Concrete beams fire design using graphs

Design Optimization of Reinforced Concrete Beams Using Artificial Neural Network

–– This paper presents an Artificial Neural Networks (ANN) model for the cost optimization of simply supported beams designed according to the requirements of the ACI 318-08 code. The model formulation includes the cost of concrete, the cost of reinforcement and the cost of formwork. A simply supported beam was designed adopting variable cross sections, in order to demonstrate the model capabil...

Finite Element Modeling of Reinforced Concrete Beams Exposed to Fire

Abstract: The practical implementation of performance-based fire safety design of reinforced concrete (RC) structures hinges on the availability of accurate numerical simulation tools for the behavior of RC members exposed to fire. This paper presents a three-dimensional (3D) finite element (FE) model for the accurate prediction of both the thermal and the mechanical behavior of RC beams expose...

Detecting Fire Damaged Concrete Using Laser Scanning

Concrete is the single most widely used building material throughout the UK and the world. A major advantage of properly designed concrete construction is its inherent resistance to the effects of unwanted fires. The detection and location of structural deficiencies in buildings is of vital importance from a health and safety point of view. In some cases the damage to these materials is easy to...

Failure design of high-rise concrete panels under fire loading

Optimum Design of Reinforced Concrete Beams using Teaching- Learning-Based Optimization

Efficiency of reinforced concrete (RC) design can be measured by finding the design with minimum cost within all possible ones that satisfied security conditions. This task is difficult because RC members consisting of two materials; concrete and steel bars with different material properties i.e., compressive and tensile strength, elasticity modulus, and unit material costs. Thus, an additional...