نشریه علمی-پژوهشی مکانیک سنگ

نشریه علمی-پژوهشی مکانیک سنگ

پیش‌بینی مقاومت سایشی سنگ‌های ساختمانی با استفاده از مدل‌های رگرسیونی و الگوریتم جنگل تصادفی

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشجوی دکتری مهندسی معدن، دانشکده مهندسی معدن، دانشگاه صنعتی اصفهان، اصفهان، ایران.
2 دانشیار گروه مهندسی معدن دانشگاه صنعتی اصفهان، اصفهان، ایران.
چکیده
این مطالعه با هدف پیش‌بینی شاخص باهمه (BAV)، به عنوان معیاری از مقاومت سایشی سنگ‌های ساختمانی، با استفاده از مدل‌های رگرسیونی و الگوریتم‌های یادگیری ماشین انجام شده است. برای این منظور، 160 مجموعه داده شامل پارامترهای فیزیکی سنگ‌ها از جمله درصد تخلخل (N)، جذب آب (Wa) و سختی شور (SHV) جمع‌آوری و تحلیل شدند. نتایج نشان داد که هر سه پارامتر تاثیر قابل توجهی بر مقاومت سایشی دارند، به‌ طوری ‌که با افزایش تخلخل و جذب آب، مقاومت سایشی کاهش و با افزایش سختی شور، مقاومت سایشی افزایش می‌یابد. مدل‌های رگرسیونی چندمتغیره و الگوریتم جنگل تصادفی با دقت بالایی مقدار BAV را پیش‌بینی کردند، به ‌ویژه مدل جنگل تصادفی که توانست با شاخص‌های عملکردی بالاتر، از جمله ضریب تعیین R2 (0.821)، خطای ریشه میانگین مربعات نرمال ‌شدهNRMSE (0.114) ، واریانس خطاVAF (0.821) و میانگین قدر مطلق خطا MAE (4.576) دقت بهتری ارایه دهد. این مدل‌ها نه تنها نیاز به آزمایش‌های فیزیکی پرهزینه و زمان‌بر را کاهش می‌دهند، بلکه به مهندسان کمک می‌کنند تا در انتخاب مصالح ساختمانی مناسب برای شرایط پرتردد و محیط‌های سخت، تصمیم‌گیری بهتری داشته باشند.
کلیدواژه‌ها
موضوعات

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