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یک روش هوشمند برای طبقهبندی ترک در سازههای بتنی بر اساس شبکههای عصبی عمیق | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 3، دوره 53، شماره 8، آبان 1400، صفحه 3201-3220 اصل مقاله (2.1 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2020.17738.6660 | ||
| نویسندگان | ||
| نوشین بیگدلی* 1؛ حامد جباری2؛ مهدی شجاعی3 | ||
| 1گروه مهندسی بزق-کنترل، دانشکده فنی و مهندسی، دانشگاه بین المللی امام خمینی (ره) | ||
| 2گروه مهندسی برق-کنترل، دانشکده فنی و مهندسی، دانشگاه بین‏‏المللی امام خمینی(ره)، قزوین، ایران، | ||
| 3گروه مهندسی برق-کنترل، دانشکده فنی و مهندسی، دانشگاه بین‏‏المللی امام خمینی(ره)، قزوین، ایران | ||
| چکیده | ||
| شناسایی و بررسی انواع ترکها در سازههای بتنی یکی از موضوعات چالشبرانگیز در حوزه مهندسی به شمار میرود. تشخیص چندشاخگی در ترک به دلیل اینکه موجب شناسایی سطوح شدت بالا در سازههای بتنی میشود، از اهمیت بسزایی برخوردار است. در این مقاله یک معماری جدید بر مبنای شبکههای عصبی کانولوشنی برای طبقهبندی ترک در سازههای بتنی ارائه گردید. معماری پیشنهادی در زمان کمتر و صحت بالاتر نسبت به سایر معماریهای مرسوم و معتبر در یادگیری عمیق، چندشاخگی در ترک را شناسایی و طبقهبندی کرد. در این مقاله ترکهای موجود در 12000 تصویر سازههای بتنی توسط الگوریتم پیشنهادی بررسی شدند که در نتیجه این تصاویر با صحت 99/3 درصد در دستههای تصاویر بدون ترک، تصاویر دارای ترک ساده و تصاویر دارای چندشاخگی در ترک طبقهبندی شدند. همچنین تحلیل ماتریس درهمریختگی نشان از دقت 99/3 درصد و فراخوانی99/5 درصد داشت که تأییدی بر عملکرد مناسب الگوریتم پیشنهادی بود. آنالیز حساسیت الگوریتم پیشنهادی نیز الزام وجود تناسب میان تعداد داده، تعداد نورونهای لایه تماماً متصل، زمان اجرا و درصد صحت مورد انتظار با توجه به کابرد مسأله را نشان داد. | ||
| کلیدواژه‌ها | ||
| ترک؛ سازههای بتنی؛ یادگیری ماشین؛ یادگیری عمیق؛ شبکههای عصبی کانولوشنی | ||
| موضوعات | ||
| پایش سلامت سازه ها؛ کشف عیوب | ||
| عنوان مقاله [English] | ||
| An Intelligent Method for Crack Classification in Concrete Structures Based on Deep Neural Networks | ||
| نویسندگان [English] | ||
| Nooshin Bigdeli1؛ Hamed Jabbari2؛ Mahdi Shojaei3 | ||
| 1Control Eng. Dept., Faculty of Technical and Engineering, Imam Khomeini International University | ||
| 2Control Engineering Department, Faculty of Technical and Engineering, Imam Khomeini International University, Qazvin, Iran. | ||
| 3Control Engineering Department, Faculty of Technical and Engineering, Imam Khomeini International University, Qazvin, Iran. | ||
| چکیده [English] | ||
| Identifying and examining the types of cracks in concrete structures is one of the challenging engineering issues. Detection of crack bifurcation is very important because it detects high-intensity surfaces in concrete structures. In this paper, a new architecture based on convolutional neural networks is presented for crack classification in concrete structures. The proposed architecture detected and classified crack bifurcation in less time and with higher accuracy than other conventional and authentic deep learning architectures. In this paper, the cracks in 12000 images of concrete structures were investigated by the proposed algorithm, which resulted in 99.3% accuracy in categorizing as non-cracked images, images with simple cracks, and bifurcated crack images. Moreover, the analysis of the confusion matrix showed an accuracy of 99.3% and a recall of 99.5%, which confirmed the proper performance of the proposed algorithm. The sensitivity analysis of the proposed algorithm also showed the need for proportionality between the number of data, the number of neurons in the fully connected layer, execution time, and the expected percentage of accuracy according to the application of the problem. | ||
| کلیدواژه‌ها [English] | ||
| Cracks, Concrete structures, Machine learning, Deep learning, Convolutional neural networks | ||
| مراجع | ||
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