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Dynamic Analysis of Smart and Nanomaterials for Applications in Structural Control and Health Monitoring

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Smart Materials".

Deadline for manuscript submissions: closed (12 March 2023) | Viewed by 6332

Special Issue Editors

Dr. Wael A. Altabey

E-Mail Website
Guest Editor
International Institute of Urban Systems Engineering (IIUSE), Southeast University, Nanjing 210096, China
Interests: smart and nanomaterials; composite structures; structure health monitoring (SHM); artificial intelligence (AI); non-destructive evaluation (NDE); damage identification; vibration-based damage detection; fiber optical sensing technique; structural control; hysteretic systems; MEMS
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mohammad Noori

E-Mail Website1 Website2
Guest Editor
1. Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93405, USA
2. School of Civil Engineering, University of Leeds, Leeds LS2 9JT, UK
Interests: AI-based methods for structural health monitoring and dynamic response; random vibrations; hysteretic systems; seismic isolation; reliability and resilience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During the last few years, remarkable progress has been made in the development of new materials. Advanced structured materials including smart and nanomaterials open up new engineering possibilities because their specific properties (chemical, mechanical, and physical) that are not found in nature that can be significantly changed by the user in a controlled manner make them appropriate for certain applications. Due to their unique properties, smart and nanomaterials have been of interest for uncountable areas of technical application, in various systems and structures including intelligent and adaptive sensing or actuation as well as an active control. An understanding of the relationship between the structures and the properties has crucial importance in the practical utilization of these materials.

Over the past several years, a series of approaches for the progress of structural control and health monitoring has left a paramount impact on our everyday lives. It has shaped the framework of many engineering fields. Given the current state of quantitative and principled methodologies, it is nowadays possible to rapidly and consistently evaluate the structural safety of mechanical systems, industrial machines, modern concrete buildings, etc., to test their capability to serve their intended purpose. However, unsolved problems as well as new challenges exist. Unmolded nonlinearities, ineffective sensor placement, and the effects of confounding influences due to operational and environmental variability still harm the effectiveness of the state of structural control and health monitoring systems.

Therefore, the aim of this special issue is to gain new, unique knowledge about the relationships between structures and physico-mechanical and chemical properties of new materials, including finding ways to structure control and developing new methods for structural health monitoring. Another goal is the gather the main contributions of academics and practitioners in mechanical, aerospace, and civil engineering to provide a common ground for improvements in approaches to structural control and health monitoring by using this unique property of smart and nanomaterials. Studies concerning sensor technologies, vibration-based techniques, artificial intelligence based methods, and related fields are all welcome, both numerical and experimental.

Potential topics include, but are not limited to, the following areas and utilization of the application of smart and nanomaterials for structural control and health monitoring:

  • Structural health monitoring techniques; 
  • Advances in structural control; 
  • Smart materials and structures;
  • Advanced nanomaterials applications;  
  • Nanocomposite applications;
  • Nano pipes and films; 
  • Fault diagnosis and control design applications; 
  • Artificial intelligence application; 
  • Damage detection and localization; 
  • Self-repair and self-assembly application; 
  • Reconstruction techniques; 
  • Vehicles and automotive control; 
  • Intelligent robotics and nanorobots; 
  • Energy harvesting; 
  • Advanced sensing techniques and new sensor design; 
  • Nanostructured and nanocomposite materials for sensing applications; 
  • Nanomaterials for sensors; 
  • Application of artificial intelligence, machine learning and deep learning in smart and nanomaterials applications. 
  • Smart and nanomaterials for aerospace applications; 
  • Smart and nanomaterials for industrial applications.

Dr. Wael Altabey
Prof. Dr. Mohammad Noori
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • structural health monitoring
  • structural control
  • smart materials and structures
  • nanomaterials and nanocomposites
  • sensors and actuators
  • energy harvesting
  • artificial intelligence
  • damage detection
  • system identification
  • machine learning
  • sensor placement
  • intelligent structure systems

Published Papers (5 papers)

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Editorial

Jump to: Research

4 pages, 570 KiB  
Editorial
A Dynamic Analysis of Smart and Nanomaterials for New Approaches to Structural Control and Health Monitoring
by Wael A. Altabey and Mohammad Noori
Materials 2023, 16(9), 3567; https://doi.org/10.3390/ma16093567 - 06 May 2023
Cited by 5 | Viewed by 879
Abstract
During recent years, remarkable progress has been made in the development of new materials [...] Full article
(This article belongs to the Special Issue Dynamic Analysis of Smart and Nanomaterials for Applications in Structural Control and Health Monitoring)
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Research

