Abstract
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.
Highlights
Multi-dwelling residential buildings are currently being constructed in areas with high population densities since these buildings can offer many houses within a limited urban space
This paper presents a technique to predict the long-term compressive deformation of resilient materials using novel machine learning algorithms: K-nearest neighbors (KNN), regression tree (RT), and artificial neural networks (ANN)
Evaluated by comparing them with the experimental observations and predictions based on ISO 20392
Summary
Multi-dwelling residential buildings are currently being constructed in areas with high population densities since these buildings can offer many houses within a limited urban space. To maximize the effectiveness of such resilient materials, many standards, including ISO, have been developed to characterize the material properties of resilient materials such as density, dynamic stiffness, loss coefficient, residual strain, and heat conductivity [7] Many of these current standards are only based on short-term loading experimental results [6,8,9]. The nonhomogeneity of the materials and the slab deflection caused by the service loading conditions could lead to cracking of the entire floor system, leading to a reduction in the sound insulation performance [13] Such a shortage makes the research on the long-term deflection of sound resilient structural material necessary. This paper presents a technique to predict the long-term compressive deformation of resilient materials using novel machine learning algorithms: K-nearest neighbors (KNN), regression tree (RT), and artificial neural networks (ANN). The two estimations are compared with the measured data points, and the long-term deformation prediction ability will be evaluated
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