نشریه علمی-پژوهشی مکانیک سنگ

نشریه علمی-پژوهشی مکانیک سنگ

پیش‌بینی نرخ نفوذ TBM با استفاده از یادگیری ماشین در سازندهای آذرآواری رشته‌کوه البرز، ایران: تلفیق مدل‌های رگرسیون خطی چندگانه، شبکه عصبی مصنوعی و درخت تصمیم

نوع مقاله : مقاله پژوهشی انگلیسی

نویسندگان
1 دکترای زمین‌شناسی مهندسی، دانشگاه تربیت مدرس، ایران
2 دانشکده زمین‌شناسی، پردیس علوم، دانشگاه تهران، تهران، ایران
3 گروه مهندسی عمران، دانشگاه آزاد اسلامی، واحد شهرکرد، ایران
4 گروه زمین‌شناسی، دانشکده علوم پایه، دانشگاه شیراز، شیراز، ایران
چکیده
این مطالعه به توسعه و مقایسه سه مدل یادگیری ماشین -رگرسیون خطی چندگانه(LMR) ، شبکه عصبی مصنوعی(ANN) ، و درخت طبقه‌بندی و رگرسیون(C&R Tree) - برای پیش‌بینی نرخ نفوذ (PR) ماشین حفار تونل (TBM) می‌پردازد. مجموعه‌داده جامعی شامل 161 نمونه صحرایی از 22.7 کیلومتر تونل انتقال آب کرج، که در سازندهای آذرآواری رشته کوه البرز (ایران) حفر شده است، گردآوری شد. این مجموعه‌داده شامل ویژگی‌های سنگ دست‌نخورده (UCS)، مشخصات توده‌سنگ (RQD، GSI، جهت‌یابی درز)، و پارامترهای عملیاتی ماشین (RPM ، Fn، Fr) است. عملکرد مدل‌ها با استفاده از ضریب تعیین (R²) و میانگین خطای مطلق (MAE) ارزیابی شد. مدل ANN با بیشترین دقت پیش‌بینی R² = و 0.93 MAE =0.02 شناخته شد و پس از آن مدل C&R Tree  R² =0.8 و  MAE =0.05قرار گرفت، در حالی که مدل LMR عملکرد کمتری نشان داد (R² =0.76) و (MAE =0.07)  قابل توجه است که مدل C&R Tree با قابلیت تفسیرپذیری بالا، شاخص مقاومت زمین‌شناسی (GSI) را به عنوان مؤثرترین پارامتر شناسایی کرد. علاوه بر این، این مدل قوانین تصمیم‌گیری صریحی را ایجاد کرد که شرایط زمین‌شناسی خاص مربوط به کمترین (2.38 متر در ساعت) و بیشترین (4.47 متر در ساعت) نرخ نفوذ مشاهده‌شده در گره‌های پایانی را مشخص می‌کند. مجموعاً، در حالی که مدل ANN دقت عددی بالاتری ارائه می‌دهد، مدل C&R Tree بینش‌های تفسیرپذیری ارائه می‌دهد که به طور مؤثری پیش‌بینی کمی و تحلیل کیفی مهندسی را پیوند می‌زند. بنابراین کاربرد هم‌افزای هر دو رویکرد به عنوان چارچوبی مستحکم برای بهینه‌سازی عملیات TBM و افزایش بهره‌وری حفاری تونل پیشنهاد می‌شود
کلیدواژه‌ها

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