A Deep Learning Model for Concrete Dam Deformation Prediction Based on RS-LSTM
المؤلفون المشاركون
Qu, Xudong
Yang, Jie
Chang, Meng
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-10-31
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
Deformation is a comprehensive reflection of the structural state of a concrete dam, and research on prediction models for concrete dam deformation provides the basis for safety monitoring and early warning strategies.
This paper focuses on practical problems such as multicollinearity among factors; the subjectivity of factor selection; robustness, externality, generalization, and integrity deficiencies; and the unsoundness of evaluation systems for prediction models.
Based on rough set (RS) theory and a long short-term memory (LSTM) network, single-point and multipoint concrete dam deformation prediction models for health monitoring based on RS-LSTM are studied.
Moreover, a new prediction model evaluation system is proposed, and the model accuracy, robustness, externality, and generalization are defined as quantitative evaluation indexes.
An engineering project shows that the concrete dam deformation prediction models based on RS-LSTM can quantitatively obtain the representative factors that affect dam deformation and the importance of each factor relative to the effect.
The accuracy evaluation index (AVI), robustness evaluation index (RVI), externality evaluation index (EVI), and generalization evaluation index (GVI) of the model are superior to the evaluation indexes of existing shallow neural network models and statistical models according to the new evaluation system, which can estimate the comprehensive performance of prediction models.
The prediction model for concrete dam deformation based on RS-LSTM optimizes the factors that influence the model, quantitatively determines the importance of each factor, and provides high-performance, synchronous, and dynamic predictions for concrete dam behaviours; therefore, the model has strong engineering practicality.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Qu, Xudong& Yang, Jie& Chang, Meng. 2019. A Deep Learning Model for Concrete Dam Deformation Prediction Based on RS-LSTM. Journal of Sensors،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1187662
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Qu, Xudong…[et al.]. A Deep Learning Model for Concrete Dam Deformation Prediction Based on RS-LSTM. Journal of Sensors No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1187662
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Qu, Xudong& Yang, Jie& Chang, Meng. A Deep Learning Model for Concrete Dam Deformation Prediction Based on RS-LSTM. Journal of Sensors. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1187662
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1187662
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