Jump to: Editorial

19 pages, 9084 KiB  
Article
Low Temperature Chemoresistive Oxygen Sensors Based on Titanium-Containing Ti2CTx and Ti3C2Tx MXenes
by Elizaveta P. Simonenko, Ilya A. Nagornov, Artem S. Mokrushin, Sergey V. Kashevsky, Yulia M. Gorban, Tatiana L. Simonenko, Nikolay P. Simonenko and Nikolay T. Kuznetsov
Materials 2023, 16(13), 4506; https://doi.org/10.3390/ma16134506 - 21 Jun 2023
Cited by 1 | Viewed by 882
Abstract
The chemoresistive properties of multilayer titanium-containing Ti2CTx and Ti3C2Tx MXenes, synthesized by etching the corresponding MAX phases with NaF solution in hydrochloric acid, and the composites based on them, obtained by partial oxidation directly in [...] Read more.
The chemoresistive properties of multilayer titanium-containing Ti2CTx and Ti3C2Tx MXenes, synthesized by etching the corresponding MAX phases with NaF solution in hydrochloric acid, and the composites based on them, obtained by partial oxidation directly in a sensor cell in an air flow at 150 °C, were studied. Significant differences were observed for the initial MXenes, both in microstructure and in the composition of surface functional groups, as well as in gas sensitivity. For single Ti2CTx and Ti3C2Tx MXenes, significant responses to oxygen and ammonia were observed. For their partial oxidation at a moderate temperature of 150 °C, a high humidity sensitivity (T, RH = 55%) is observed for Ti2CTx and a high and selective response to oxygen for Ti3C2Tx at 125 °C (RH = 0%). Overall, these titanium-containing MXenes and composites based on them are considered promising as receptor materials for low temperature oxygen sensors. Full article
(This article belongs to the Special Issue Dynamic Analysis of Smart and Nanomaterials for Applications in Structural Control and Health Monitoring)
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18 pages, 5714 KiB  
Article
Microplotter Printing of a Miniature Flexible Supercapacitor Electrode Based on Hierarchically Organized NiCo2O4 Nanostructures
by Tatiana L. Simonenko, Nikolay P. Simonenko, Philipp Yu. Gorobtsov, Elizaveta P. Simonenko and Nikolay T. Kuznetsov
Materials 2023, 16(12), 4202; https://doi.org/10.3390/ma16124202 - 06 Jun 2023
Cited by 1 | Viewed by 1016
Abstract
The hydrothermal synthesis of a nanosized NiCo2O4 oxide with several levels of hierarchical self-organization was studied. Using X-ray diffraction analysis (XRD) and Fourier-transform infrared (FTIR) spectroscopy, it was determined that under the selected synthesis conditions, a nickel-cobalt carbonate hydroxide hydrate [...] Read more.
The hydrothermal synthesis of a nanosized NiCo2O4 oxide with several levels of hierarchical self-organization was studied. Using X-ray diffraction analysis (XRD) and Fourier-transform infrared (FTIR) spectroscopy, it was determined that under the selected synthesis conditions, a nickel-cobalt carbonate hydroxide hydrate of the composition M(CO3)0.5(OH)·0.11H2O (where M–Ni2+ and Co2+) is formed as a semi-product. The conditions of semi-product transformation into the target oxide were determined by simultaneous thermal analysis. It was found by means of scanning electron microscopy (SEM) that the main powder fraction consists of hierarchically organized microspheres of 3–10 μm in diameter, and individual nanorods are observed as the second fraction of the powder. Nanorod microstructure was further studied by transmission electron microscopy (TEM). A hierarchically organized NiCo2O4 film was printed on the surface of a flexible carbon paper (CP) using an optimized microplotter printing technique and functional inks based on the obtained oxide powder. It was shown by XRD, TEM, and atomic force microscopy (AFM) that the crystalline structure and microstructural features of the oxide particles are preserved when deposited on the surface of the flexible substrate. It was found that the obtained electrode sample is characterized by a specific capacitance value of 420 F/g at a current density of 1 A/g, and the capacitance loss during 2000 charge–discharge cycles at 10 A/g is 10%, which indicates a high material stability. It was established that the proposed synthesis and printing technology enables the efficient automated formation of corresponding miniature electrode nanostructures as promising components for flexible planar supercapacitors. Full article
(This article belongs to the Special Issue Dynamic Analysis of Smart and Nanomaterials for Applications in Structural Control and Health Monitoring)
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19 pages, 2773 KiB  
Article
Genetic Algorithm Optimization of Rainfall Impact Force Piezoelectric Sensing Device, Analytical and Finite Element Investigation
by Muath A. Bani-Hani, Dima A. Husein Malkawi, Khaldoon A. Bani-Hani and Sallam A. Kouritem
Materials 2023, 16(3), 911; https://doi.org/10.3390/ma16030911 - 18 Jan 2023
Cited by 2 | Viewed by 1438
Abstract
