کاربرد روش یادگیری عمیق در مدیریت ریسک‌های زیست محیطی پروژه‌های عمرانی با دیدگاه پدافند غیرعامل

امرایی, امین; حسینی, سید عظیم; فرخی زاده, فرشید; حائری, محمدحسن

کاربرد روش یادگیری عمیق در مدیریت ریسک‌های زیست محیطی پروژه‌های عمرانی با دیدگاه پدافند غیرعامل

نوع مقاله : ترویجی

نویسندگان

¹ دانشجوی دکتری، گروه مهندسی عمران، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران

² دانشیار، گروه مهندسی عمران، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران

³ استادیار، گروه مهندسی نگهداری و تعمیرات، دانشکده علوم و مهندسی دفاعی، دانشگاه افسری و تربیت پاسداری امام حسین (ع)، تهران، ایران

⁴ استادیار، گروه مهندسی عمران، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران

چکیده

صنعت ساخت و ساز در کشور ایران از صنایع پیشرو در اقتصاد به شمار رفته و از اقبال قابل توجهی در نظر مردم نیز برخوردار است. این صنعت به عنوان گزینه اول در سرمایه‌گذاری نیز به حساب آمده و بیشترین بازدهی را بنا بر گزارشهای منتشر شده از خود نشان داده است. همچنین خطرات محیط زیستی ناشی از این صنعت نیز از اهمیت بسیار بالایی برخوردار بوده و از دغدغه‌های اصلی مسئولین ذیربط علی‌الخصوص در سالهای اخیر است. این مقاله به مدیریت ریسک محیط‌زیستی در پروژه‌های عمرانی با استفاده از روش بسیار موفق FMEA و با کاربرد روشهای یادگیری ماشین پرداخته است. نوآوری اصلی تحقیق حاضر در به کار بردن همزمان روش نوین یادگیری ماشین و همچنین مدیریت ریسک به کمک روش FMEA است. روش ترکیبی حاضر از دو رویکرد توصیفی و مدلسازی عددی استفاده کرده است. در این مطالعه بیش از 10 پروژه بزرگ در کلان شهر تهران ارزیابی شده و بیش از 20 ریسک محیط‌زیستی شناسایی شده است. پیش‌نیاز مدلسازی به روش یادگیری عمیق به کمک FMEA تهیه شده و سپس مدلسازی با ترکیب روش شبکه عصبی مصنوعی و الگوریتم بهینه سازی MVO انجام شده است. این مطالعه پیش پردازش‌های پر حجمی جهت انتخاب نوع شبکه عصبی و الگوریتم بهینه‌سازی انجام داده است. نتایج تحقیق نشان از کارایی مناسب روش ترکیبی به کار رفته می‌دهند. چهار نوع شبکه عصبی مختلف و دو نوع الگوریتم بهینه‌سازی بر اساس شاخصهای ارزیابی مشخص استفاده شده‌اند و موفق‌ترین نوع آنها معرفی شده است. در نهایت آنالیز حساسیت برای ریسک‌های محیط‌زیستی شناسایی شده بر روی مدلسازی موفق معرفی شده، انجام شده و مهمترین ریسک‌ها به روش حساسیت نسبی و مطلق معرفی شده‌اند.

کلیدواژه‌ها

20.1001.1.20086849.1403.15.4.7.0

عنوان مقاله [English]

The Application of Deep Learning Method in the Management of Environmental Risks of Construction Projects with the View of Passive Defense

نویسندگان [English]

Amin Amraee ¹
Seyed Azim Hosseini ²
Farshid Farokhizadeh ³
Mohammadhasan Haeri ⁴

¹ PhD student, Department of Civil Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

² Associate Professor, Department of Civil Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

³ Assistant Professor, Department of Maintenance and Repair Engineering, Faculty of Defense Science and Engineering, Imam Hossein (AS) Guards Officer Training and Training University, Tehran, Iran

⁴ Assistant Professor, Department of Civil Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

چکیده [English]

The construction industry in Iran is considered one of the leading sectors in the economy and enjoys significant popularity among the people. This industry has also been a top choice for investment and has shown high returns based on published reports. Additionally, environmental risks associated with this industry have been of utmost importance, especially in recent years, for relevant authorities. This article focuses on environmental risk management in civil projects using the highly successful FMEA method and employing machine learning techniques. The main innovation of this research lies in simultaneously utilizing a novel machine learning method and risk management with the help of the FMEA method. The hybrid approach incorporates both descriptive and numerical modeling. Over 10 major projects in the metropolitan area of Tehran have been evaluated in this study, identifying more than 20 environmental risks. The prerequisite modeling using deep learning with FMEA was prepared, followed by modeling using a combination of artificial neural network and the MVO optimization algorithm. This study conducted extensive preprocessing to select the type of neural network and optimization algorithm. The research results demonstrate the effectiveness of the combined method employed. Four different types of neural networks and two optimization algorithms have been utilized based on specific evaluation criteria, with the most successful type being introduced. Finally, sensitivity analysis for identified environmental risks on the successful modeling has been performed, and the most significant risks have been identified using relative and absolute sensitivity methods.

کلیدواژه‌ها [English]

Risk Management
FMEA Method
Deep Learning
Civil Project
Environmental

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