Artificial Neural Network Based Optimized Design Of Reinforced Concrete Structures
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Artificial Neural Network-based Optimized Design of Reinforced Concrete Structures
Artificial Neural Network-based Optimized Design of Reinforced Concrete Structures introduces AI-based Lagrange optimization techniques that can enable more rational engineering decisions for concrete structures while conforming to codes of practice. It shows how objective functions including cost, CO2 emissions, and structural weight of concrete structures are optimized either separately or simultaneously while satisfying constraining design conditions using an ANN-based Lagrange algorithm. Any design target can be adopted as an objective function. Many optimized design examples are verified by both conventional structural calculations and big datasets. Uniquely applies the new powerful tools of AI to concrete structural design and optimization Multi-objective functions of concrete structures optimized either separately or simultaneously Design requirements imposed by codes are automatically satisfied by constraining conditions Heavily illustrated in color with practical design examples The book suits undergraduate and graduate students who have an understanding of collegelevel calculus and will be especially beneficial to engineers and contractors who seek to optimize concrete structures.
AI-Based Optimized Design of Structural Frames
This book introduces an auto‐design‐based optimization for building frames using an artificial neural network (ANN)‐based Lagrange method and novel genetic algorithm (GA). The work of great mathematician Joseph‐Louis Lagrange and ANNs are merged to identify parameters that optimize structural frames of reinforced concrete, prestressed concrete, and steel frames subject to one or more design constraints. New features for enhancing conventional GA are also demonstrated to optimize structural frames. New features for optimizing multiple design targets of the building frames are highlighted, while design requirements imposed by codes are automatically satisfied. Chapters provide readers with an understanding of how both ANN‐based and novel GA‐based structural optimization can be implemented in holistically optimizing designated design targets for building structural frames, guiding readers toward more rational designs that is consistent with American Institute of Steel Construction (AISC) and American Concrete Institute (ACI) standards. ANN‐based holistic designs of multi‐story frames in general and reinforced concrete, prestressed concrete, and steel frames in particular, are introduced. This book suits structural engineers, architects, and graduate students in the field of building frame designs and is heavily illustrated with color figures and tables.
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.