Won-Kee Hong – författare

Hybrid Composite Precast Systems: Numerical Investigation to Construction focuses on the design and construction of novel composite precast frame systems that permit almost effortless erection and structural efficiency. The precast frame systems discussed in the book are similar to that of steel frames, but offer similar savings to concrete frames. The design of connections and detailed analysis of their structural behavior is discussed in detail. Fundamentals with regards to the post yield behavior of concrete and metal are also presented to illustrate how these two different materials are integrated together to remove individual material drawbacks.

Readers are given a broad introduction to existing technologies that are then combined with a description of the construction methods the author proposes. This book will help the end users become familiar with the existing types of structural forms, not just the "Lego" type frame system that the author proposes.

Artificial Intelligence-Based Design of Reinforced Concrete Structures: Artificial Neural Networks for Engineering Applications is an essential reference resource for readers who want to learn how to perform artificial intelligence-based structural design. The book describes, in detail, the main concepts of ANNs and their application and use in civil and architectural engineering. It shows how neural networks can be established and implemented depending on the nature of a broad range of diverse engineering problems. The design examples include both civil and architectural engineering solutions, for both structural engineering and concrete structures.

Those who have not had the opportunity to study or implement neural networks before will find this book very easy to follow. It covers the basic network theory and how to formulate and apply neural networks to real-world problems. Plenty of examples based on real engineering problems and solutions are included to help readers better understand important concepts.

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.

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.

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.

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.

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.

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.

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.

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.

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.