• Smart Structures and Systems
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• v.23 no.6
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• pp.579-587
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• 2019

In this paper, pounding tuned mass dampers (PTMDs) were designed to mitigate the multi-mode vortex-induced vibration (VIV) of stay cable utilizing the viscous-elastic material's energy-dissipated ability. The PTMD device consists of a cantilever metal rod beam, a metal mass block and a specially designed damping element covered with viscous-elastic material layer. Wind-tunnel experiment on VIV of stay cable model was set up to validate the effectiveness of the PTMD on multi-mode VIV mitigation of stay cable. By analyzing and comparing testing results of all testing cases, it could be verified that the PTMD with viscous-elastic pounding boundary can obviously mitigate the VIV amplitude of the stay cable. Moreover, the installed location and the design parameters of the PTMD device based on the controlled modes of the primary stay cable, would have a certain extent suppression on the other modal vibration of the stay cable, which means that the designed PTMDs are effective among a large band of frequency for the multi-mode VIV control of the stay cable.

• Advances in nano research
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• v.6 no.4
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• pp.299-321
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• 2018

A layerwise shear deformation theory is applied in this paper for buckling analysis of piezoelectric truncated conical shell. The core is a multiphase nanocomposite reinforced by carbon nanotubes (CNTs) and carbon fibers. The top and bottom face sheets are piezoelectric subjected to 3D electric field and external voltage. The Halpin-Tsai model is used for obtaining the effective moisture and temperature dependent material properties of the core. The proposed layerwise theory is based on Mindlin's first-order shear deformation theory in each layer and results for a laminated truncated conical shell with three layers considering the continuity boundary condition. Applying energy method, the coupled motion equations are derived and analyzed using differential quadrature method (DQM) for different boundary conditions. The influences of some parameters such as boundary conditions, CNTs weight percent, cone semi vertex angle, geometrical parameters, moisture and temperature changes and external voltage are investigated on the buckling load of the smart structure. The results show that enhancing the CNTs weight percent, the buckling load increases. Furthermore, increasing the moisture and temperature changes decreases the buckling load.

• Current Applied Physics
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• v.18 no.11
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• pp.1327-1337
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• 2018

This paper investigates the vibration behavior of pristine and defected triangular graphene sheets; which has recently attracted the attention of researchers and compare these two types in natural frequencies and sensitivity. Here, the molecular dynamics method has been employed to establish a virtual laboratory for this purpose. After measuring the different parameters obtained by the molecular dynamics approach, these data have been analyzed by using the frequency domain decomposition (FDD) method, and the dominant frequencies and mode shapes of the system have been extracted. By analyzing the vibration behaviors of pristine triangular graphene sheets in four cases (right angle of 45-90-45 configuration, right angle of 60-90-30 configuration, equilateral triangle and isosceles triangle), it has been demonstrated that the natural frequencies of these sheets are higher than the natural frequency of a square sheet, with the same number of atoms, by a minimum of 7.6% and maximum of 26.6%. Therefore, for increasing the resonance range of sensors based on 2D materials, nonrectangular structures, and especially the triangular structure, can be considered as viable candidates. Although the pristine and defective equilateral triangular sheets have the highest values of resonance, the sensitivity of defective (45,90,45) triangular sheet is more than other configurations and then, defective (45,90,45) sheet is the worst choice for sensor applications.

• Smart Structures and Systems
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• v.26 no.6
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• pp.753-763
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• 2020

The early prediction of Compressive Strength of Concrete (CSC) is a significant task in the civil engineering construction projects. This study, therefore, is dedicated to introducing two novel hybrids of neural computing, namely Shuffled Complex Evolution (SCE) and Teaching-Learning-Based Optimization (TLBO) for predicting the CSC. The algorithms are applied to a Multi-Layer Perceptron (MLP) network to create the SCE-MLP and TLBO-MLP ensembles. The results revealed that, first, intelligent models can properly handle analyzing and generalizing the non-linear relationship between the CSC and its influential parameters. For example, the smallest and largest values of the CSC were 17.19 and 58.53 MPa, and the outputs of the MLP, SCE-MLP, and TLBO-MLP range in [17.61, 54.36], [17.69, 55.55] and [18.07, 53.83], respectively. Second, applying the SCE and TLBO optimizers resulted in increasing the correlation of the MLP products from 93.58 to 97.32 and 97.22%, respectively. The prediction error was also reduced by around 34 and 31% which indicates the high efficiency of these algorithms. Moreover, regarding the computation time needed to implement the SCE-MLP and TLBO-MLP models, the SCE is a considerably more time-efficient optimizer. Nevertheless, both suggested models can be promising substitutes for laboratory and destructive CSC evaluative models.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.34 no.5
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• pp.321-326
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• 2021

We fabricated BiAlO thin film by a solution process with a brush coating to be used as liquid crystal (LC) alignment layer. Solution-processed BiAlO was coated on the glass substrate by brush process. Prepared thin films were annealed at different temperatures of 80℃, 180℃, and 280℃. To verify whether the BiAlO film was formed properly, X-ray photoelectron spectroscopy analysis was performed on Bi and Al. Using a crystal rotation method by polarized optical microscopy, LC alignment state was evaluated. At the annealing temperature of 280℃, the uniform homogenous LC alignment was achieved. To reveal the mechanism of LC alignment by brush coating, field emission scanning electron microscope was used. Through this analysis, spin-coated and brush coated film surface were compared. It was revealed that physical anisotropy was induced by brush coating at a high annealing temperature. Particles were aligned in one direction along which brush coating was made, resulting in a physical anisotropy that affects a uniform LC alignment. Therefore, it was confirmed that brush coating combined with BiAlO thin film annealed at high temperature has a significant potential for LC alignment.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.259-270
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• 2022

