Artificial Neural Network-based Designs of Prestressed Concrete and Composite Structures

This book introduces artificial neural network (ANN)-based Lagrange optimization techniques for a structural design of prestressed concrete structures based on Eurocode 2, and composite structures based on American Institute of Steel Construction and American Concrete Institute standards. The book provides robust design charts for prestressed concrete structures, which are challenging to achieve using conventional design methods.

Using ANN-based design charts, the holistic design of a post-tensioned beam is performed to optimize design targets (objective functions), while calculating 21 forward outputs, in arbitrary sequences, from 21 forward inputs.

• Applies the powerful tools of ANN to the optimization of prestressed concrete structures and composite structures including columns and beams
• Multi-objective optimizations (MOO) of prestressed concrete beams are performed using an ANN-based Lagrange algorithm
• Offers a Pareto frontier using an ANN-based MOO for composite beams and composite columns sustaining multi-biaxial loads
• Heavily illustrated in color and with diverse practical design examples in line with EC2, ACI, and ASTM codes

The book offers optimal solutions for structural designers and researchers, enabling readers to construct design charts to minimize their own design targets under various design requirements based on any design code.

Won‐Kee Hong is a Professor of Architectural Engineering at Kyung Hee University, South Korea. He has more than 35 years of professional experience in structural and construction engineering, having worked for Englekirk and Hart, USA; Nihhon Sekkei, Japan; and Samsung Engineering and Construction, Korea. He is the author of Artificial Neural Network-based Optimized Design of Reinforced Concrete Structures, also published by CRC Press.