Prediction of the dynamic stiffness of resilient materials using artificial neural network (ANN) technique

Changhyuk Kim, Jung Yoon Lee, Moonhyun Kim

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

High-rise residential buildings are constructed in countries with high population density in response to the need to utilize small development areas. As many high-rise buildings are being constructed, issues of floor impact sound tend to occur in buildings. In general, resilient materials are implemented between the slab and the finishing mortar to control the floor impact sound. Various mechanical properties of resilient materials can affect the floor impact sound. To investigate the impact sound reduction capacity, various experimental tests were conducted. The test results show that the floor impact sound reduction capacity has a close relationship with the dynamic stiffness of resilient materials. A total of six different kinds of resilient materials were loaded under four loading conditions. The test results show that loading time, loading, and material properties influence the change in dynamic stiffness. Artificial neural network (ANN) technique was implemented to obtain the responses between the deflection and dynamic stiffness. Three different algorithms were considered in the ANN models and the trained results were analyzed based on the root mean square error. The feasibility of using the ANN technique was verified with a high and consistent level of accuracy.

Original languageEnglish
Article number1088
JournalApplied Sciences (Switzerland)
Volume9
Issue number6
DOIs
StatePublished - 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019 by the authors.

Keywords

  • Artificial neural network
  • Data regression
  • Dynamic stiffness
  • Long-term load
  • Resilient material

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