This study explores the use of the recently developed "strain-sensing smart skin" (S⁴) method for noncontact strain measurements on cement-based samples. S⁴ sensors are single-wall carbon nanotubes dilutely embedded in thin polymer films. Strains transmitted to the nanotubes cause systematic shifts in their near-infrared fluorescence spectra, which are analyzed to deduce local strain values. It is found that with cement-based materials, this method is hampered by spectral interference from structured near-infrared cement luminescence. However, application of an opaque blocking layer between the specimen surface and the nanotube sensing film enables interference-free strain measurements. Tests were performed on cement, mortar, and concrete specimens with such modified S⁴ coatings. When specimens were subjected to uniaxial compressive stress, the spectral peak separations varied linearly and predictably with induced strain. These results demonstrate that S⁴ is a promising emerging technology for measuring strains down to ca. 30 𝜇𝜀 in concrete structures.

• Smart Structures and Systems
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• v.30 no.6
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• pp.557-569
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• 2022

Vibration-based structural health monitoring (SHM) is crucial for the dynamic maintenance of civil building structures to protect property security and the lives of the public. Analyzing these vibrations with modern artificial intelligence and deep learning (DL) methods is a new trend. This paper proposed an unsupervised deep learning method based on a convolutional autoencoder (CAE), which can overcome the limitations of conventional supervised deep learning. With the convolutional core applied to the DL network, the method can extract features self-adaptively and efficiently. The effectiveness of the method in detecting damage is then tested using a benchmark model. Thereafter, this method is used to detect damage and instant disaster events in a rubber bearing-isolated gymnasium structure. The results indicate that the method enables the CAE network to learn the intact vibrations, so as to distinguish between different damage states of the benchmark model, and the outcome meets the high-dimensional data distribution characteristics visualized by the t-SNE method. Besides, the CAE-based network trained with daily vibrations of the isolating layer in the gymnasium can precisely recover newly collected vibration and detect the occurrence of the ground motion. The proposed method is effective at identifying nonlinear variations in the dynamic responses and has the potential to be used for structural condition assessment and safety warning.

• Smart Structures and Systems
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• v.29 no.4
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• pp.523-533
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• 2022

A novel metaheuristic search method, namely black widow optimization (BWO) is employed to increase the accuracy of slope stability analysis. The BWO is a recently-developed optimizer that supervises the training of an artificial neural network (ANN) for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The designed slope bears a loaded foundation in different distances from the crest. A sensitivity analysis is conducted based on the number of active individuals in the BWO algorithm, and it was shown that the best performance is acquired for the population size of 40. Evaluation of the results revealed that the capability of the ANN was significantly enhanced by applying the BWO. In this sense, the learning root mean square error fell down by 23.34%. Also, the correlation between the testing data rose from 0.9573 to 0.9737. Therefore, the postposed BWO-ANN can be promisingly used for the early prediction of FOS in real-world projects.

• Smart Structures and Systems
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• v.30 no.2
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• pp.195-207
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• 2022

Computational drawbacks associated with regular predictive models have motivated engineers to use hybrid techniques in dealing with complex engineering tasks like simulating the compressive strength of concrete (CSC). This study evaluates the efficiency of tree potential metaheuristic schemes, namely shuffled complex evolution (SCE), multi-verse optimizer (MVO), and beetle antennae search (BAS) for optimizing the performance of a multi-layer perceptron (MLP) system. The models are fed by the information of 1030 concrete specimens (where the amount of cement, blast furnace slag (BFS), fly ash (FA1), water, superplasticizer (SP), coarse aggregate (CA), and fine aggregate (FA2) are taken as independent factors). The results of the ensembles are compared to unreinforced MLP to examine improvements resulted from the incorporation of the SCE, MVO, and BAS. It was shown that these algorithms can considerably enhance the training and prediction accuracy of the MLP. Overall, the proposed models are capable of presenting an early, inexpensive, and reliable prediction of the CSC. Due to the higher accuracy of the BAS-based model, a predictive formula is extracted from this algorithm.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.41-52
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• 2023

The purpose of this study is to review the proposed solutions to the Blockchain trilemma put forward by various research scholars and to draw conclusions by comparing the findings of each study. We found that the models so far developed either compromise scalability, decentralization, or security. The first model compromises decentralization. By partially centralizing the network, transaction processing speed can be improved, but security strength is weakened. Examples of this include Algorand and EOS. Because Algorand randomly selects the node that decides the consensus, the security of Algorand is better than EOS, wherein a designated selector decides. The second model recognizes that scalability causes a delay in speed when transactions are included in a block, reducing the system's efficiency. Compromising scalability makes it possible to increase decentralization. Representative examples include Bitcoin and Ethereum. Bitcoin is more vital than Ethereum in terms of security, but in terms of scalability, Ethereum is superior to Bitcoin. In the third model, information is stored and managed through various procedures at the expense of security. The application case is to weaken security by applying a layer 1 or 2 solution that stores and reroutes information. The expected effect of this study is to provide a new perspective on the trilemma debate and to stimulate interest in continued research into the problem.