Presenting a novel approach for estimation the compressive strength of high strength concrete using ANN & GEP

Article Type:
Research/Original Article (دارای رتبه معتبر)
In this article, the application of artificial neural networks in predicting the degree of concrete compressive strength of High Strength Concrete (HSC) was investigated. For this purpose, use was made of the pattern recognition neural network and the obtained data from the experimental tests for predicting the compressive strength degree of HSC. Five inputs from the HSC mix design were utilized for predicting the degree of compressive strength, by application of the scaled conjugate gradient backpropagation algorithm in neural network. The outputs were classified into 5 strength groups of M1, M2, M3, M4 and M5. The simulation results shows 97.9% accuracy in classifying the different predefined degrees of HSC using the confusion matrix diagram. Moreover, the cross-entropy error obtained from testing the neural network (NN) model and correlation coefficient (R2) of GEP for predicting compressive strength of the HSC were evaluated at 0.042096 and 0.9795, respectively, indicating high accuracy of the model. Application of this model could greatly help the persons, companies and research centers in terms of preparation and making of HSC with desired compressive strength, that are in need of this type of concrete.
International Journal of Advanced Structural Engineering, Volume:12 Issue: 1, Spring 2022
606 to 617  
دانلود و مطالعه متن این مقاله با یکی از روشهای زیر امکان پذیر است:
اشتراک شخصی
با عضویت و پرداخت آنلاین حق اشتراک یک‌ساله به مبلغ 1,390,000ريال می‌توانید 70 عنوان مطلب دانلود کنید!
اشتراک سازمانی
به کتابخانه دانشگاه یا محل کار خود پیشنهاد کنید تا اشتراک سازمانی این پایگاه را برای دسترسی نامحدود همه کاربران به متن مطالب تهیه نمایند!
  • حق عضویت دریافتی صرف حمایت از نشریات عضو و نگهداری، تکمیل و توسعه مگیران می‌شود.
  • پرداخت حق اشتراک و دانلود مقالات اجازه بازنشر آن در سایر رسانه‌های چاپی و دیجیتال را به کاربر نمی‌دهد.
In order to view content subscription is required

Personal subscription
Subscribe for 70 € euros via PayPal and download 70 articles during a year.
Organization subscription
Please contact us to subscribe your university or library for unlimited access!