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Home > Archives > Volume 16, No 6 (2018) > Article

DOI: 10.14704/nq.2018.16.6.1590

Real-time Strength Prediction of Different Types of Concrete Based on BP Neural Network

Zhuo Yang, Mengxiong Tang, Xuan Ji1, Hesong Hu


In terms of information processing, artificial neural network is similar to the synaptic connection structure in the human brain. As a mathematical model for information processing, it is widely used in various fields like biology, medicine and construction engineering. This paper adopts the back propagation neural network and predicts the strength of concrete considering a variety of impact factors. Firstly, it introduces the basic principle of the neural network algorithm based on brain neural mechanism, and summarizes the safety problems in concrete projects and concrete strength prediction methods. With the real-time strength prediction BP model for different types of concrete proposed in this paper, a nonlinear mapping relationship between concrete strength and impact factors can be established, which effectively reduces the number of concrete trials and improve the quality of concrete. The measured results show that the accuracy of concrete strength prediction is higher than 96%. Therefore, this model can provide theoretical guidance for better application of concrete in construction projects.


BP neural network, concrete, impact factor, strength prediction

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Abbasloo AA, Shayanfar MA, Pahlavan H, Barkhordari MA, Hamze-Ziabari SM. Prediction of shear strength of frp-reinforced concrete members using a rule-based method. Magazine of Concrete Research 2018; 1-50.

Beeby AW. Standard practice for concrete for civil works structures (technical engineering and design guides as adapted from the us army corps of engineers, no. 8): American society of civil engineers, New York: ASCE Press, 1994: 122.

Carino NJ. Prediction of potential concrete strength at later ages. Chemistry Letters 1994; (3): 205-06.

Cheng J, Wang X, Si T, Zhou F, Zhou, J, Cen K. Ignition temperature and activation energy of power coal blends predicted with back-propagation neural network models. Fuel 2016; 173: 230-38.

Ilunga M, Stephenson D. Infilling streamflow data using feed-forward back-propagation (bp) artificial neural networks: application of standard bp and pseudo mac laurin power series BP techniques. Water S A 2005; 31(2): 491-503.

Ismail MP, Yusof KM, Abrahim AN. A combined ultrasonic method on the estimation of compressive concrete strength. NDT & E International 1996; 30(4): 781-85.

İlker Bekir Topçu, Sarıdemir M. Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Computational Materials Science2008; 41(3): 305-11.

Lataste JF, Sirieix C, Breysse D, Frappa M. Electrical resistivity measurement applied to cracking assessment on reinforced concrete structures in civil engineering. Ndt & E International 2003; 36(6): 383-94.

Madandoust R, Bungey JH, Ghavidel R. Prediction of the concrete compressive strength by means of core testing using gmdh-type neural network and anfis models. Computational Materials Science 2012; 51(1): 261-72.

Słoński M. A comparison of model selection methods for compressive strength prediction of high-performance concrete using neural networks. Computers & Structures 2010; 88(21): 1248-53.

Stoppel M, Taffe A, Wiggenhauser H, Kurz JH, Boller C. Automated multi-sensor systems in civil engineering for condition assessment of concrete structures. Agriculture Ecosystems & Environment 2012; 147(3): 24-35.

Taşpınar F. Improving artificial neural network model predictions of daily average pm10 concentrations by applying principle component analysis and implementing seasonal models. Journal of the Air & Waste Management Association 2015; 65(7): 800-09.

Trtnik G, Kavcic F, Turk G. Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks. Ultrasonics 2009; 49(1): 53-60.

Wang Z, Xiang B. Application of artificial neural network to determination of active principle ingredient in pharmaceutical quality control based on near infrared spectroscopy. Microchemical Journal 2008; 89(1): 52-57.

Xu J, Ren Q, Shen Z. Prediction of the strength of concrete radiation shielding based on ls-svm. Annals of Nuclear Energy 2015; 85: 296-300.