Evolutionary multivariate adaptive regression splines for estimating shear strength in reinforced-concrete deep beams

Authors:
Min-Yuan Cheng;Minh-Tu Cao
Affiliations:
-;-
Venue:
Engineering Applications of Artificial Intelligence
Year:
2014

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Abstract

This study proposes a novel artificial intelligence (AI) model to estimate the shear strength of reinforced-concrete (RC) deep beams. The proposed evolutionary multivariate adaptive regression splines (EMARS) model is a hybrid of multivariate adaptive regression splines (MARS) and artificial bee colony (ABC). In EMARS, MARS addresses learning and curve fitting and ABC implements optimization to determine the optimal parameter settings with minimal estimation errors. The proposed model was constructed using 106 experimental datasets from the literature. EMARS performance was compared with three other data-mining techniques, including back-propagation neural network (BPNN), radial basis function neural network (RBFNN), and support vector machine (SVM). EMARS estimation accuracy was benchmarked against four prevalent mathematical methods, including ACI-318 (2011), CSA, CEB-FIP MC90, and Tang's Method. Benchmark results identified EMARS as the best model and, thus, an efficient alternative approach to estimating RC deep beam shear strength.