In the field of engineering, it is crucial to have accurate estimates of the performance of building materials. These estimates are required in order to develop safety guidelines governing the materials used in the construction of buildings, bridges, and roadways. Estimating the strength of concrete is a challenge of particular interest. Although it is used in nearly every construction project, concrete performance varies greatly due to the use of a wide variety of ingredients that interact in complex ways. As a result, it is difficult to accurately predict the strength of the final product. A model that could reliably predict concrete strength given a listing of the composition of the input materials could result in safer construction practices.
For this analysis, we will utilize data on the compressive strength of concrete donated to the UCI Machine Learning Data Repository ( http://archive.ics.uci.edu/ml ) by I-Cheng Yeh. As he found success using neural networks to model these data, we will attempt to replicate Yeh's work using a simple neural network model in R.
For more information on Yeh's approach to this learning task, refer to: Modeling of strength of high performance concrete using artificial neural networks, Cement and Concrete Research, Vol. 28, pp. 1797-1808, by I-C Yeh (1998).
Based on the 7 chapter Machine Learning With R - Author Brett Lantz