scholarly journals A Machine Learning Based Prediction Model for the Sound Absorption Coefficient of Micro-Expanded Metal Mesh (MEMM)

2020 ◽  
Vol 10 (21) ◽  
pp. 7612
Author(s):  
Yaw-Shyan TSAY ◽  
Chiu-Yu YEH
Keyword(s):  
Machine Learning ◽  
Absorption Coefficient ◽  
Prediction Model ◽  
Sound Absorption ◽  
Prediction Models ◽  
Sound Absorption Coefficient ◽  
Metal Mesh ◽  
Commercial Market ◽  
Conventional Models ◽  
The Cost

Recently, micro-perforated panels (MPP) have become a popular sound absorbing material in the field of architectural acoustics. However, the cost of MPP is still high for the commercial market in Taiwan, and MPP is still not very popular compared to other sound absorbing materials and devices. The objective of this study is to develop a prediction model for MEMM via a machine learning approach. An experiment including 14 types of MEMM was first carried out in a reverberation room based on ISO 354. To predict the sound absorption coefficient of the MEMM, the capability of three conventional models and three machine learning (ML) models of the supervised learning method were studied for the development of the prediction model. The results showed that in most conventional models, the sound absorption coefficient of using an equivalent perimeter had the best agreement compared with other parameters, and the root mean square error (RMSE) between prediction models and experimental data were around 0.2~0.3. However, the RMSE of all ML models was less than 0.1, and the RMSE of the gradient boost model was 0.033 in the training sets and 0.062 in the testing sets, which showed the best agreement with the experiment data.

Parametric Influence on the Sound Absorption Coefficient of Porous Asphalt

2009 ◽  
Author(s):  
Punnamee Sachakamol ◽  
Liming Dai
Keyword(s):  
Absorption Coefficient ◽  
Sound Absorption ◽  
Prediction Models ◽  
Traffic Noise ◽  
Acoustic Properties ◽  
Sound Absorption Coefficient ◽  
Control Parameters ◽  
Porous Asphalt ◽  
Pavement Materials ◽  
Impedance Tube Method

This research intends to develop a method for predicting the sound absorption coefficient of various porous highway pavement materials. Since many of the existing prediction models for acoustic properties and traffic noise still have limitations and problems with accuracy, sound absorption coefficients are measured with the impedance tube method to verify numerical values obtained from the model. Results obtained from the experiment and numerical simulations are compared and presented to reveal the effect and influence of the control parameters.

scholarly journals Explainable machine learning determines effects on the sound absorption coefficient measured in the impedance tube

2021 ◽  
Vol 149 (3) ◽  
pp. 1932-1945
Author(s):  
Merten Stender ◽  
Christian Adams ◽  
Mathies Wedler ◽  
Antje Grebel ◽  
Nobert Hoffmann
Keyword(s):  
Machine Learning ◽  
Absorption Coefficient ◽  
Sound Absorption ◽  
Sound Absorption Coefficient ◽  
Impedance Tube

scholarly journals Sound Absorption Characteristics of Ternary Concrete using SCBA and Silica Fume as Partial Replacement of Cement in Concrete

2019 ◽  
Vol 8 (2) ◽  
pp. 2007-2011
Keyword(s):  
Absorption Coefficient ◽  
Silica Fume ◽  
Sound Absorption ◽  
Ambient Noise ◽  
Sound Absorption Coefficient ◽  
Partial Replacement ◽  
Split Tensile Strength ◽  
Strength Tests ◽  
Living Organisms ◽  
The Cost

