Prediction of the compressive strength of high-performance self-compacting concrete by an ultrasonic-rebound method based on a GA-BP neural network
Authors:
Open Access
- 3 May 2021
- journal article
- research article
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 16 (5) , e0250795
Abstract
To address the problem of low accuracy and poor robustness of in situ testing of the compressive strength of high-performance self-compacting concrete (SCC), a genetic algorithm (GA)-optimized backpropagation neural network (BPNN) model was established to predict the compressive strength of SCC. Experiments based on two concrete nondestructive testing methods, i.e., ultrasonic pulse velocity and Schmidt rebound hammer, were designed and test sample data were obtained. A neural network topology with two input nodes, 19 hidden nodes, and one output node was constructed, and the initial weights and thresholds of the resulting traditional BPNN model were optimized using GA. The results showed a correlation coefficient of 0.967 between the values predicted by the established BPNN model and the test values, with an RMSE of 3.703, compared to a correlation coefficient of 0.979 between the values predicted by the GA-optimized BPNN model and the test values, with an RMSE of 2.972. The excellent agreement between the predicted and test values demonstrates the model can accurately predict the compressive strength of SCC and hence reduce the cost and time for SCC compressive strength testing.Keywords
Funding Information
- Innovative Research Group Project of the National Natural Science Foundation of China (51278187)
This publication has 31 references indexed in Scilit:
- Prediction of compressive strength of recycled aggregate concrete using artificial neural networksConstruction and Building Materials, 2013
- Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networksAdvances in Engineering Software, 2011
- Artificial neural network model for steel–concrete bond predictionEngineering Structures, 2009
- Neural networks for predicting compressive strength of structural light weight concreteConstruction and Building Materials, 2009
- Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networksUltrasonics, 2009
- Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networksAutomation in Construction, 2006
- Neural Networks in Civil Engineering: 1989–2000Computer-Aided Civil and Infrastructure Engineering, 2001
- Concrete strength by combined nondestructive methods simply and reliably predictedCement and Concrete Research, 2000
- Artificial Neural Networks in Hydrology. II: Hydrologic ApplicationsJournal of Hydrologic Engineering, 2000
- A two stage procedure for assessment of in situ concrete strength using combined non-destructive testingMaterials and Structures, 1999