چکیده مقاله
Recent advances in artificial intelligence and machine learning are reshaping the field of materials science by enabling predictive frameworks that simplify the design process, substantially reducing reliance on traditional, costly, and labor intensive testing methods This study explores the potential of four machine learning techniques: Deep Neural Networks, Random Forest, Light Gradient Boosting Machine, and eXtreme Gradient Boosting, in predicting the compressive strength of concrete While many studies have applied machine learning to this problem using the same established dataset, a notable gap in the literature is the reliance on validation methods prone to optimistic bias The primary contribution of this work is the application of a more methodologically rigorous evaluation framework to address this gap We employ a nested cross validation CV scheme, where the outer loop provides an unbiased measure of model generalization while the inner loop systematically fine tunes hyperparameters By mitigating the risk of overfitting, this robust approach provides a comprehensive and reliable benchmark of each algorithm's true performance These insights offer practitioners trustworthy guidance for forecasting concrete compressive strength, ultimately contributing to more efficient and reliable construction processes
کلیدواژهها
نویسندگان
Hossein Babaei
B Sc in Civil Engineering, High Performance Computing Laboratory, School of Civil Engineering, College of Engineering, University of Tehran, Iran
Mohammad Zamani
M Sc in Civil Engineering, High Performance Computing Laboratory, School of Civil Engineering, College of Engineering, University of Tehran, Iran
Soheil Mohammadi
Professor, High Performance Computing Laboratory, School of Civil Engineering, College of Engineering, University of Tehran, Iran
شیوه ارجاع
Babaei, Hossein and Zamani, Mohammad and Mohammadi, Soheil,1404,Enhancing Concrete Compressive Strength Prediction through Machine Learning Techniques,14th International Congress on Civil Engineering,Tehran
ارائهشده در
مجموعه مقالات چهاردهمین کنگره بین المللی مهندسی عمران29 مهر 1404 · تهران