Prediction of Long-Term Strength of Concrete Based on Artificial Neural Network

Abstract:

Recently, the safety of existing civil engineering structures attracts more and more attention. The long-term strength of concrete plays a key role during the assessment of safety and durability for civil engineering structures. The strength of concrete will gradually decrease during the service of civil engineering structures. It is significant to accurately predict the strength deterioration of concrete for correctly evaluating the safety of structures. The factors affecting the long-term strength of concrete include environment type, age, climate, water cement ratio, amount of cementing material and so on. In this paper, artificial neural network with powerful mapping ability has been selected to predict the long-term strength of concrete. First, there-layer BP neural network with age, water cement ratio, amount of cementing material as input and long-term strength as output was built. Then, the neural network was trained by the samples measured in real structures and the well-trained neural network was test. From the test results, the trained neural network can accurately predict the long-term strength of concrete with the error less then 9%.

• Periodical: Applied Mechanics and Materials (Volumes 357-360)
• Pages: 905-908
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.357-360.905
• Online since: August 2013
• Authors: Xiao Ming Yang*, Dan Shi
• Keywords: Artificial Neural Network (ANN), Concrete, Long-Term Strength, Prediction

References

[1] Ou Jin-ping. Research and Practice of Smart Sensor Networks and Health Monitoring for Civil Infrastructures in Mainland China. Bulletin of National Science Foundation of China, 19(1): 8-12. (2005).

[2] Pan Hong-ke, Bian Ya-dong, Yang Lin-hong. Reliability Analysis of Reinforced Concrete Structure Based on Durability and Deterioration Grade. Journal of Building Structures, 32(1): 105-109. (2011).

[3] Cheng Yun-hong, Liu Bin. Present and Tendency of the Research on Durability of Reinforced Concrete Structure. Journal of Northeastern University, 24(6): 600-605. (2003).

[4] Lautour de OR., Omenzetter P. Prediction of seismic-induced structural damage using artificial neural networks. Engineering Structures, 31, 600-606, (2009).

DOI: 10.1016/j.engstruct.2008.11.010

[5] Okan Karahan, Harun Tanyildizi, Cengiz D. Atis. An Artificial Neural Network Approach for Prediction of Long-term Strength Properties of Steel Fiber Reinforced Concrete Containing Fly Ash. Journal of Zhejiang University Science A 9(11): 1514-1523. (2008).

DOI: 10.1631/jzus.a0720136

[6] Nie Fa-yuan, Wang Tian-zhu, Li Li-xia. Study and Application on Evaluation for Later Age Strength of Concrete. Ash Comprehensive Utilization. (1): 40-42. (2008).

[7] Su Zheng. Confecting and Application of C60 Pumping Concrete Containing Fly Ash. Concrete, 2: 31-36. (1996).