# Characterization and Simulation of Acoustic Properties of Sugarcane Bagasse-Based Composite Using Artificial Neural Network Model

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Preparation of the Samples

- To begin, the bagasse obtained by separating the fibers from the bark was sieved with a metal sieve, obtaining the smallest possible filaments.
- The sieved bagasse filaments were then placed in a 15 × 25 cm size frame, making sure they completely covered the thickness of the frame, and then the amount of bagasse used in this process was weighed (Figure 3a).
- To produce panels with SCB matrix and plaster or clay-based binders, the frame was divided into 3 parts and 1/3 of the frame is filled with the binder and then weighed. In this way, the proportions of 3/1 which had been indicated were respected. Table 1 shows the quantities of each component in the different types of assembled panels.
- Once it had been demonstrated that the proportions of the elements were optimal for the construction of panels, the weights of the components necessary to create cylindrical-shaped samples of thicknesses equal to 6, 12 and 25 mm were calculated, to be used for the measurement of the coefficients of sound absorption using the impedance tube (Kundt tube). To obtain these samples, molds were made of the diameter allowed by the Kundt tube, which is about 35 mm. The samples were weighed on a digital scale and the weights obtained are shown in Table 2.
- Finally, 18 samples were obtained, divided into the two binders combined with the SCB: 9 samples of SCB - plaster, and 9 samples of SCB - clay. Several similar samples were then made for each of the three foreseen thicknesses of 6, 12 and 25 mm, as shown in Figure 4.

#### 2.2. Sound Absorption Coefficient Measurement

#### 2.3. Artificial Neural Network (ANN) Based Modelling

_{1}, x

_{2}, …, x

_{n}). Furthermore, for each input node, we will have an array of weights indicated with (w

_{1}, w

_{2}, …, w

_{n}). To get the actual data we will perform a simple x

_{n}* w

_{n}operation for each input x

_{n}. The output will then be calculated using the following equation:

- x
_{i}= input - w
_{n}= weight - b = bias
- y = output

- y = output expected
- y
^{*}= output predicted

- $\mu $ = learning rate

## 3. Results and Discussion

#### 3.1. Sound Absorption Coefficient Measurements

#### 3.2. ANN-Based Model for SAC Prediction

## 4. Conclusions

- simulated data curve adapted effectively to the measured data, also showing a capacity to correct at the low frequencies those data which had highlighted anomalies,
- a deviation between the measured and predicted data was found for the clay binders for the thicker samples 25 mm,
- the predicted data underlies those measured for the entire frequency range.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 5.**Electroacoustic chain of the Kundt impedance tube located in the UDLA acoustic measurement laboratory.

**Figure 8.**Results of measurements in one-third octave bands. Each curve corresponds to a sample thickness (6 mm, 12 mm, 25 mm). (

**a**) SCB-Clay; (

**b**) SCB-Plaster.

**Figure 12.**Measured versus Predicted SAC values: (

**a**) SCB-Clay 6 mm; (

**b**) SCB-Clay 12 mm; (

**c**) SCB–Clay 25 mm (

**d**) SCB–Plaster 6 mm; (

**e**) SCB–Plaster 12 mm; (

**f**) SCB – Plaster 25 mm.

**Table 1.**Quantity of bagasse, binder, and water for each panel, the percentage by weight is shown in brackets.

Panel Type | Thickness (mm) | SCB (g) | Binder (g) | Water (g) |
---|---|---|---|---|

