Prediction of the Dynamic Stiffness of Resilient Materials using Artificial Neural Network (ANN) Technique
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Structural Health Monitoring Techniques · Infrastructure Maintenance and Monitoring · Structural Engineering and Vibration Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University, Suwon 16419, Korea
College of Software, Sungkyunkwan University, Suwon 16419, Korea
Funding
National Research Foundation of Korea
References (33)
Show source-provided references
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