• Advances in nano research
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• v.15 no.6
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• pp.521-531
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• 2023

Quantum-dot cellular automata (QCA) has shown great potential in the nanoscale regime as a replacement for CMOS technology. This work presents a specific approach to static random-access memory (SRAM) cell based on 2:1 multiplexer, 4-bit SRAM array, and 32-bit SRAM array in QCA. By utilizing the proposed SRAM array, a single-layer 16×32-bit SRAM with the read/write capability is presented using an optimized signal distribution network (SDN) crossover technique. In the present study, an extremely-optimized 2:1 multiplexer is proposed, which is used to implement an extremely-optimized SRAM cell. The results of simulation show the superiority of the proposed 2:1 multiplexer and SRAM cell. This study also provides a more efficient and accurate method for calculating QCA costs. The proposed extremely-optimized SRAM cell and SRAM arrays are advantageous in terms of complexity, delay, area, and QCA cost parameters in comparison with previous designs in QCA, CMOS, and FinFET technologies. Moreover, compared to previous designs in QCA and FinFET technologies, the proposed structure saves total energy consisting of overall energy consumption, switching energy dissipation, and leakage energy dissipation. The energy and structural analyses of the proposed scheme are performed in QCAPro and QCADesigner 2.0.3 tools. According to the simulation results and comparison with previous high-quality studies based on QCA and FinFET design approaches, the proposed SRAM reduces the overall energy consumption by 25%, occupies 33% smaller area, and requires 15% fewer cells. Moreover, the QCA cost is reduced by 35% compared to outstanding designs in the literature.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.851-858
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• 2023

Purpose: A study was conducted to ensure the structural safety of a raised girder bridge with improved cross-sectional efficiency compared to the conventional PSC girder. For this purpose, the cross-sectional specifications such as girder length, height, and width were determined, the arrangement of the tendons was designed, and the practical performance of the raised girder under static and dynamic loads was verified. Method: The static performance experiment examined the serviceability limit state by measuring behavioral responses such as deflection and cracking to primary and secondary static loads. In addition, the dynamic load loading experiment measured the acceleration and displacement behavior response over time to calculate the natural frequency and damping ratio to examine the usability limit state. Result: As a result of the static performance test, the deflection value based on the maximum applied load showed stable behavior, and the crack width measured at the maximum applied load level was very small, satisfying the serviceability limit state. In addition, a natural frequency exceeding the natural frequency calculated during the design of the dynamic loading experiment was found, and a damping ratio that satisfies the current regulations was found to be secured.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.882-896
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• 2023

Pupose: The purpose is to prepare safety management and seismic reinforcement measures that can effectively improve the potential risks of earthquake-resistant design and the deficiencies of safety guidance and inspection of factory facilities in national industrial complexes. Method: In this study, problems and improvement measures were derived through investigation and analysis of overall earthquake disaster safety management, such as safety management status and management system in preparation for earthquake disasters in national industrial complexes. was implemented to suggest improvement plans based on facility types and structural characteristics. Result: In conclusion, the problems of safety management and seismic reinforcement in preparation for earthquake disasters in national industrial complexes were summarized and classified into four types (seismic performance evaluation and related system supplementation, authority of tenant companies and local governments, seismic reinforcement and safety management support measures, organizational structure capacity building) to derive improvement measures. Conclusion: Based on this, seismic reinforcement measures that companies in national industrial complexes should implement in preparation for earthquake disasters were prepared, and detailed plans for each measure were presented.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.5
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• pp.875-884
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• 2023

Hydrogen infiltration into metals has been reported to induce alterations in their mechanical properties under load. In this study, we conducted CTOD (Crack Tip Opening Displacement) tests on steel specimens designed for use in liquid hydrogen storage systems. Electrochemical hydrogen charging was performed using both FCC series austenitic stainless steel and BCC series structural steel specimens, while CTOD testing was carried out using a 500kN-class material testing machine. Results indicate a notable divergence in behavior: SS400 test samples exhibited a higher susceptibility to failure compared to austenitic stainless steel counterparts, whereas SUS 316L test samples displayed minimal changes in displacement and maximum load due to hydrogen charging. However, SEM (Scanning Electron Microscopy) analysis results presented challenges in clearly explaining the mechanical degradation phenomenon in the tested materials. This study's resultant database holds significant promise for enhancing the safety design of liquid hydrogen storage systems, providing invaluable insights into the performance of various steel alloys under the influence of hydrogen embrittlement.

