Determination of Fire Resistance of Eccentrically Loaded Reinforced Concrete Columns Using Fuzzy Neural Networks

Abstract

Artificial neural networks, in interaction with fuzzy logic, genetic algorithms, and fuzzy neural networks, represent an example of a modern interdisciplinary field, especially when it comes to solving certain types of engineering problems that could not be solved using traditional modeling methods and statistical methods. They represent a modern trend in practical developments within the prognostic modeling field and, with acceptable limitations, enjoy a generally recognized perspective for application in construction. Results obtained from numerical analysis, which includes analysis of the behavior of reinforced concrete elements and linear structures exposed to actions of standard fire, were used for the development of a prognostic model with the application of fuzzy neural networks. As fire resistance directly affects the functionality and safety of structures, the significance which new methods and computational tools have on enabling quick, easy, and simple prognosis of the same is quite clear. This paper will consider the application of fuzzy neural networks by creating prognostic models for determining fire resistance of eccentrically loaded reinforced concrete columns.

A Statistical Image Feature‐Based Deep Belief Network for Fire Detection

“…Traditional machine learning methods are support vector machine (SVM) [18], fuzzy neural networks [19], smoke segmentation-based local binary pattern Silhouettes coefficient variant (LSPSCV) [20], color segmentation-based radial basis function (RBF) nonlinear Gaussian kernel-based binary SVM [21], color segmentation-based fuzzy model [22], and fire frame segmentation-based Markov random field [23]. However, most existing studies on fire detection train models using smoke or flame from videos are often difficult to track more complicated smoke situations.…”

A Stepwise Risk Assessment for Explosion Events considering Probability Distribution of Explosion Load Parameters

“…As a result of increasing energy demand, the offshore oil and gas industry is using the advanced technologies to explore more remote, deeper, and harsh environments [1][2][3][4][5][6]. Thus, hazard for accidental loads and the related engineering problems are increasing and both the risk management for such an issue and technical design method for getting a resistance against the potential incidents are becoming more challenging [7]. Speaking of a floating production storage and offloading (FPSO) facility, it is a representative multifunctional structural system in the offshore industry.…”

Importance of artificial neural networks in civil engineering: a systematic review of the literature

“…neuronas en la segunda capa oculta y 1 neurona en la capa de salida, estableciendo como variables de entrada la resistencia a la compresión, la velocidad de pulso y el peso unitario. Asimismo, la red neuronal predijo la resistencia a la compresión del concreto con un R 2 de 0,7027 y un error promedio fue de 9,3%; también, los datos de la resistividad eléctrica del concreto simulados tuvieron un R 2 de 0,4281 y un error promedio de 30,7%.3.2.4 Resistencia al fuegoEn[16] se propuso el modelo de RNA Adaptive Neuro-Fuzzy Interface System (ANFIS) para pronosticar la resistencia al fuego de las columnas de concreto, para ello construyeron una base de datos a partir de los resultados obtenidos del análisis numérico a los elementos estructurales de los diseños de las edificaciones. En total, fueron analizaron 398 series de datos que conformaron el conjunto de datos de las cuales 318 (80%) fueron utilizados para el entrenamiento y 80 (20%) para validación.…”