Dynamic Strength Study of Small, Fixed-edge, Longitudinally Restrained Two-way Reinforced Concrete Slabs
Dynamic Strength Study of Small, Fixed-edge, Longitudinally Restrained Two-way Reinforced Concrete Slabs

Author: Wayne M. Brown

Publisher:

Published: 1973

Total Pages: 118

ISBN-13:

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Increases in ultimate strengths of 23.7 (Series I) and 24.6 (Series II) percent under dynamic loading were obtained. Theoretical slab strengths were determined. Modification of the equations used allowed good predictions of tensile membrane resistance of the static slabs. The equations were used to predict peak pressures sustained by the dynamic slabs.

Dynamic Neural Network for Predicting Creep of Structural Masonry
Dynamic Neural Network for Predicting Creep of Structural Masonry

Author: Mustafa Mohammed Abed

Publisher: LAP Lambert Academic Publishing

Published: 2012-02

Total Pages: 96

ISBN-13: 9783846588208

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One of the inherent modeling problems in structural engineering is creep of quasi-brittle materials (e.g., concrete and masonry). The creep strain represents the non-instantaneous strain that occurs with time when the stress is sustained. Several creep models with limited accuracy have been developed within the last few decades to predict creep of concrete and masonry structures. The stochastic nature of creep deformation and its reliance on a large number of uncontrolled parameters (e.g., relative humidity, age of loading, stress level) makes the process of prediction difficult, and yet accurate mathematical model almost impossible. This study investigates the potential use of Dynamic Neural Network (DNN) for predicting creep of structural masonry. The main motive of use DNN is that DNN could memorize the sequential or time-varying patterns while training process. Thus, DNN becomes more capable of capturing the time-dependent of creep deformation than the static networks. The results showed that the developed DNN models are able to predict the creep deformation with an excellent level of accuracy compared with that of conventional methods and the static networks models.

Artificial Intelligence-Based Design of Reinforced Concrete Structures
Artificial Intelligence-Based Design of Reinforced Concrete Structures

Author: Won-Kee Hong

Publisher: Elsevier

Published: 2023-04-29

Total Pages: 510

ISBN-13: 0443152535

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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. Helps civil engineers understand the fundamentals of AI and ANNs and how to apply them in simple reinforced concrete design cases Contains practical case study examples on the application of AI technology in structural engineer Teaches readers how to apply ANNs as solutions for a broad range of engineering problems Includes AI-based software [MATLAB], which will enable readers to verify AI-based examples