• Steel and Composite Structures
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• v.49 no.2
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• pp.161-180
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• 2023

In the previous research, the axial compressive capacity models for the glass fiber-reinforced polymer (GFRP)-reinforced circular concrete compression elements restrained with GFRP helix were put forward based on small and noisy datasets by considering a limited number of parameters portraying less accuracy. Consequently, it is important to recommend an accurate model based on a refined and large testing dataset that considers various parameters of such components. The core objective and novelty of the current research is to suggest a deep learning model for the axial compressive capacity of GFRP-reinforced circular concrete columns restrained with a GFRP helix utilizing various parameters of a large experimental dataset to give the maximum precision of the estimates. To achieve this aim, a test dataset of 61 GFRP-reinforced circular concrete columns restrained with a GFRP helix has been created from prior studies. An assessment of 15 diverse theoretical models is carried out utilizing different statistical coefficients over the created dataset. A novel model utilizing the group method of data handling (GMDH) has been put forward. The recommended model depicted good effectiveness over the created dataset by assuming the axial involvement of GFRP main bars and the confining effectiveness of transverse GFRP helix and depicted the maximum precision with MAE = 195.67, RMSE = 255.41, and R2 = 0.94 as associated with the previously recommended equations. The GMDH model also depicted good effectiveness for the normal distribution of estimates with only a 2.5% discrepancy from unity. The recommended model can accurately calculate the axial compressive capacity of FRP-reinforced concrete compression elements that can be considered for further analysis and design of such components in the field of structural engineering.

• Composites Research
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• v.37 no.1
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• pp.32-39
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• 2024

Solid-state hydrogen storage is gaining prominence as a crucial subject in advancing the hydrogen-based economy and innovating energy storage technology. This storage method shows superior characteristics in terms of safety, storage, and operational efficiency compared to existing methods such as compression and liquefied hydrogen storage. In this study, we aim to evaluate the solid hydrogen storage performance on the nanotube surface by various structural design factors. This is accomplished through molecular dynamics simulations (MD) with the aim of uncovering the underlying ism. The simulation incorporates diverse carbon nanotubes (CNTs) - encompassing various diameters, multi-walled structures (MWNT), single-walled structures (SWNT), and boron-nitrogen nanotubes (BNNT). Analyzing the storage and effective release of hydrogen under different conditions via the radial density function (RDF) revealed that a reduction in radius and the implementation of a double-wall configuration contribute to heightened solid hydrogen storage. While the hydrogen storage capacity of boron-nitrogen nanotubes falls short of that of carbon nanotubes, they notably surpass carbon nanotubes in terms of effective hydrogen storage capacity.

• Advances in nano research
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• v.16 no.6
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• pp.623-638
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• 2024

This study investigates the thermal post-buckling behavior of concrete eccentric annular sector plates reinforced with graphene oxide powders (GOPs). Employing the minimum total potential energy principle, the plates' stability and response under thermal loads are analyzed. The Haber-Schaim foundation model is utilized to account for the support conditions, while the transform differential quadrature method (TDQM) is applied to solve the governing differential equations efficiently. The integration of GOPs significantly enhances the mechanical properties and stability of the plates, making them suitable for advanced engineering applications. Numerical results demonstrate the critical thermal loads and post-buckling paths, providing valuable insights into the design and optimization of such reinforced structures. This study presents a machine learning algorithm designed to predict complex engineering phenomena using datasets derived from presented mathematical modeling. By leveraging advanced data analytics and machine learning techniques, the algorithm effectively captures and learns intricate patterns from the mathematical models, providing accurate and efficient predictions. The methodology involves generating comprehensive datasets from mathematical simulations, which are then used to train the machine learning model. The trained model is capable of predicting various engineering outcomes, such as stress, strain, and thermal responses, with high precision. This approach significantly reduces the computational time and resources required for traditional simulations, enabling rapid and reliable analysis. This comprehensive approach offers a robust framework for predicting the thermal post-buckling behavior of reinforced concrete plates, contributing to the development of resilient and efficient structural components in civil engineering.

