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Intelligent Classification Method for Tunnel Lining Cracks Based on PFC-BP Neural Network
المؤلفون المشاركون
Ding, Hao
Jiang, Xinghong
Li, Ke
Guo, Hongyan
Li, Wenfeng
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-18
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Tunnel lining crack is the most common disease and also the manifestation of other diseases, which widely exists in plain concrete lining structure.
Proper evaluation and classification of engineering conditions directly relate to operation safety.
Particle flow code (PFC) calculation software is applied in this study, and the simulation reliability is verified by using the laboratory axial compression test and 1 : 10 model experiment to calibrate the calculation parameters.
Parameter analysis is carried out focusing on the load parameters, structural parameters, dimension, and direction which affect the crack diseases.
Based on that, an evaluation index system represented by tunnel buried depth (H), crack position (P), crack length (L), crack width (W), crack depth (D), and crack direction (A) is put forward.
The training data of the back propagation (BP) neural network which takes load-bearing safety and crack stability as the evaluation criteria are obtained.
An expert system is introduced into the BP neural network for correction of prediction results, realizing classified dynamic optimization of complex engineering conditions.
The results of this study can be used to judge the safety state of cracked lining structure and provide guidance to the prevention and control of crack diseases, which is significant to ensure the safety of tunnel operation.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Ding, Hao& Jiang, Xinghong& Li, Ke& Guo, Hongyan& Li, Wenfeng. 2020. Intelligent Classification Method for Tunnel Lining Cracks Based on PFC-BP Neural Network. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1201675
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Ding, Hao…[et al.]. Intelligent Classification Method for Tunnel Lining Cracks Based on PFC-BP Neural Network. Mathematical Problems in Engineering No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1201675
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Ding, Hao& Jiang, Xinghong& Li, Ke& Guo, Hongyan& Li, Wenfeng. Intelligent Classification Method for Tunnel Lining Cracks Based on PFC-BP Neural Network. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1201675
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1201675
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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