SCB-plaster | 6 | 20 (6.06%) | 60 (18.2%) | 250 (75.8%) |

SCB-plaster | 12 | 30 (4.61%) | 120 (18.4%) | 500 (76.9%) |

SCB-plaster | 25 | 60 (5.61%) | 260 (24.3%) | 750 (70.1%) |

SCB-clay | 6 | 20 (4.76%) | 150 (35.7%) | 250 (59.5%) |

SCB-clay | 12 | 30 (4.16%) | 190 (26.4%) | 500 (69.4%) |

SCB-clay | 25 | 60 (4.58%) | 500 (38.2%) | 750 (57.3%) |

Title 1 | Thickness (mm) | Weight (g) |
---|---|---|

SCB-plaster | 6 | 8.37 |

SCB-plaster | 12 | 4.25 |

SCB-plaster | 25 | 2.69 |

SCB–clay | 6 | 9.10 |

SCB–clay | 12 | 3.91 |

SCB–clay | 25 | 2.83 |

SCB-Clay | SCB-Plaster | |||||
---|---|---|---|---|---|---|

Frequency (Hz) | 6 mm | 12 mm | 25 mm | 6 mm | 12 mm | 25 mm |

100 | 0.05537 | 0.05236 | 0.02008 | 0.03179 | 0.03484 | 0.02029 |

125 | 0.04705 | 0.04502 | 0.09607 | 0.03929 | 0.04604 | 0.04886 |

160 | 0.07248 | 0.09294 | 0.06864 | 0.06639 | 0.09405 | 0.08359 |

200 | 0.09794 | 0.09028 | 0.09249 | 0.09576 | 0.09920 | 0.09353 |

250 | 0.08369 | 0.09001 | 0.09513 | 0.08006 | 0.07764 | 0.08141 |

315 | 0.08338 | 0.09793 | 0.08338 | 0.08905 | 0.09781 | 0.07158 |

400 | 0.08749 | 0.07029 | 0.08558 | 0.05815 | 0.07825 | 0.08075 |

500 | 0.05474 | 0.06549 | 0.07011 | 0.06253 | 0.06091 | 0.04812 |

630 | 0.03421 | 0.03147 | 0.01452 | 0.03791 | 0.04048 | 0.06693 |

800 | 0.01712 | 0.01037 | 0.05724 | 0.02366 | 0.02495 | 0.07276 |

1000 | 0.01476 | 0.00566 | 0.05398 | 0.05010 | 0.00673 | 0.02378 |

1250 | 0.02401 | 0.03003 | 0.08326 | 0.06363 | 0.00376 | 0.07901 |

1600 | 0.04914 | 0.04745 | 0.08951 | 0.00962 | 0.00201 | 0.08443 |

2000 | 0.05788 | 0.03840 | 0.07811 | 0.06646 | 0.09806 | 0.08101 |

2500 | 0.06058 | 0.04491 | 0.00320 | 0.08833 | 0.09820 | 0.07516 |

3150 | 0.04779 | 0.05657 | 0.00721 | 0.08333 | 0.09048 | 0.06392 |

4000 | 0.05711 | 0.06059 | 0.07270 | 0.09979 | 0.09992 | 0.07745 |

Input Layer | Hidden Layer | Output Layer | Training Algorithm |
---|---|---|---|

4 nodes | 10 nodes | 1 node | Levenberg Marquardt |

Parameter | Initial Value | Stopped Value | Target Value |
---|---|---|---|

Epoch | 0 | 48 | 1000 |

Performance | 0.18 | 0.0127 | 0 |

Gradient | 0.391 | 0.00259 | 1.00 10^{−7} |

Observations | MSE | R | |
---|---|---|---|

Training | 1134 | 0.0111 | 0.8434 |

Validation | 243 | 0.0098 | 0.8647 |

Test | 243 | 0.0101 | 0.8841 |

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**MDPI and ACS Style**

Puyana-Romero, V.; Chuquín, J.S.A.; Chicaiza, S.I.M.; Ciaburro, G.
Characterization and Simulation of Acoustic Properties of Sugarcane Bagasse-Based Composite Using Artificial Neural Network Model. *Fibers* **2023**, *11*, 18.
https://doi.org/10.3390/fib11020018

**AMA Style**

Puyana-Romero V, Chuquín JSA, Chicaiza SIM, Ciaburro G.
Characterization and Simulation of Acoustic Properties of Sugarcane Bagasse-Based Composite Using Artificial Neural Network Model. *Fibers*. 2023; 11(2):18.
https://doi.org/10.3390/fib11020018

**Chicago/Turabian Style**

Puyana-Romero, Virginia, Jorge Santiago Arroyo Chuquín, Saúl Israel Méndez Chicaiza, and Giuseppe Ciaburro.
2023. "Characterization and Simulation of Acoustic Properties of Sugarcane Bagasse-Based Composite Using Artificial Neural Network Model" *Fibers* 11, no. 2: 18.
https://doi.org/10.3390/fib11020018