• Steel and Composite Structures
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• v.51 no.5
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• pp.509-527
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• 2024

Bonding carbon fiber-reinforced polymer (CFRP) laminates have been extensively employed in the restoration of steel constructions. In addition to the mechanical properties of the CFRP, the bond strength (P_U) between the CFRP and steel is often important in the eventual strengthened performance. Nonetheless, the bond behavior of the CFRP-steel (CS) interface is exceedingly complicated, with multiple failure causes, giving the P_U challenging to forecast, and the CFRP-enhanced steel structure is unsteady. In just this case, appropriate methods were established by hybridized Random Forests (RF) and support vector regression (SVR) approaches on assembled CS single-shear experiment data to foresee the P_U of CS, in which a recently established optimization algorithm named Aquila optimizer (AO) was used to tune the RF and SVR hyperparameters. In summary, the practical novelty of the article lies in its development of a reliable and efficient method for predicting bond strength at the CS interface, which has significant implications for structural rehabilitation, design optimization, risk mitigation, cost savings, and decision support in engineering practice. Moreover, the Fourier Amplitude Sensitivity Test was performed to depict each parameter's impact on the target. The order of parameter importance was t_c> L_c > E_A > t_A > E_c > b_c > f_c > f_A from largest to smallest by 0.9345 > 0.8562 > 0.79354 > 0.7289 > 0.6531 > 0.5718 > 0.4307 > 0.3657. In three training, testing, and all data phases, the superiority of AO - RF with respect to AO - SVR and MARS was obvious. In the training stage, the values of R² and VAF were slightly similar with a tiny superiority of AO - RF compared to AO - SVR with R² equal to 0.9977 and VAF equal to 99.772, but large differences with results of MARS.

• Smart Structures and Systems
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• v.33 no.2
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• pp.165-175
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• 2024

Rotating beams play a crucial role in representing complex mechanical components that are prevalent in vital sectors like energy and transportation industries. These components are susceptible to the initiation and propagation of cracks, posing a substantial risk to their structural integrity. This study presents a two-stage methodology for detecting the location and estimating the size of an open-edge transverse crack in a rotating Euler-Bernoulli beam with a uniform cross-section. Understanding the dynamic behavior of beams is vital for the effective design and evaluation of their operational performance. In this regard, modal parameters such as natural frequencies and eigenmodes are frequently employed to detect and identify damages in mechanical components. In this instance, the Frobenius method has been employed to determine the first two natural frequencies and corresponding eigenmodes associated with flapwise bending vibration. These calculations have been performed by solving the governing differential equation that describes the motion of the beam. Various parameters have been considered, such as rotational speed, beam slenderness, hub radius, and crack size and location. The effect of the crack has been replaced by a rotational spring whose stiffness represents the increase in local flexibility as a result of the damage presence. In the initial phase of the proposed methodology, a damage index utilizing the slope of the beam's eigenmode has been employed to estimate the location of the crack. After detecting the presence of damage, the size of the crack is determined using a Genetic Algorithm optimization technique. The ultimate goal of the proposed methodology is to enable the development of more suitable and reliable maintenance plans.

• Structural Engineering and Mechanics
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• v.89 no.6
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• pp.557-570
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• 2024

Due to interfacial ageing, chemical action and interfacial damage, the interface debonding may appear in the interfaces of composite laminates. Particularly, the laminates display a side-dependent effect at small scale. In this work, a three-dimensional (3D) and anisotropic thick nanoplate model is proposed to investigate the effects of imperfect interface and nonlocal parameter on the bending deformation, vibrational response and buckling stability of one-dimensional (1D) hexagonal quasicrystal (QC) layered nanoplates. By combining the linear spring model with the transferring matrix method, exact solutions of phonon and phason displacements, phonon and phason stresses of bending deformation, the natural frequencies of vibration and the critical buckling loads of 1D hexagonal QC layered nanoplates are derived with imperfect interfaces and nonlocal effects. Numerical examples are illustrated to demonstrate the effects of the imperfect interface parameter, aspect ratio, thickness, nonlocal parameter, and stacking sequence on the bending deformation, the vibrational response and the critical buckling load of 1D hexagonal QC layered nanoplate. The results indicate that both the interface debonding and nonlocal effect can reduce the stiffness and stability of layered nanoplates. Increasing thickness of QC coatings can enhance the stability of sandwich nanoplates with the perfect interfaces, while it can reduce first and then enhance the stability of sandwich nanoplates with the imperfect interfaces. The biaxial compression easily results in an instability of the QC layered nanoplates compared to uniaxial compression. QC material is suitable for surface layers in layered structures. The mechanical behavior of QC layered nanoplates can be optimized by imposing imperfect interfaces and controlling the stacking sequence artificially. The present solutions are helpful for the various numerical methods, thin nanoplate theories and the optimal design of QC nano-composites in engineering practice with interfacial debonding.