Predicting Long-Term Deformation of Soundproofing Resilient Materials Subjected to Compressive Loading: Machine Learning Approach

Materials

Jongkwon Choi¹  Changhyuk Kim²  Seungbum Koo³ 

[1] Department of Civil and Environmental Engineering, Hongik University, Seoul 04066, Korea;Korea Institute of Civil Engineering and Building Technology, Ilsan 10223, Korea;The MathWorks Inc., Natick, MA 01760, USA;

关键词: resilient material;    long-term deformation;    floor impact sound;    machine learning;   

DOI  :  10.3390/ma13184133

来源: DOAJ

【 摘 要 】

Soundproofing materials are widely used within structural components of multi-dwelling residential buildings to alleviate neighborhood noise problems. One of the critical mechanical properties for the soundproofing materials to ensure its appropriate structural and soundproofing performance is the long-term compressive deformation under the service loading conditions. The test method in the current test specifications only evaluates resilient materials for a limited period (90-day). It then extrapolates the test results using a polynomial function to predict the long-term compressive deformation. However, the extrapolation is universally applied to materials without considering the level of loads; thus, the calculated deformation may not accurately represent the actual compressive deformation of the materials. In this regard, long-term compressive deformation tests were performed on the selected soundproofing resilient materials (i.e., polystyrene, polyethylene, and ethylene-vinyl acetate). Four levels of loads were chosen to apply compressive loads up to 350 to 500 days continuously, and the deformations of the test specimens were periodically monitored. Then, three machine learning algorithms were used to predict long-term compressive deformations. The predictions based on machine learning and ISO 20392 method are compared with experimental test results, and the accuracy of machine learning algorithms and ISO 20392 method are discussed.

【 授权许可】

Unknown