SavedAdd your journal

Journals / Mühendislik Bilimleri ve Tasarım Dergisi / 2022 / Cilt: 10 - Sayı: 2

ESTIMATION OF CONCRETE STRENGTH PROPERTIES THROUGH THE RESPONSE SURFACE METHODOLOGY, GENETIC ALGORITHM, AND ARTIFICIAL NEURAL NETWORKS

BETON DAYANIM ÖZELLİKLERİNİN YÜZEY TEPKİ YÖNTEMİ, GENETİK ALGORİTMA VE YAPAY SİNİR AĞLARI İLE TAHMİNİ

Ebru BAŞPINAR TUNCAY
Ekin KÖKEN
Şemsettin KILINÇARSLAN
Pages
429–441
Publication Date
2022-06-30
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 performances of the models obtained were evaluated in the light of some statistical indicators, and it was determined that methods based on genetic algorithms and artificial neural networks could be successfully used to estimate the concrete strength properties.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, concrete strength properties were estimated by surface response method, genetic algorithm, and artificial neural network methods.
📊
Key Findings & Data:Cubic (10x10x10 cm) and prismatic (15x15x60 cm) concrete samples were prepared using six different concrete aggregates, and some models were developed to estimate the uniaxial compressive strength (UCSc) and flexural strength (FSc) of concrete.
💡
Conclusion & Impact:The performances of the models obtained were evaluated in the light of some statistical indicators, and it was determined that methods based on genetic algorithms and artificial neural networks could be successfully used to estimate the concrete strength properties.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Beton Dayanımı · Agrega · Yüzey Tepki Yöntemi

Abstract

In this study, concrete strength properties were estimated by surface response method, genetic algorithm, and artificial neural network methods. Cubic (10x10x10 cm) and prismatic (15x15x60 cm) concrete samples were prepared using six different concrete aggregates, and some models were developed to estimate the uniaxial compressive strength (UCSc) and flexural strength (FSc) of concrete. In the developed models, parameters such as concrete density (ρc), Los Angeles abrasion loss of concrete aggregates (LAA), and P wave velocity (Vpc) of concretes were used. The performances of the models obtained were evaluated in the light of some statistical indicators, and it was determined that methods based on genetic algorithms and artificial neural networks could be successfully used to estimate the concrete strength properties.

Özet

Bu çalışmada, beton dayanım özellikleri yüzey tepki yöntemi, genetik algoritma ve yapay sinir ağları yöntemleri ile analiz edilmiştir. Altı farklı beton agregası kullanılarak küp (10x10x10 cm) ve prizmatik (15x15x60 cm) beton numuneleri hazırlanmış olup, beton tek eksenli basınç dayanımı (UCSc) ve eğilme dayanımının (FSc) tahminlenmesi için bazı tahmin modeller geliştirilmiştir. Geliştirilen modellerde beton yoğunluğu (ρc), beton agregalarının Los Angeles aşınma kaybı (LAA) ve betonlara ait P dalgası hızı (Vpc) gibi parametreler kullanılmıştır. Elde edilen modellerin performansları bazı istatistiksel göstergeler ışığında değerlendirilmiş ve genetik algoritma ve yapay sinir ağlarını temel alan yöntemlerin beton dayanım özelliklerini tahmininde başarılı bir şekilde kullanılabileceği belirlenmiştir.

🔖

Cite This Article

TUNCAY, E. B., KÖKEN, E., KILINÇARSLAN, Ş. (2022). ESTIMATION OF CONCRETE STRENGTH PROPERTIES THROUGH THE RESPONSE SURFACE METHODOLOGY, GENETIC ALGORITHM, AND ARTIFICIAL NEURAL NETWORKS. Mühendislik Bilimleri ve Tasarım Dergisi, Cilt: 10 - Sayı: 2, 429–441. https://ajindex.com/articles/693252

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (9)

  1. American Concrete Institute (ACI) Committee 211, 1993. Guide for Selecting Proportions for High Strength Concrete with Portland Cement and Fly Ash. ACI211.4R-93, Detroit, MI. Armaghani, D.J., Asteris, P.G., 2021. A Comparative Study of ANN and ANFIS Models for the Prediction of Cement-Based Mortar Materials Compressive Strength. Neural Computing and Applications, 33(9), 4501-4532. ASTM C597, 1994. Standard Test Method for Pulse Velocity Through Concrete. Annual book of ASTM Standards, Vol. 04.02. West Conshohocken, PA: American Society for Testing and Materials. Asutkar, P., Shinde, S.B., Patel, R., 2017. Study on the Behaviour of Rubber Aggregates Concrete Beams Using Analytical Approach. Engineering Science and Technology, an International Journal, 20(1), 151-159.
  2. Cihan, M.T., Güner, A., Yüzer, N., 2013. Response Surfaces for Compressive Strength of Concrete. Construction and Building Materials, 40, 763-774.
  3. Deng, F., He, Y., Zhou, S., Yu, Y., Cheng, H., Wu, X., 2018. Compressive Strength Prediction of Recycled Concrete Based on Deep Learning. Construction and Building Materials, 175, 562-569.
  4. Duan, Z.H., Kou, S.C., Poon, C.S., 2013. Prediction of Compressive Strength of Recycled Aggregate Concrete Using Artificial Neural Networks. Construction and Building Materials, 40, 1200-1206.
  5. Feng, D.C., Liu, Z.T., Wang, X.D., Chen, Y., Chang, J.Q., Wei, D.F., Jiang, Z.M., 2020. Machine learning-Based Compressive Strength Prediction for Concrete: An Adaptive Boosting Approach. Construction and Building Materials, 230, 117000. Ferreira, C., 2001. Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems, 13(2), 87-129.
  6. Gallo, C., 2015. Artificial Neural Networks Tutorial. In Encyclopedia of Information Science and Technology, Third Edition (pp. 6369-6378).
  7. IGI Global. Getahun, M.A., Shitote, S.M., Gariy, Z.C.A., 2018. Artificial Neural Network Based Modelling Approach for Strength Prediction of Concrete Incorporating Agricultural and Construction Wastes. Construction and Building Materials, 190, 517-525.
  8. Hammoudi, A., Moussaceb, K., Belebchouche, C., Dahmoune, F., 2019. Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) Prediction in Compressive Strength of Recycled Concrete Aggregates. Construction and Building Materials, 209, 425-436.
  9. Kewalramani, M.A., Gupta, R., 2006. Concrete Compressive Strength Prediction Using Ultrasonic Pulse Velocity Through Artificial Neural Networks. Automation in Const