پیش‌بینی عمق کربناته شدن بتن و بررسی عوامل تأثیرگذار از طریق مدل‌‌های یادگیری ماشین و بهینه‌سازی

منصوری, شعیب; رهایی, علیرضا; راشدی, سیدهادی; مقدس نژاد, فریدون

doi:10.22060/ceej.2024.22980.8086

دانشگاه صنعتی امیرکبیر

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	پیش‌بینی عمق کربناته شدن بتن و بررسی عوامل تأثیرگذار از طریق مدل‌‌های یادگیری ماشین و بهینه‌سازی
نشریه مهندسی عمران امیرکبیر
مقاله 5، دوره 56، شماره 12، 1403، صفحه 1583-1604 اصل مقاله (1.14 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22060/ceej.2024.22980.8086
نویسندگان
شعیب منصوری؛ علیرضا رهایی؛ سیدهادی راشدی؛ فریدون مقدس نژاد^*
دانشکده مهندسی عمران و محیط زیست، دانشگاه صنعتی امیرکببر، تهران، ایران
چکیده
پیش‌بینی دقیق عمق کربناته شدن بتن برای محافظت در برابر پیامدهای زیان‌بار مانند ترک‌‌خوردگی و خوردگی بسیار مهم است. با این وجود، به دلیل پیچیدگی‌های فرآیند و انبوه متغیرهای موجود، تشخیص پارامترهایی که بیشترین اهمیت را در مدل‌سازی عمق کربناته بتن دارند، چالش‌ بزرگی محسوب می‌‌شود. این مطالعه به توسعه یک روش انتخاب ویژگی هایبرید (ترکیبی) به نام MOEA/D-ANN می‌‌پردازد. هدف این روش شناسایی مهم‌‌ترین متغیرهایی است که به دستیابی بالاترین دقت پیش بینی کمک می کنند. این روش پیشنهادی الگوریتم تکاملی بهینه‌‌سازی چند هدفه مبتنی بر جداسازی را با شبکه‌های عصبی مصنوعی ترکیب می‌کند تا با استفاده از قدرت روش‌های بهینه‌سازی و یادگیری ماشین، مشکل انتخاب ویژگی را به طور موثر حل کند. برای ارزیابی عملکرد روش معرفی شده از الگوریتم (RReliefF) که یک الگوریتم رتبه بندی ویژگی است ، نیز بهره گرفته شده است. از روش ANN برای پیش‌بینی عمق کربناته بتن و از روش‌‌های ترکیبیMOEA/D-ANN و RReliefFبرای یافتن متغیرهای‌‌ تأثیرگذار استفاده شده است. نتایج بدست آمده نشان داده اند که مدل ایجاد شده با استفاده از رویکرد MOEA/D-ANN، با ترکیب متغیرهای تعیین‌شده توسط آن، درصد خطا را کاهش و دقت را افزایش داد. در این مدل به مقدار قابل‌‌توجه ضریب تعیین R² = 0.99 می‌‌رسد، که بر دقت خوب آن در پیش بینی عمق کربناته بتن و تأیید انتخاب دقیق متغیرهای تأثیرگذار تأکید می‌‌کند. همچنین نتایج نشان داده که با افزایش نسبت آب به سیمان به میزان 10 درصد، عمق کربناته شدن بتن به میزان 15 درصد افزایش می‌یابد.
کلیدواژه‌ها
عمق کربناته شدن؛ پیش‌‌بینی؛ یادگیری ماشین؛ شبکه عصبی مصنوعی؛ MOEA/D؛ بهینه‌‌سازی
موضوعات
یادگیری ماشین؛ بهینه سازی؛ تعمیر و تقویت روسازی؛ تکنولوژی بتن؛ تکنولوژی بتن
عنوان مقاله [English]
Predicting Concrete Carbonation Depth and Investigating the Influencing Factors through Machine Learning Approaches and Optimization
نویسندگان [English]
Shoaib Mansouri؛ Alireza Rahai؛ Seyed Hadi Rashedi؛ Fereidoon Moghadas Nejad
Civil and Environmental Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
چکیده [English]
Accurate prediction of concrete carbonation depth is crucial for mitigating detrimental effects such as cracking and corrosion. However, due to the complexity of the process and the multitude of variables involved, identifying the most significant parameters for modeling carbonation depth poses a considerable challenge. This paper introduces a hybrid feature selection method known as MOEA/D-ANN. The primary aim of this method is to identify the most critical variables that contribute to achieving the highest prediction accuracy. The proposed approach combines a multi-objective evolutionary optimization algorithm based on decomposition with artificial neural networks to effectively address the feature selection problem using the strengths of optimization and machine learning techniques. To evaluate the performance of the introduced method, the conventional feature ranking algorithm RReliefF was also employed. ANN was used for predicting carbonation depth, while the combined methods of MOEA/D-ANN and RReliefF were utilized to identify influential variables. The results indicate that the model developed using the MOEA/D-ANN approach significantly reduced the error rate and increased accuracy by combining the selected variables. This model achieves a notable coefficient of determination (R² = 0.99), highlighting its excellent accuracy in predicting concrete carbonation depth and confirming the precise selection of influential variables. Additionally, the results demonstrate that an increase in the water-to-cement ratio by 10% leads to a 15% increase in carbonation depth.
کلیدواژه‌ها [English]
Carbonation Depth, Prediction, Machine Learning, Artificial Neural Network, MOEA/D, Optimization

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پیش‌بینی عمق کربناته شدن بتن و بررسی عوامل تأثیرگذار از طریق مدل‌‌های یادگیری ماشین و بهینه‌سازی