A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Estimation of compressive strength of hollow concrete masonry prisms using artificial neural networks and adaptive neuro-fuzzy inference systems
HighlightsANN and ANFIS are used to predict compressive strength of hollow concrete block masonry prisms.ANN and ANFIS show excellent performance in fitting experimental data.ANFIS model performs slightly better than ANN model.ANN and ANFIS outperform empirical methods including masonry codes.
AbstractThis paper proposes the use of artificial neural networks and adaptive neuro-fuzzy inference systems for estimating the compressive strength of hollow concrete block masonry prisms. Three main influential parameters, namely the prisms’ height-to-thickness ratio and the compressive strengths of hollow concrete blocks and mortars, were used as input to the models. The two models were trained and tested using 102 data sets obtained from the tests conducted by the authors as well as published technical literatures and then verified by comparison with other empirical calculation methods. The results showed that the proposed models have excellent prediction ability with insignificant error rates.
Estimation of compressive strength of hollow concrete masonry prisms using artificial neural networks and adaptive neuro-fuzzy inference systems
HighlightsANN and ANFIS are used to predict compressive strength of hollow concrete block masonry prisms.ANN and ANFIS show excellent performance in fitting experimental data.ANFIS model performs slightly better than ANN model.ANN and ANFIS outperform empirical methods including masonry codes.
AbstractThis paper proposes the use of artificial neural networks and adaptive neuro-fuzzy inference systems for estimating the compressive strength of hollow concrete block masonry prisms. Three main influential parameters, namely the prisms’ height-to-thickness ratio and the compressive strengths of hollow concrete blocks and mortars, were used as input to the models. The two models were trained and tested using 102 data sets obtained from the tests conducted by the authors as well as published technical literatures and then verified by comparison with other empirical calculation methods. The results showed that the proposed models have excellent prediction ability with insignificant error rates.
Estimation of compressive strength of hollow concrete masonry prisms using artificial neural networks and adaptive neuro-fuzzy inference systems
Zhou, Qiang (author) / Wang, Fenglai (author) / Zhu, Fei (author)
Construction and Building Materials ; 125 ; 417-426
2016-08-18
10 pages
Article (Journal)
Electronic Resource
English
ANN , artificial neural network , ANFIS , adaptive neuro-fuzzy inference systems , BP , backpropagation , LMBP , Levenberg–Marquardt backpropagation , FIS , fuzzy inference systems , MSE , mean squared error , MAPE , mean absolute percentage error , IAE , integral absolute error , COV , coefficient of variation , Concrete masonry prism , Compressive strength , Artificial neural network , Adaptive network-based fuzzy inference system
| British Library Online Contents | 2016
| British Library Online Contents | 2016
| British Library Online Contents | 2016
Compressive Strength Prediction of Hollow Concrete Block Masonry Prisms
| Online Contents | 2000