Comparison of neural network and binary logistic regression methods in conceptual design of tall steel buildings

Purpose – To investigate the feasibility of using artificial neural networks for conceptual design of bracings systems for tall steel buildings. Design/methodology/approach – Database of 234 design examples has been developed using commercially available detailed design software. These examples represent building up to 20 storeys. Feed forward back-propagation neural network is trained on these examples. The results obtained from the artificial neural network are evaluated by re-substitution, hold-out and ten-fold cross-validation techniques. Findings – Results indicate that artificial neural network would give a performance of 97.91 percent (ten-fold cross-validation). The performance of this system is benchmarked by developing a binary logistic regression model from the same data. Performance of the two models has been compared using McNemar's test and receiver operation characteristics curves. Artificial neural network shows a better performance. The difference is found to be statically significant. Research limitations/implications – The developed model is applicable only to steel building up to 20 storeys. The feasibility of using artificial neural networks for conceptual design of bracings systems for tall steel buildings more than 20 storeys has not been investigated. Practical implications – Implementation of the broad methodology outlined for the use of neural networks can be accomplished by conducting short training courses. This will provide personnel with flexibility in addressing buildings-specifics bracing conditions and limitations. Originality/value – In tall building design a lot of progress has been made in the development of software tools for numerical intensive tasks of analysis, design and optimization, however, professional software tools are not available to help the designer to choose an optimum building configuration at the conceptual design stage. The presented research provides a methodology to investigate the feasibility of using artificial neural networks for conceptual design of bracings systems for tall buildings. It is found that this approach for the selection of bracings in tall buildings is a better and cost effective option compared with database generated on the basis of expert opinion. It also correctly classifies and recommends the type of trussed bracing system.