یک روش هوشمند برای طبقه‏بندی ترک در سازه‏های بتنی بر اساس شبکه‏های عصبی عمیق

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی بزق-کنترل، دانشکده فنی و مهندسی، دانشگاه بین المللی امام خمینی (ره)

2 گروه مهندسی برق-کنترل، دانشکده فنی و مهندسی، دانشگاه بین‏‏المللی امام خمینی(ره)، قزوین، ایران،

3 گروه 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

موضوعات

عنوان مقاله [English]

An Intelligent Method for Crack Classification in Concrete Structures Based on Deep Neural Networks

نویسندگان [English]

  • Nooshin Bigdeli 1
  • Hamed Jabbari 2
  • Mahdi Shojaei 3
1 Control Eng. Dept., Faculty of Technical and Engineering, Imam Khomeini International University
2 Control Engineering Department, Faculty of Technical and Engineering, Imam Khomeini International University, Qazvin, Iran.
3 Control 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

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نشریه مهندسی عمران امیرکبیر
دوره 53، شماره 8
آبان 1400
صفحه 3201-3220

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