پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 163 |
| تعداد شماره‌ها | 6,762 |
| تعداد مقالات | 72,839 |
| تعداد مشاهده مقاله | 131,889,104 |
| تعداد دریافت فایل اصل مقاله | 103,544,501 |
Integrating Machine Learning and Genetic Expression Programming for Enhanced Punching Shear Strength Prediction | ||
| Civil Engineering Infrastructures Journal | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 16 آبان 1403 اصل مقاله (1.3 M) | ||
| شناسه دیجیتال (DOI): 10.22059/ceij.2024.380171.2118 | ||
| نویسندگان | ||
| Alireza Mahmoudian1؛ Nima Tajik2؛ Amirhossein Darabi3؛ Mostafa Mohammadzadeh Taleshi4؛ Saba Marmarchinia2؛ Abazar Asghari* 5؛ Seyed Rasoul Mirghaderi5 | ||
| 1Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran | ||
| 21-Department of Civil, Structural and Environmental Engineering, State University of New York at Buffalo, Buffalo, NY, USA 2-School of Civil Engineering, College of Engineering, University of Tehran, Iran | ||
| 3School of Civil Engineering, Iran University of Science & Technology, Tehran, Iran | ||
| 4Civil and Environmental Engineering Department, University of Nevada, Reno | ||
| 5School of Civil Engineering, College of Engineering, University of Tehran, Iran | ||
| چکیده | ||
| Estimating the punching shear strength of reinforced concrete (RC) flat slabs is critical in structural engineering due to potential catastrophic failures. This study introduces advanced data-driven methods, including machine learning (ML), deep learning (DL), and genetic expression programming (GEP), to improve predictions of punching shear strength. Analyzing a dataset of 380 test samples, the research evaluates various models such as linear regression, stochastic gradient descent, ridge regression, decision trees, K-nearest neighbors, random forests, adaptive boosting, extreme gradient boosting (XGBoost) for ML, alongside artificial neural networks (ANNs) for DL, and GEP for deriving explicit equations. Significant enhancements in model performance were achieved through rigorous hyperparameter tuning, notably with the XGBoost model, which attained an R² (coefficient of determination) score of 0.98, surpassing other models and existing code-based predictions. The study uses SHapley values to interpret model predictions, highlighting the significant impact of slab depth on punching shear strength, especially in the XGBoost model. Additionally, the GEP method derives explicit equations that accurately represent the relationship between input features and punching shear strength. This research highlights the advantages of advanced computational models and offers new insights into the factors influencing punching shear strength in RC slabs. | ||
| کلیدواژه‌ها | ||
| Machine learning؛ Artificial neural networks؛ Extreme gradient boosting؛ Punching shear strength؛ genetic expression programming | ||
|
آمار تعداد مشاهده مقاله: 185 تعداد دریافت فایل اصل مقاله: 124 |
||
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب