Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Predicting the Shear Strength of Reinforced Concrete Beams Using Support Vector Machine
A wide range of machine learning techniques have been successfully applied to model different civilengineering systems. The application of support vector machine (SVM) to predict the ultimate shearstrengths of reinforced concrete (RC) beams with transverse reinforcements is investigated in thispaper. An SVM model is built trained and tested using the available test data of 175 RC beamscollected from the technical literature. The data used in the SVM model are arranged in a format ofnine input parameters that cover the cylinder concrete compressive strength, yield strength of thelongitudinal and transverse reinforcing bars, the shear-span-to-effective-depth ratio, the span-toeffective-depth ratio, beam’s cross-sectional dimensions, and the longitudinal and transversereinforcement ratios. The relative performance of the SVMs shear strength predicted results were alsocompared to ACI building code and artificial neural network (ANNs) on the same data sets.Furthermore, the SVM shows good performance and it is proved to be competitive with ANN modeland empirical solution from ACI-05.
Predicting the Shear Strength of Reinforced Concrete Beams Using Support Vector Machine
A wide range of machine learning techniques have been successfully applied to model different civilengineering systems. The application of support vector machine (SVM) to predict the ultimate shearstrengths of reinforced concrete (RC) beams with transverse reinforcements is investigated in thispaper. An SVM model is built trained and tested using the available test data of 175 RC beamscollected from the technical literature. The data used in the SVM model are arranged in a format ofnine input parameters that cover the cylinder concrete compressive strength, yield strength of thelongitudinal and transverse reinforcing bars, the shear-span-to-effective-depth ratio, the span-toeffective-depth ratio, beam’s cross-sectional dimensions, and the longitudinal and transversereinforcement ratios. The relative performance of the SVMs shear strength predicted results were alsocompared to ACI building code and artificial neural network (ANNs) on the same data sets.Furthermore, the SVM shows good performance and it is proved to be competitive with ANN modeland empirical solution from ACI-05.
Predicting the Shear Strength of Reinforced Concrete Beams Using Support Vector Machine
Lesmana, Cindrawaty (Autor:in)
24.03.2019
doi:10.28932/jts.v2i2.1257
Jurnal Teknik Sipil; Vol 2 No 2 (2006): Jurnal Teknik Sipil; 74-95 ; 2549-7219 ; 1411-9331
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC:
690
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