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.