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

Puyana‐Romero, Virginia; Chuquín, Jorge Santiago Arroyo; Chicaiza, Saúl Israel Méndez; Ciaburro, Giuseppe

doi:10.3390/fib11020018

Cited by 3 publications

(2 citation statements)

References 55 publications

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“…The framework created cloaking metamaterial with minimized TSCS within minutes, which could be considered “express delivery” in contrast to traditional methods. Several studies have also demonstrated the feasibility of DNN for acoustic cloak design due to the fast iteration ability ( Figure 3 E) 106 , 107 under different conditions such as water and air 108 as well as for the development of a biphysical cloak with triple-wave cloaking capabilities. 109 …”

Section: Emerging Applications Of Ai-based Design In Acoustic Metamat...mentioning

confidence: 99%

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AI-Based Metamaterial Design

Tezsezen,

Yigci,

Ahmadpour

et al. 2024

ACS Appl. Mater. Interfaces

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The use of metamaterials in various devices has revolutionized applications in optics, healthcare, acoustics, and power systems. Advancements in these fields demand novel or superior metamaterials that can demonstrate targeted control of electromagnetic, mechanical, and thermal properties of matter. Traditional design systems and methods often require manual manipulations which is time-consuming and resource intensive. The integration of artificial intelligence (AI) in optimizing metamaterial design can be employed to explore variant disciplines and address bottlenecks in design. AI-based metamaterial design can also enable the development of novel metamaterials by optimizing design parameters that cannot be achieved using traditional methods. The application of AI can be leveraged to accelerate the analysis of vast data sets as well as to better utilize limited data sets via generative models. This review covers the transformative impact of AI and AI-based metamaterial design for optics, acoustics, healthcare, and power systems. The current challenges, emerging fields, future directions, and bottlenecks within each domain are discussed.

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Section: Emerging Applications Of Ai-based Design In Acoustic Metamat...mentioning

confidence: 99%

“…(D) Sugar cane bagasse-based acoustic cloak composite using artificial NN model. Adapted from ref ( 107 ) in accordance with its CC BY 4.0 license. (E) Omnidirectional acoustic cloaking against airborne sound is realized by a locally resonant sonic material.…”

Section: Emerging Applications Of Ai-based Design In Acoustic Metamat...mentioning

confidence: 99%

AI-Based Metamaterial Design

Tezsezen,

Yigci,

Ahmadpour

et al. 2024

ACS Appl. Mater. Interfaces

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Simulation of Acoustic Properties of Plaster Matrix Composite MATERIAL Reinforced with Corn Stem Fibers

Puyana‐Romero

Cevallos

Ciaburro

2023

Fibers

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Environmental sustainability and environmental protection are key to shaping the built environment. The use of environmentally sustainable materials in architecture is essential to transform urban centers into modern, sustainable cities, reducing the pollution of air and natural ecosystems, lowering gas emissions, and improving the energy efficiency of structures. In this study, corn processing waste was used as a reinforcing material to create a plaster matrix composite material for use as a sound absorption material. Specimens of two thicknesses were created, and the sound absorption coefficient (SAC) was measured by applying the normal incidence technique. Subsequently, a simulation model for predicting SAC using Artificial Neural Network (ANN) algorithms was utilized to compare the absorption performance of the specimens. The fibers extracted from the corn stem significantly improved the sound absorption performance of the gypsum matrix specimens. This is due to the increase in the porosity of the material caused by the adhesion between the fiber and the plaster which creates air pockets due to the roughness of the fiber. The simulation model appears to be effective in predicting the absorption properties of the material, as indicated by the results.

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Enhanced Environmental Sustainability for the Acoustic Absorption Properties of Cabuya Fiber in Building Construction Using Machine Learning Predictive Model

Bravo-Moncayo,

Puyana-Romero,

Argotti-Gómez

et al. 2024

Sustainability

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Sustainability in construction is a growing concern due to the significant polluting waste generated before, during, and after a building’s life cycle. The use of natural materials can significantly reduce the environmental footprint in obtaining, manufacturing, transportation, execution, use, maintenance, and demolition of the building, especially when locally sourced. Natural fibers, in particular, can be used in room acoustics, offering good acoustic absorption while meeting sustainability goals. The objectives of this paper are to evaluate cabuya fiber, grown in Ecuador, as an acoustic absorbing material and to introduce a novel approach using machine learning to simulate the material’s acoustic properties. Eight samples of cabuya fiber, bound with a solution of water and Ca(OH)2, were prepared with thicknesses between 12 and 30.6 mm. The sound absorption coefficients (SACs) were calculated using an impedance tube, following international standards. A Gaussian regression model was built for the predictions. The results showed that the 30.6 mm sample achieved maximum absorption coefficients of 0.91 at 2 kHz and 0.9 at 5 kHz. The model predictions are very accurate, with a mean square error of just 0.0002. These findings offer valuable insights into using cabuya fiber and advanced predictive models to enhance building acoustic performance and reduce environmental impact.

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

Cited by 3 publications

References 55 publications

AI-Based Metamaterial Design

AI-Based Metamaterial Design

Simulation of Acoustic Properties of Plaster Matrix Composite MATERIAL Reinforced with Corn Stem Fibers

Enhanced Environmental Sustainability for the Acoustic Absorption Properties of Cabuya Fiber in Building Construction Using Machine Learning Predictive Model

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