Prediction of the Dynamic Stiffness of Resilient Materials using Artificial Neural Network (ANN) Technique

Changhyuk Kim,Moonhyun Kim,Jung-Yoon Lee

doi:10.3390/app9061088

Changhyuk Kim, Moonhyun Kim + Show 1 more

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https://doi.org/10.3390/app9061088

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Journal: Applied Sciences	Publication Date: Mar 14, 2019
Citations: 5	License type: CC BY 4.0

Affiliation: Sungkyunkwan University

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.

Highlights

In Asian countries, where the population density is very high, high-rise residential buildings are common
This section compares the relation between the deflection and dynamic stiffness based on the test results to the equation proposed by Kim et al [10], and the artificial neural network modeling using three different data regression algorithms
Models was about 9.5% more than that of the proposed equation by Kim et al when the Artificial Neural Network (ANN) models were used, the Root Mean Squared Error (RMSE) decreased by 54.4% and the relative errors decreased by an average

Summary

Introduction

In Asian countries, where the population density is very high, high-rise residential buildings are common. The issue of floor impact sound tends to occur in most high-rise buildings. Many countries have set regulations for controlling floor impact sounds [1,2,3]. Various systems are needed to minimize the floor impact sounds and one of the most effective ways is to use a floating floor system. Many studies have shown that lightweight and heavyweight impact sounds can be reduced by using resilient materials. Findley [4] developed an empirical and analytical model to evaluate the influence of impact sound transmission on low frequencies. Experimental tests on the response between the floor impact sound reduction and dynamic stiffness showed that as the dynamic stiffness decreases, the lightweight impact sound reduction tends to increase [5]. Measuring the apparent dynamic stiffness is an important procedure for evaluating the impact sound reduction.

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