In this paper, rainfall droplet impact force is transformed into a measurable voltage signal output via the piezoelectric material direct effect utilized for sensing purposes. The motivating sensor is utilized to measure the peak impact forces of rainfall droplets for further analysis and [...] Read more.
In this paper, rainfall droplet impact force is transformed into a measurable voltage signal output via the piezoelectric material direct effect utilized for sensing purposes. The motivating sensor is utilized to measure the peak impact forces of rainfall droplets for further analysis and processing. Constructing a sense for the impact force of rainfall droplets has great implications in many real-life applications that can provide vital information regarding the amplifications of the impact force of rainfall on soil erosion, and the impact on small creatures and plants, etc. The rainfall droplet is set to collide on a very thin aluminum plate with negligible mass that can be presented geometrically as an extended segment of the proposed sensing device. The proposed sensing device is composed of a bimorph simply supported composite-piezoelectric beam that buckles due to the effect of the rain droplets’ vertical impact force. The proposed device is designed for optimal performance in terms of the amount of voltage that can be measured. This is accomplished by having the first critical buckling load of the device as less than the impact force of the rainfall droplet. Accordingly, the well-known genetic algorithm (GA) automated optimization technique is utilized in this paper to enhance the measured voltage signal. A proof mass is added to the middle of the beam to amplify the magnitude of the measured voltage signal. The voltage signal is intended to be transferred to the PC via a data acquisition system. The rainfall droplets’ peak impact forces are obtained analytically due to the nonlinear behavior of the beam using the Euler–Bernoulli thin beams assumptions. The FE model using COMSOL 6.0 Multiphysics commercial software is used to verify the analytical results. Full article
(This article belongs to the Special Issue Dynamic Analysis of Smart and Nanomaterials for Applications in Structural Control and Health Monitoring)
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24 pages, 8849 KiB  
Article
Studying Acoustic Behavior of BFRP Laminated Composite in Dual-Chamber Muffler Application Using Deep Learning Algorithm
by Wael A. Altabey, Mohammad Noori, Zhishen Wu, Mohamed A. Al-Moghazy and Sallam A. Kouritem
Materials 2022, 15(22), 8071; https://doi.org/10.3390/ma15228071 - 15 Nov 2022
Cited by 12 | Viewed by 1334
Abstract
Over the last two decades, several experimental and numerical studies have been performed in order to investigate the acoustic behavior of different muffler materials. However, there is a problem in which it is necessary to perform large, important, time-consuming calculations particularly if the [...] Read more.
Over the last two decades, several experimental and numerical studies have been performed in order to investigate the acoustic behavior of different muffler materials. However, there is a problem in which it is necessary to perform large, important, time-consuming calculations particularly if the muffler was made from advanced materials such as composite materials. Therefore, this work focused on developing the concept of the indirect dual-chamber muffler made from a basalt fiber reinforced polymer (BFRP) laminated composite, which is a monitoring system that uses a deep learning algorithm to predict the acoustic behavior of the muffler material in order to save effort and time on muffler design optimization. Two types of deep neural networks (DNNs) architectures are developed in Python. The first DNN is called a recurrent neural network with long short-term memory blocks (RNN-LSTM), where the other is called a convolutional neural network (CNN). First, a dual-chamber laminated composite muffler (DCLCM) model is developed in MATLAB to provide the acoustic behavior datasets of mufflers such as acoustic transmission loss (TL) and the power transmission coefficient (PTC). The model training parameters are optimized by using Bayesian genetic algorithms (BGA) optimization. The acoustic results from the proposed method are compared with available experimental results in literature, thus validating the accuracy and reliability of the proposed technique. The results indicate that the present approach is efficient and significantly reduced the time and effort to select the muffler material and optimal design, where both models CNN and RNN-LSTM achieved accuracy above 90% on the test and validation dataset. This work will reinforce the mufflers’ industrials, and its design may one day be equipped with deep learning based algorithms. Full article
(This article belongs to the Special Issue Dynamic Analysis of Smart and Nanomaterials for Applications in Structural Control and Health Monitoring)
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