پیش‌بینی عمق موضعی آبشستگی پایه‌های پل با استفاده از ترکیب الگوریتم‌های بهینه‌سازی ازدحام ذرات و گرگ خاکستری
محورهای موضوعی : تحلیل، طراحی و ساخت سازه های آبی
1 - دانشکده مهندسی عمران، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
2 - دانشکده مهندسی عمران، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
کلید واژه: آبشستگی موضعی, الگوریتم ازدحام ذرات- گرگ خاکستری, پایه‌های پل, داده‌های صحرایی ,
چکیده مقاله :
ساخت پایه‌های پل یک کار پرهزینه بوده و آبشستگی در نزدیکی پایه‌های پل باعث ناپایداری آن‌ها و در صورت عدم اتخاذ راهکاری مناسب در نهایت موجب تخریب این سازه می‌گردد. بنابراین مطالعه دقیق جهت شناخت این پدیده و عوامل موثر بر آن الزامی است. لذا در این تحقیق، بر اساس مجموعه وسیعی از داده‌های صحرایی برای اندازه‌گیری عمق آبشستگی موضعی پایه‌های پل، معادله‌ای متشکل از پارامترهای مؤثر بر عمق آبشستگی پیشنهاد گردید، برای تعیین این رابطه یک مدل بهینه‌سازی تعریف شد و متغیرهای تصمیم این مدل را با الگوریتم فرا ابتکاری ترکیبی گرگ خاکستری- ازدحام ذرات (HPSGWO) برآورد گردید. برای این منظور روابط مختلفی برای تعیین عمق آبشستگی تعیین شد. بر اساس معادله‌های مختلف تعیین شده، عمق آبشستگی موضعی پایه‌های پل محاسبه شد. برای ارزیابی روابط از شاخص‌های اندازه‌گیری خطا RMSE، RSR، NSE، PBIAS و CC استفاده شد. با مقایسه شاخص‌های اندازه‌گیری خطا برای روابط بدست آمده، بهترین ترکیب ورودی از پارامترها و رابطه ریاضی بهترین مدل برای محاسبه عمق آبشستگی تعیین شد. این شاخص‌ها برای مدل برتر به ترتیب برابر m504/0، 52/0، 73/0، 7/7، 734/0 است. این مقادیر نشان می‌دهد معادله ارائه شده در این تحقیق برای محاسبه عمق آبشستگی مناسب و نسبت به روش‌های تجربی ارائه شده قابل اعتمادتر است. در رابطه پیشنهادی عمق آبشستگی با عدد فرود و نسبت عرض پایه به عمق آب نسبت مستقیم و با اندازه متوسط ذرات بستر نسبت به عمق آب نسبت معکوس دارد.
Construction of bridge piers is expensive, and scouring near them can lead to instability. Without a suitable solution, it can ultimately result in the structure’s destruction. Therefore, a detailed study is required to understand this phenomenon and the factors affecting it. This research entails utilizing extensive field data to measure the local scour depth around bridge piers. It proposes an equation comprising scour-affecting parameters and defines an optimization model to establish this relationship. The decision variables of this model were determined using a meta-heuristic algorithm called the hybrid gray wolf-particle swarm (HPSGWO). For this purpose, various relationships were established to ascertain scour depth, and subsequently, the local scour depth of the bridge piers was calculated, based on these equations. Root Mean Square Error (RMSE), Relative Square Root (RSR), Nash-Sutcliffe Efficiency (NSE), Percent Bias (PBIAS), and Correlation Coefficient (CC) were employed as error measurement indices to evaluate the relationships. Upon comparison of the error measurement indices for the obtained relationships, the best input parameter combination and mathematical relationship for calculating scour depth were determined. These indices for the superior model are equal to 0.504 m, 0.52, 0.73, 7.7%, and 0.734 for RMSE, RSR, NSE, PBIAS, and CC, respectively. These values show that the equation presented in this research is suitable for calculating scour depth and is more reliable than the presented experimental methods. In the proposed relationship, scour depth is directly proportional to the Froude number and the ratio of base width to water depth while inversely proportional to the average size of bed particles to water depth.
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