The Study of BP Neural Network in Predicting Concrete Strength

Zhao Ming

doi:10.4028/www.scientific.net/AMR.243-249.6169

Paper Titles

Application Analysis of Form-Finding on the Large Building Construction Tower Crane Flexible Attachments
p.6150

Integration Research for Steel Shelf CAD/CAE Calculation and Analysis System
p.6154

Thermomechanical Analysis System for Building Structures under Fire Based on MSCPATRAN Platform
p.6159

The Assembled Structure Analysis of Energy Conservation and Material Saving Unit Sheet Aluminum Curtain Wall
p.6165

The Study of BP Neural Network in Predicting Concrete Strength
p.6169

Accuracies of Finite Beam Element in Prediction of Inelastic Material Behaviors
p.6174

Simulation of Hydrostatic Drive System of Crawler Bulldozer
p.6178

Research on Optimizing Precast Water Head on Preloading Filling Spiral Case of a Large-Scale Hydropower Station
p.6183

Optimization Design of the Gas Station Roof Based on Analysis
p.6188

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 243-249The Study of BP Neural Network in Predicting...

The Study of BP Neural Network in Predicting Concrete Strength

Abstract:

Concrete is a mainly and commonly good combined construction material, and is consisted of many well-defined components, so mechanical properties of concrete are very complex. the compressive strength of the concrete is a main criterion in producing concrete, but the test on it is complicated because test components of concrete must be kept in the special condition an tested after 28 days. To simplify the procedures and obtain a reasonable data, the paper presents a method using the system of BP neural network predicting the strength of concrete. the system is trained and tested by using many data of strength of concrete in the past ,the test result shows that the value of the strength of concrete predicted is approximate to the experimental value, and the method presented is very efficient and reasonable in predicting the compressive strength of concrete .

You might also be interested in these eBooks

Advances in Civil Engineering and Architecture

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 243-249)

Pages:

6169-6173

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.243-249.6169

Citation:

Cite this paper

Online since:

May 2011

Authors:

Zhao Ming

Keywords:

BP Neural Network (BPNN), Concrete, Predicting, Strength, Study

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Ni, H.G., and Wang, J.Z. 2000. "Prediction of compressive strength of concrete by neural networks." Cem. Concr. Res. Vol.30, 1245–1250.

Google Scholar

[2] Oh, J.W., Lee, I.W., Kim, J.T., and Lee, G.W. 1999. "Application of neural networks for proportioning of concrete mixes." ACI Mater. J., Vol.96, No.1, 61–67.

Google Scholar

[3] American Society for Testing and Materials AS- TM.1992."Standard test method for compressive strength of cylindrical concrete specimens." Annual book of ASTM standards: ASTM39-93a, Vol.4, 22–24.

Google Scholar

[4] Aoki,T., Ceravolo,R., DeStefano,A., Genovese, C., and Sabia, D. 2002. "Seismic vulnerability assessment of chemical plants through probabilistic neural networks. "Reliability Eng. Sys. Safety, Vol.77, No.3, 263 –268.

DOI: 10.1016/s0951-8320(02)00059-5

Google Scholar

[5] Rajasekaran, S., and Lee, S.C. 2003. "Prediction of concrete strength using serial functional network model." Struct. Eng. Mech., Vol.16, No.1, 83–99.

DOI: 10.12989/sem.2003.16.1.083

Google Scholar

[6] Specht, D.F. 1990. "Probabilistic neural networks." Neural Networks, Vol.3, 109–11

Google Scholar