An ANN-based Hong-Lagrange algorithm (ANN-based HLA) for auto design-based building application (ABBA) with prestressed precast piperack frame

Abstract

Building optimization using traditional approaches based on explicit objective functions is difficult because of large numbers of variables and constraints. Many studies, thus, developed derivative-free optimizations using evolutionary programming, particle swarm methods, swarm intelligence, etc. Applications of artificial neural networks (ANNs) in frame designs are not common, although ANN is an evolutionary technology in many areas. This study presents novel holistic designs and optimizations of prestressed multi-story frames in general and piperack frames in particular, using the ANN-based Hong-Lagrange Algorithm. The proposed method provides optimized designs in terms of multiple objective functions such as costs, CO2 emissions, weights, and energy consumptions. A software is developed based on MATLAB codes for generating big datasets following American standards. A big data of 120,000 samples is used to train ANNs, formulating objective and constraint functions of 38 input and 69 output parameters. Designs minimizing multiple objective functions are performed by applying sequential quadratic programming to ANN-based functions constrained by equalities and inequalities imposed by architectural and code requirements. Examples show reductions up to 31.81%, 38.63%, 5.06%, and 37.36% in costs, CO2 emissions, weights, and energy consumption, respectively, compared with the minima of those identified from 340,000 samples.

Keywords

• ann-based hong-lagrange algorithm
• data centric optimization
• building frames
• abba (auto design-based building applications)
• multiple objectives optimization