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

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.

A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings’ Energy Efficiency for Smart City Planning

“…It is even smarter than the human in some cases, with substantial computing power. In real-life, ANN was studied and applied to solve many problems, such as prediction of self-compacting concrete strength [55], anisotropic masonry failure criterion [56], prediction of the mechanical properties of sandcrete materials [57], blasting issues [58][59][60][61][62][63][64], landslide assessment [65][66][67], to name a few [68][69][70][71][72][73][74][75]. They operate based on data analysis from input neurons, where the input data of the dataset is contained.…”

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

“…However, the query time may be very long, since the algorithm must investigate every data point in S to find the nearest k neighbors in the worst case, and may have large memory costs since the prediction must inquire of S . Additional regression details such as equations for KNN can be found in Kim et al [ 19 ].…”

“…The value other than the mean of ’s can also be considered, whatever is appropriate. Additional regression details such as equations for RT can be found in Kim et al [ 19 ].…”

“…( Figure 4 .) Additional regression details such as equations for ANN can be found in Kim et al [ 19 ].…”

“…The ANN and neuro-fuzzy inference system models well predicted the test results and obtained improved performance as compared to the existing empirical models. ANN could well describe the behavior of the magnetorheological elastomer base isolator [ 19 ]. Also, many ANN models have been considered and proposed to predict the compressive strength of concrete [ 20 , 21 ].…”