دوره 15، شماره 29 - ( 2-1398 )                   جلد 15 شماره 29 صفحات 167-179 | برگشت به فهرست نسخه ها

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Gandomi M, Dolatshahi Pirooz M, Varjavand I, Nikoo M R. Application of Multilayer Perceptron Neural Network and Support Vector Machine for Modeling the Hydrodynamic Behavior of Permeable Breakwaters with Porous Core. Journal Of Marine Engineering. 2019; 15 (29) :167-179
URL: http://marine-eng.ir/article-1-734-fa.html
گندمی مصطفی، دولتشاهی پیروز محرم، ورجاوند ایمان، نیکو محمد رضا. بکارگیری شبکه‌ عصبی پرسپترون چند‌لایه و ماشین بردار پشتیبانی بمنظور مدلسازی رفتار هیدرودینامیکی موج شکن قائم نفوذپذیر با هسته متخلخل. مهندسی دریا. 1398; 15 (29) :167-179

URL: http://marine-eng.ir/article-1-734-fa.html


دانشگاه تهران
چکیده:   (84 مشاهده)
در این تحقیق امکان بکارگیری شبکه‌ عصبی پرسپترون چند‌لایه و ماشین بردار پشتیبانی بمنظور مدلسازی رفتار هیدرودینامیکی موج­شکن­ های نفوذپذیر قائم با هسته متخلخل مورد بررسی قرار می­گیرد. بدین منظور از داده­ های مطالعه آزمایشگاهی بر روی مدل فیزیکی استفاده شده است تا ضرایب انعکاس و گذر موج برخوردی به سازه که بیانگر رفتار هیدرودینامیکی هستند را به عرض محفظه موج­ شکن، نسبت ارتفاع مصالح سنگی به عمق آب، نسبت عرض محفظه به طول موج، ارتفاع موج، عدد موج در عمق آب و تیزی موج مرتبط شود. نتایج حاکی از آن است مدل شبکه‌ عصبی پرسپترون چند‌لایه نسبت به مدل ماشین بردار پشتیبانی دارای عملکرد بهتری در مدلسازی رفتار هیدرودینامیکی موج­ شکن­ مورد مطالعه بوده و تا حد زیادی به داده­ های واقعی همبسته(0/8689=R برای ضریب انعکاس موج و 0/96629=R برای ضریب گذر) است. در ادامه بمنظور آشکارسازی پاسخ ضرایب انعکاس و گذر به هریک از پارامترهای ورودی مدل برتر، مطالعه پارامتریک انجام گرفته است. همچنین با استفاده از آنالیز حساسیت میزان مشارکت پارامترهای ورودی در پیشبینی ضرایب انعکاس و گذر مورد بررسی قرار گرفته است.
متن کامل [PDF 1357 kb]   (17 دریافت)    
نوع مطالعه: مقاله پژوهشي | موضوع مقاله: سازه های ساحلی
دریافت: ۱۳۹۸/۳/۱۱ | پذیرش: ۱۳۹۸/۴/۲۳

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