پژوهشنامه حمل و نقل

پژوهشنامه حمل و نقل

ارزیابی دقت روش‌های تجربی و الگوریتم‌های یادگیری ماشین در تعیین ظرفیت باربری شمع‌های کوبشی جنوب ایران

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشجوی دکتری، گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه آزاد اسلامی واحد تهران مرکزی، تهران، ایران
2 دانشیار، گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه آزاد اسلامی واحد تهران مرکزی، تهران، ایران
3 استادیار، گروه مهندسی ژئوتکنیک، دانشکده مهندسی عمران‌، آب و محیط زیست، دانشگاه شهید بهشتی، تهران، ایران
چکیده
شمع‌های فراساحلی از ارکان کلیدی در پروژه‌های ساخت‌وساز دریایی به شمار می‌روند و تأمین استحکام و پایداری سازه‌ها در برابر بارهای اعمالی بر عهده آن‌هاست. ازاین‌رو، پیش‌بینی دقیق ظرفیت باربری این شمع‌ها نقشی تعیین‌کننده در طراحی و اجرای مطمئن سازه‌های فراساحلی دارد. در پژوهش حاضر، دقت روش‌های مختلف پیش‌بینی ظرفیت باربری شمع‌های فولادی نصب‌شده در رسوبات خاکی فراساحلی جنوب ایران مورد مقایسه قرار گرفته است. برای این منظور، علاوه بر استفاده از روش‌های تجربی طراحی استاتیکی API و رویکردهای مبتنی بر آزمایش نفوذ مخروط (CPT) شامل NGI، ICP، UWA و Fugro، هفت الگوریتم یادگیری ماشین (ML) شامل درخت تصمیم (DT)، رگرسیون خطی (LR)، نزدیک‌ترین همسایه (KNN)، شبکه‌های عصبی(NN) ، رگرسیون بردار پشتیبان(SVR) ، گرادریان بوستینگ سبک (LGBM) و ناییو بیز (NB) نیز مورد ارزیابی قرار گرفتند. نتایج نشان می‌دهد که الگوریتم رگرسیون خطی (LR) بالاترین دقت را در پیش‌بینی ظرفیت باربری نوک شمع‌ها داشته و پس از آن الگوریتم NB و روش API قرار دارند. در خصوص پیش‌بینی ظرفیت باربری جدار شمع‌ها، روش Fugro بهترین عملکرد را ارائه می‌دهد و الگوریتم NB و روش‌های UWA و API به ترتیب در رده‌های بعدی قرار می‌گیرند.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Evaluation of the Accuracy of Empirical Methods and Machine Learning Algorithms in Determining the Bearing Capacity of Driven Piles in Southern Iran

نویسندگان English

Mohammad Mahdi Bayat Tork 1
Amirali Zad 2
Babak Ebrahimian 3
1 Ph.D., Student,Department of Civil Engineering, CT.C., Islamic Azad University, Tehran, Iran.
2 Associate Professor, Department of Civil Engineering, CT.C., Islamic Azad University, Tehran, Iran.
3 Department of Geotechnical Enginnering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University (SBU), Tehran, Iran.
چکیده English

Offshore piles are one of the fundamental components in marine construction projects and are responsible for providing strength and stability to structures against applied loads. Therefore, accurate prediction of the bearing capacity of these piles is of great importance in the design and construction of offshore structures. In this study, the accuracy of different methods for predicting the bearing capacity of steel piles installed in offshore soil deposits of southern Iran is evaluated and compared. For this purpose, in addition to conventional empirical static design methods such as the API approach and cone penetration test (CPT)-based methods including NGI, ICP, UWA, and Fugro, seven machine learning (ML) algorithms were also assessed. These algorithms include Decision Tree (DT), Linear Regression (LR), K-Nearest Neighbors (KNN), Neural Networks (NN), Support Vector Regression (SVR), Light Gradient Boosting Machine (LGBM), and Naïve Bayes (NB).The results indicate that the Linear Regression (LR) algorithm provides the highest accuracy in predicting the pile tip bearing capacity, followed by the NB algorithm and the API method. Regarding the prediction of pile shaft bearing capacity, the Fugro method demonstrates the best performance, while the NB algorithm and the UWA and API methods rank in the subsequent positions.

کلیدواژه‌ها English

Offshore pile bearing capacity
machine learning algorithms
linear regression
neural networks
PDA analysis
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