SavedAdd your journal

Journals / Challenge Journal of Concrete Research Letters / 2021 / Cilt: 12 - Sayı: 1

Application of an artificial neural network for predicting compressive and flexural strength of basalt fiber added lightweight

GÖKHAN ÇALIŞ
SADIK ALPER YILDIZEL
Ulku Sultan KESKİN
Pages
12–19
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The most important strength contributing factors were analyzed within the scope of this study.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Concrete is known as one of the fundamental materials in construction with its high amount of use.
📊
Key Findings & Data:It is proved that the error rate of ANN is smaller than regression method.
💡
Conclusion & Impact:The most important strength contributing factors were analyzed within the scope of this study.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Özet

Concrete is known as one of the fundamental materials in construction with its high amount of use. Lightweight concrete (LWC) can be a good alternative in reducing the environmental effect of concrete by decreasing the self-weight and dimensions of the structure. In order to reduce self-weight of concrete artificial aggregates, some of which are produced from waste materials, are utilized, and it also contributes to develop a sustainable material Artificial neural networks have been the focus of many scholars for long time with the purpose of analyzing and predicting the lightweight concrete compressive and flexural strengths. The artificial neural network is more powerful method in terms of providing explanation and prediction in engineering studies. It is proved that the error rate of ANN is smaller than regression method. Furthermore, ANN has superior performance over nonlinear regression model. In this paper, an ANN based system is proposed in order to provide a better understanding of basalt fiber reinforced lightweight concrete. In the regression analysis predicted vs. experimental flexural strength, R-sqr is determined to be 86%. The most important strength contributing factors were analyzed within the scope of this study.

🔖

Cite This Article

ÇALIŞ, G., YILDIZEL, S. A., KESKİN, U. S. (2021). Application of an artificial neural network for predicting compressive and flexural strength of basalt fiber added lightweight. Challenge Journal of Concrete Research Letters, Cilt: 12 - Sayı: 1, 12–19. https://ajindex.com/articles/153252

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (11)

  1. Allahyari HM, Nikbin I, Rahimi RS, Heidarpour A (2018). A new approach to determine strength of Perfobond rib shear connector in steel-concrete composite structures by employing neural network. Engineering Structures, 15, 235–249.
  2. Alengaram UJ, Ghazali NB, Jumaat MZ, Yusoff S, Bashar I, Islam A (2017). Influence of steel fibers on the mechanical properties and impact resistance of lightweight geopolymer concrete. Construction and Building Materials, 152, 964–977.
  3. Altun F, Kişi Ö, Aydin K (2008). Predicting the compressive strength of steel fiber added lightweight concrete using neural network. Computational Materials Science, 42(2), 259–265.
  4. Behnood A, Golafshani EM (2018). Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves. Journal of Cleaner Production, 202, 54–64.
  5. Bhanja S, Sengupta B (2005). Influence of silica fume on the tensile strength of concrete. Cement and Concrete Research, 35(4), 743– 747. Calis G, Yıldızel SA (2019). Investigation of roller compacted concrete: Literature review. Challenge Journal of Concrete Research Letters, 10(3), 63–74.
  6. Castelli M, Vanneschi L, Silva S (2013). Prediction of high performance concrete strength using Genetic Programming with geometric semantic genetic operators. Expert Systems with Applications, 40(17), 6856–6862.
  7. Chopra P, Sharma RK, Kumar M (2019). Prediction of compressive strength of high-volume fly ash concrete using artificial neural network. Advances in Materials Science and Engineering, 25, 471–483.
  8. Duan ZH, Kou SC, Poon CS (2013). Prediction of compressive strength of recycled aggregate concrete using artificial neural networks. Construction and Building Materials, 40, 1200–1206.
  9. Dvir D, Ben-David A, Sadeh A, Shenhar AJ (2006). Critical managerial factors affecting defense projects success: A comparison between neural network and regression analysis. Engineering Applications of Artificial Intelligence, 19(5), 535–543.
  10. Gavin HP (2013). The Levenberg-Marquardt method for nonlinear least squares curve-fitting problems. Department of Civil and Environmental Engineering Proceedings, Duke University, NC, USA. Getahun MA, Shitote SM, Abiero-Gariy ZC (2018). Artificial neural network based modelling approach for strength prediction of concrete incorporating agricultural and construction wastes. Construction and Building Materials, 190, 517–525.
  11. Hamad AJ (2017). Size and shape effect of specimen on the compressive strength of HPL