Estimation of exposed temperature for fire-damaged concrete using support vector machine

By Bo-Tsun Chen, Ta-Peng Chang, Jeng-Ywan Shih, Jin-Jun Wang. 2009

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How researchers see this paper

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Machine learning in concrete strength simulations: Multi-nation data analytics (2014, methodology): “Other material science problems that have been solved by ML include mixture design, predicting mechanical properties, or fault diagnosis [10,14,16–19].”
Emerging artificial intelligence methods in structural engineering (2018, background): “, strength, elastic modulus), as well as modeling the tensile and compressive strength of normal concrete [196,197].”
Evaluation of pore structures and cracking in cement paste exposed to elevated temperatures by X-ray computed tomography (2013, background): “The significance of these features in assessments of heated concrete has led engineers to evaluate changes in themechanical properties [3–10], color [11–14], evolution of cracking [10,15–21], and spalling [10,22–29] of fire-damaged concrete in relation to various temperatures, and to develop methods”