The ambient noise from transportation and in metro politan areas due to tremendous inhabiting density, it promotes infelicitous noise threats, which originates severe health problems to humans and other living organisms. The present study focus on the employment of industrial and agricultural misuse can be adopted in concrete as partly replacement of cement to reduce the cost, also improves the concrete properties and reduce environmental pollution. The main objective is to assess sound absorption of TBASF concrete by partly replace with cement with SCBA (0%, 5%, 10%, 15%, 20%, and 25%) and additionally 10% Silica Fume. Compressive strength, Split tensile strength, Flexural strength tests were conducted for 7days, 28days, and 56days and sound absorption test was conducted for 7days and 28days. The optimum strength obtained for the mix TBASF15 at all the ages. Further, the results of the sound absorption coefficient (α) of SCBA Silica Fume concrete compared with the sound absorption coefficient (SAC) (α) of parent concrete is carried out by using the impedance tube.

scholarly journals Acoustic Panels Made of Paper Sludge and Clay Composites

2021 ◽  
Vol 13 (2) ◽  
pp. 637
Author(s):  
Tomas Astrauskas ◽  
Tomas Januševičius ◽  
Raimondas Grubliauskas
Keyword(s):  
Absorption Coefficient ◽  
Sound Absorption ◽  
Composite Panel ◽  
Sound Absorption Coefficient ◽  
Core Material ◽  
Paper Sludge ◽  
Frequency Range ◽  
Absorption Properties ◽  
Air Gaps ◽  
The Core

Studies on recycled materials emerged during recent years. This paper investigates samples’ sound absorption properties for panels fabricated of a mixture of paper sludge (PS) and clay mixture. PS was the core material. The sound absorption was measured. We also consider the influence of an air gap between panels and rigid backing. Different air gaps (50, 100, 150, 200 mm) simulate existing acoustic panel systems. Finally, the PS and clay composite panel sound absorption coefficients are compared to those for a typical commercial absorptive ceiling panel. The average sound absorption coefficient of PS-clay composite panels (αavg. in the frequency range from 250 to 1600 Hz) was up to 0.55. The resulting average sound absorption coefficient of panels made of recycled (but unfinished) materials is even somewhat higher than for the finished commercial (finished) acoustic panel (αavg. = 0.51).

scholarly journals Air permeability and sound absorption coefficient changes from ultrasonic treatment in a cross section of Malas (Homalium foetidum)

2021 ◽  
Vol 67 (1) ◽  
Author(s):  
Chun-Won Kang ◽  
Eun-Suk Jang ◽  
Nam-Ho Lee ◽  
Sang-Sik Jang ◽  
Min Lee
Keyword(s):  
Absorption Coefficient ◽  
Ultrasonic Treatment ◽  
Sound Absorption ◽  
Gas Permeability ◽  
Treated Wood ◽  
Untreated Wood ◽  
Sound Absorption Coefficient ◽  
Permeability Constant ◽  
Average Value ◽  
Permeability Increase

AbstractWe investigated the effect of ultrasonic treatment on Malas (Homalium foetidum) gas permeability and sound absorption coefficient using the transfer function method. Results showed a longitudinal average Darcy permeability constant of 2.02 (standard deviation SD 0.72) for untreated wood and 6.15 (SD 3.07) for ultrasound-treated wood, a permeability increase of 3.04 times. We also determined the average sound absorption coefficients in the range of 50 to 6.4 kHz and NRC (noise reduction coefficient: average value of sound absorption coefficient value at 250, 500, 1000, and 2000 Hz) of untreated Malas. Those values were 0.23 (SD 0.02) and 0.13 (SD 0.01), respectively, while those of ultrasonic-treated Malas were 0.28 (SD 0.02) and 0.14 (SD 0.02), a 19.74% increase in average sound absorption coefficient.

