Prediction of elastic modulus of normal and high strength concrete by support vector machine

By K. Yan, C. Shi. 2010

200 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Machine learning in concrete strength simulations: Multi-nation data analytics (2014, methodology): “Moreover, Yan and Shi reported that SVMwas better than other models for predicting elastic modulus of normal and high strength concrete [21].”
A New Method for Predicting the Ingredients of Self-Compacting Concrete (SCC) Including Fly Ash (FA) Using Data Envelopment Analysis (DEA) (2020, methodology): “Yan and Shi [9] forecasted modulus of elasticity of high and normal strength concrete by SVM.”
Emerging artificial intelligence methods in structural engineering (2018, background): “Yan and Shi [200] investigated the applicability of SVM for predicting the elastic modulus of normal and high strength concrete.”
Computational design optimization of concrete mixtures: A review (2018, background): “A number of other researchers have since used SVMs to predict a variety of concrete mixture properties, such as elastic modulus [77], compressive strength [68,76,78–80], and splitting tensile strength [81].”