پیش‌بینی مدول برجهندگی خاک‌های ریزدانه با استفاده از شبکه عصبی مصنوعی، ماشین بردار پشتیبان و سیستم استنتاج تطبیقی عصبی-فازی بهینه‌سازی‌شده با الگوریتم ازدحام ذرات

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، دانشکده مهندسی عمران، دانشگاه صنعتی سیرجان، سیرجان، ایران

2 دانش آموخته کارشناسی ارشد، دانشکده مهندسی عمران، دانشگاه صنعتی سیرجان، سیرجان، ایران

چکیده

مدول برجهندگی خاک بستر ازجمله پارامترهای بسیار مهم در تحلیل و طراحی روسازی‌ است. این پارامتر هم در روش‌های تجربی (مانند اشتو 1993) و هم در روش‌های مکانیستیک-تجربی (مانند MEPDG) به عنوان اصلی‌ترین پارامتر برای بیان مقاومت و خصوصیات مکانیکی خاک بستر مورداستفاده قرار می‌گیرد. برای تعیین این پارامتر نیاز است تا آزمایش بارگذاری سه محوری دینامیک تحت تنش‌های محدود‌کننده و تنش‌های انحرافی مختلف بر روی خاک انجام شود که انجام این آزمایش‌ها بسیار وقت‌گیر و پرهزینه است. در این مقاله عملکرد سه روش ترکیبی هوش محاسباتی شامل شبکه عصبی مصنوعی بهینه‌سازی شده با الگوریتم ازدحام ذرات (ANN-PSO)، ماشین بردار پشتیبان بهینه‌سازی شده با الگوریتم ازدحام ذرات (SVM-PSO) و سیستم استنتاج تطبیقی عصبی-فازی بهینه‌سازی شده با الگوریتم ازدحام ذرات (ANFIS-PSO) به‌منظور پیش‌بینی مدول برجهندگی مصالح خاک بستر ریزدانه مورد ارزیابی قرار گرفته است و نتایج این سه روش با یکدیگر مقایسه گردیده‌ است. در کلیه این مدل‌ها درصد عبوری از الک نمره 200، حد روانی، شاخص خمیری، درصد رطوبت بهینه، درصد رطوبت، درجه اشباع، مقاومت فشاری تک‌محوری، تنش محدودکننده و تنش انحرافی به عنوان ورودی و مدول برجهندگی به عنوان پارامتر خروجی در نظر گرفته شد. نتایج این تحقیق نشان می‌دهد که روش ANN-PSO بیش‌ترین دقت را در پیش‌بینی مدول برجهندگی خاک‌های ریزدانه فراهم می‌سازد. ضریب رگرسیون حاصل از این روش برای مجموع کل داده‌ها برابر با 992/0 است و این روش در اکثر موارد مقدار مدول برجهندگی را با درصد خطای کمتر از 20 درصد پیش‌بینی می‌کند. ضریب رگرسیون حاصل از دو روش SVM-PSO وANFIS-PSO به ترتیب برابر با  989/0 و 951/0 است. نتایج این تحقیق همچنین نشان داد که درصد مصالح عبوری از الک نمره 200 بیشترین تأثیر و پارامتر تنش انحرافی کمترین تأثیر را بر روی مدول برجهندگی مصالح خاکی ریزدانه دارند.

کلیدواژه‌ها

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