Prediction of temperature of tubular truss under fire using artificial neural networks

Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy

“…Diameter ratio (β), the wall thickness ratio (τ), the diameter-thickness ratio (γ), and the load ratio [86] Steel columns Hybrid neural network and genetic algorithm…”

Collapse of I-section tapered beam-columns in medium-span steel frames: Finite element model validation and parameters influence evaluation

“…Areas like i) buckling load prediction (Mukherjee et al 1996, Sharifi andTohidi 2014), ii) bearing capacity prediction (Chuang et al 1998, Gandomi et al 2013, iii) constitutive modeling (Jung andGhaboussi 2006, Oeser andFreitag 2016), iv) structural reliability and/or optimization (Adeli andPark 1995, Papadrakakis andLagaros 2016), or v) structural health monitoring (Masri et al 2000, Min et al 2012, have received special focus until today. Many successful ANN-based models have been proposed to assess the behavior of metals and structures, when composed by prismatic members (Sourmail et al 2002, Guzelbey et al 2006, Efstathiadesa et al 2007, Lu et al 2009, Sheidaii and Bahraminejad 2012, Xu et al 2013, Tohidi and Sharifi 2015, Nazari et al 2015, Banu and Rani 2016. Several works have revealed a huge decrease in computing time when comparing the proposed ANN model with the FEA counterpart, and without compromising accuracy -e.g., when estimating the temperature of a tubular truss under fire, Xu et al (2013) concluded that the ANN computes the desired output 1800 times faster than FEA.…”

“…Many successful ANN-based models have been proposed to assess the behavior of metals and structures, when composed by prismatic members (Sourmail et al 2002, Guzelbey et al 2006, Efstathiadesa et al 2007, Lu et al 2009, Sheidaii and Bahraminejad 2012, Xu et al 2013, Tohidi and Sharifi 2015, Nazari et al 2015, Banu and Rani 2016. Several works have revealed a huge decrease in computing time when comparing the proposed ANN model with the FEA counterpart, and without compromising accuracy -e.g., when estimating the temperature of a tubular truss under fire, Xu et al (2013) concluded that the ANN computes the desired output 1800 times faster than FEA. Surprisingly, unlike for prismatic members, virtually no effort has been done to develop analysis and design methods for tapered metal members based on ANNs.…”

Model updating of suspended-dome using artificial neural networks

“…ANNs have been used to match the ''damage patterns'' for the purpose of detecting damage locations and estimating their severity (Lam et al 2006). Recently, multilayered networks trained by the back-propagation (BP) algorithm have also been applied extensively to solve various engineering problems (Tsai et al, 2002;Xu et al, 2013).…”