Assessing the reliability of RC code predictions through the use of Artificial Neural Networks

A large number of tests have been conducted to date in order to investigate in detail the behaviour of simply supported reinforced concrete (RC) beam specimens under static loading. The test data obtained forms the basis for the development of the physical models currently adopted by the codes of practice (e.g. ACI, EC2 and JSCE) for the ultimate limit-state (ULS) design of RC members. However, a comparison of the code predictions concerning important aspects of RC structural response at the ULS with their experimental counterparts often exhibits significant differences. This raises concerns regarding the validity of the fundamental assumptions adopted by the various codes concerning the mechanics underlying RC structural response at the ULS as well as the effectiveness of the proposed design solutions in safeguarding the often stringent structural performance requirements (dictated by the codes of practice) usually associated with strength and ductility. Present work forms the initial step of a more general study aiming to assess the reliability of the RC design codes by comparing their predictions with their counterparts established: (i) experimentally, (ii) numerically via nonlinear finite element analysis (iii) through the use of Artificial Neural Networks (ANNs) and (iv) by employing an alternative physical model adopted by the Compressive Force Path (CFP) method which provides design solutions considerably different to those of the available design codes. In the present study ANNs are employed to directly assess the available test data and to objectively quantify the effect of important design parameters on RC structural response. The training and validation of the ANNs is achieved through the use of databases formed by the test data mentioned above. The input parameters are selected on the basis of the physical models employed for describing the mechanisms underlying RC structural response at the ULS and are associated with the specimen design details. The comparative study reveals that for certain values of input parameters the predictions of the CFP method and the ANNs correlate closer to the relevant experimental data than their counterparts obtained by the codes. This highlights the urgent need to re-assess current codes for RC design and the underlying assumptions upon which they are based.