim of this research is to use data mining techniques to explore the main factors affecting the strength of concrete mix. In this research, we are interested in finding some of the factors that influence the high performance of concrete to increase the Concrete Compressive Strength (CCS) mix. We used Waikato’s Knowledge Analysis Environment (WEKA) tool and algorithms such as K-Means, Kohonen’s Self Organizing Map (KSOM) and EM to identify the most influential factors that increase the strength of the concrete mix. The results of this research showed that EM is highly capable of determining the main components that affect the compressive strength of high performance concrete mix. The other two algorithms, K-Means and KSOM, were noted to be an advanced predictive model for predicting the strength of the concrete mix.
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