Research of Possibilities of Using the Recycled Materials Based on Rubber and Textiles Combined with Vermiculite Material in the Area of ​​Noise Reduction

2014 ◽  
Vol 1001 ◽  
pp. 171-176 ◽  
Author(s):  
Pavol Liptai ◽  
Marek Moravec ◽  
Miroslav Badida
Keyword(s):  
Absorption Coefficient ◽  
Standing Wave ◽  
Sound Pressure Level ◽  
Sound Absorption ◽  
Sound Pressure ◽  
Sound Level ◽  
Pressure Level ◽  
Sound Absorption Coefficient ◽  
Physical Parameters ◽  
Materials Research

This paper describes possibilities in the use of recycled rubber granules and textile materials combined with vermiculite panel. The aim of the research is the application of materials that will be absorbing or reflecting sound energy. This objective is based on fundamental physical principles of materials research and acoustics. Method of measurement of sound absorption coefficient is based on the principle of standing wave in the impedance tube. With a sound level meter is measured maximum and minimum sound pressure level of standing wave. From the maximum and minimum sound pressure level of standing wave is calculated sound absorption coefficient αn, which can take values from 0 to 1. Determination of the sound absorption coefficient has been set in 1/3 octave band and in the frequency range from 50 Hz to 2000 Hz. In conclusion are proposed possibilities of application of these materials in terms of their mechanical and physical parameters.

scholarly journals Study on the sound absorption behavior of multi-component polyester nonwovens: experimental and numerical methods

2018 ◽  
Vol 89 (16) ◽  
pp. 3342-3361 ◽  
Author(s):  
Tao Yang ◽  
Ferina Saati ◽  
Kirill V Horoshenkov ◽  
Xiaoman Xiong ◽  
Kai Yang ◽  
...  
Keyword(s):  
Numerical Methods ◽  
Absorption Coefficient ◽  
Empirical Model ◽  
Surface Impedance ◽  
Sound Absorption ◽  
Low Frequency ◽  
Accurate Method ◽  
Small Error ◽  
Sound Absorption Coefficient ◽  
Acoustical Properties

This study presents an investigation of the acoustical properties of multi-component polyester nonwovens with experimental and numerical methods. Fifteen types of nonwoven samples made with staple, hollow and bi-component polyester fibers were chosen to carry out this study. The AFD300 AcoustiFlow device was employed to measure airflow resistivity. Several models were grouped in theoretical and empirical model categories and used to predict the airflow resistivity. A simple empirical model based on fiber diameter and fabric bulk density was obtained through the power-fitting method. The difference between measured and predicted airflow resistivity was analyzed. The surface impedance and sound absorption coefficient were determined by using a 45 mm Materiacustica impedance tube. Some widely used impedance models were used to predict the acoustical properties. A comparison between measured and predicted values was carried out to determine the most accurate model for multi-component polyester nonwovens. The results show that one of the Tarnow model provides the closest prediction to the measured value, with an error of 12%. The proposed power-fitted empirical model exhibits a very small error of 6.8%. It is shown that the Delany–Bazley and Miki models can accurately predict surface impedance of multi-component polyester nonwovens, but the Komatsu model is less accurate, especially at the low-frequency range. The results indicate that the Miki model is the most accurate method to predict the sound absorption coefficient, with a mean error of 8.39%.

scholarly journals Development of Heavy Rain Damage Prediction Model Using Machine Learning Based on Big Data

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Changhyun Choi ◽  
Jeonghwan Kim ◽  
Jongsung Kim ◽  
Donghyun Kim ◽  
Younghye Bae ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Prediction Model ◽  
Prediction Models ◽  
Meteorological Data ◽  
Heavy Rain ◽  
Machine Learning Techniques ◽  
Damage Prediction ◽  
Explanatory Variables ◽  
The Republic

Prediction models of heavy rain damage using machine learning based on big data were developed for the Seoul Capital Area in the Republic of Korea. We used data on the occurrence of heavy rain damage from 1994 to 2015 as dependent variables and weather big data as explanatory variables. The model was developed by applying machine learning techniques such as decision trees, bagging, random forests, and boosting. As a result of evaluating the prediction performance of each model, the AUC value of the boosting model using meteorological data from the past 1 to 4 days was the highest at 95.87% and was selected as the final model. By using the prediction model developed in this study to predict the occurrence of heavy rain damage for each administrative region, we can greatly reduce the damage through proactive disaster management.

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