Safety Monitoring Model of a Super-High Concrete Dam by Using RBF Neural Network Coupled with Kernel Principal Component Analysis
المؤلفون المشاركون
Gu, Chongshi
Song, Jintao
Zhao, Erfeng
Chen, Siyu
Lin, Chaoning
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-08-30
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Effective deformation monitoring is vital for the structural safety of super-high concrete dams.
The radial displacement of the dam body is an important index of dam deformation, which is mainly influenced by reservoir water level, temperature effect, and time effect.
In general, the safety monitoring models of dams are built on the basis of statistical models.
The temperature effect of dam safety monitoring models is interpreted using approximate functions or the temperature values of a few points of measurement.
However, this technique confers difficulty in representing the nonlinear features of the temperature effect on super-high concrete dams.
In this study, a safety monitoring model of super-high concrete dams is established through the radial basis neural network (RBF-NN) and kernel principal component analysis (KPCA).
The RBF-NN with strong nonlinear fitting capacity is utilized as the framework of the model, and KPCA with different kernels is adopted to extract the temperature variables of the dam temperature dataset.
The model is applied to a super-high arch dam in China, and results show that the Hybrid-KPCA -RBF-NN model has high fitting and prediction precision and thus has practical application value.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Chen, Siyu& Gu, Chongshi& Lin, Chaoning& Zhao, Erfeng& Song, Jintao. 2018. Safety Monitoring Model of a Super-High Concrete Dam by Using RBF Neural Network Coupled with Kernel Principal Component Analysis. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1205770
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Chen, Siyu…[et al.]. Safety Monitoring Model of a Super-High Concrete Dam by Using RBF Neural Network Coupled with Kernel Principal Component Analysis. Mathematical Problems in Engineering No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1205770
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Chen, Siyu& Gu, Chongshi& Lin, Chaoning& Zhao, Erfeng& Song, Jintao. Safety Monitoring Model of a Super-High Concrete Dam by Using RBF Neural Network Coupled with Kernel Principal Component Analysis. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1205770
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1205770
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر