Assessing the accuracy of RC design code predictions through the use of artificial neural networks

Abstract

In light of recently published work highlighting the incompatibility between the concepts underlying current code specifications and fundamental concrete properties, the work presented herein focuses on assessing the ability of the methods adopted by some of the most widely used codes of practice for the design of reinforced concrete structures to provide predictions concerning load-carrying capacity in agreement with their experimentally established counterparts. A comparative study is carried out between the available experimental data and the predictions obtained from (1) the design codes considered, (2) a published alternative method (the compressive force path method), the development of which is based on assumptions different (if not contradictory) to those adopted by the available design codes, as well as (3) artificial neural networks that have been calibrated based on the available test data (the later data are presented herein in the form of a database). The comparative study reveals that the predictions of the artificial neural networks provide a close fit to the available experimental data. In addition, the predictions of the alternative assessment method are often closer to the available test data compared to their counterparts provided by the design codes considered. This highlights the urgent need to re-assess the assumptions upon which the development of the design codes is based and identify the reasons that trigger the observed divergence between their predictions and the experimentally established values. Finally, it is demonstrated that reducing the incompatibility between the concepts underlying the development of the design methods and the fundamental material properties of concrete improves the effectiveness of these methods to a degree that calibration may eventually become unnecessary.