• Computers and Concrete
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• v.33 no.1
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• pp.27-39
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• 2024

In this study, the authors investigate the free vibration behavior of three-phases functionally graded sandwich plates using a novel nth-order shear deformation theory. These plates are composed of a homogeneous core and two face-sheet layers made of different functionally graded materials. This is the novel type of the sandwich structures that can be applied in many fields of mechanical engineering and industrial. The proposed theory only requires four unknown displacement functions, and the transverse displacement does not need to be separated into bending and shear parts, simplifying the theory. One noteworthy feature of the proposed theory is its ability to capture the parabolic distribution of transverse shear strains and stresses throughout the plate's thickness while ensuring zero values on the two free surfaces. By eliminating the need for shear correction factors, the theory further enhances computational efficiency. Equations of motion are established using Hamilton's principle and solved via Navier's solution. The accuracy and efficiency of the proposed theory are verified by comparing results with available solutions. The authors then use the proposed theory to investigate the free vibration characteristics of three-phases functionally graded sandwich plates, considering the effects of parameters such as aspect ratio, side-to-thickness ratio, skin-core-skin thicknesses, and power-law indexes. Through careful analysis of the free vibration behavior of three-phases functionally graded sandwich plates, the work highlighted the significant roles played by individual material ingredients in influencing their frequencies.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.1
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• pp.57-65
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• 2024

In a fire-resistant structure, uncertainties arise in factors such as ventilation, material elasticity modulus, yield strength, coefficient of thermal expansion, external forces, and fire location. The ventilation uncertainty affects thefactor contributes to uncertainties in fire temperature, subsequently impacting the structural temperature. These temperatures, combined with material properties, give rise to uncertain structural responses. Given the nonlinear behavior of structures under fire conditions, calculating fire fragility traditionally involves time-consuming Monte Carlo simulations. To address this, recent studies have explored leveraging machine learning algorithms to predict fire fragility, aiming to enhance efficiency while maintaining accuracy. This study focuses on predicting the fire fragility of a steel moment frame building, accounting for uncertainties in fire size, location, and structural material properties. The fragility curve, derived from nonlinear structural behavior under fire, follows a log-normal distribution. The results demonstrate that the proposed method accurately and efficiently predicts fire fragility, showcasing its effectiveness in streamlining the analysis process.

• Steel and Composite Structures
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• v.51 no.1
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• pp.25-41
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• 2024

The measurement of pile bearing capacity is crucial for the design of pile foundations, where in-situ tests could be costly and time needed. The primary objective of this research was to investigate the potential use of fuzzy-based techniques to anticipate the maximum weight that concrete driven piles might bear. Despite the existence of several suggested designs, there is a scarcity of specialized studies on the exploration of adaptive neuro-fuzzy inference systems (ANFIS) for the estimation of pile bearing capacity. This paper presents the introduction and validation of a novel technique that integrates the fire hawk optimizer (FHO) and equilibrium optimizer (EO) with the ANFIS, referred to as ANFIS_FHO and ANFIS_EO, respectively. A comprehensive compilation of 472 static load test results for driven piles was located within the database. The recommended framework was built, validated, and tested using the training set (70%), validation set (15%), and testing set (15%) of the dataset, accordingly. Moreover, the sensitivity analysis is performed in order to determine the impact of each input on the output. The results show that ANFIS_FHO and ANFIS_EO both have amazing potential for precisely calculating pile bearing capacity. The R² values obtained for ANFIS_FHO were 0.9817, 0.9753, and 0.9823 for the training, validating, and testing phases. The findings of the examination of uncertainty showed that the ANFIS_FHO system had less uncertainty than the ANFIS_EO model. The research found that the ANFIS_FHO model provides a more satisfactory estimation of the bearing capacity of concrete driven piles when considering various performance evaluations and comparing it with existing literature.

• Steel and Composite Structures
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• v.50 no.2
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• pp.201-216
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• 2024

This paper is motivated by the lack of studies relating to vibration and nonlinear resonance of fluid-conveying cantilever porous GPLR pipes with fractional viscoelastic model resting on nonlinear foundations. A dynamical model of cantilever porous Graphene Platelet Reinforced (GPLR) pipes conveying fluid and resting on nonlinear foundation is proposed, and the vibration, natural frequencies and primary resonant of such system are explored. The pipe body is considered to be composed of GPLR viscoelastic polymeric pipe with porosity in which Halpin-Tsai scheme in conjunction with fractional viscoelastic model is used to govern the construction relation of the nanocomposite pipe. Three different porosity distributions through the pipe thickness are introduced. The harmonic concentrated force is also applied on pipe and excitation frequency is close to the first natural frequency. The governing equation for transverse motion of the pipe is derived by the Hamilton principle and then discretized by the Galerkin procedure. In order to obtain the frequency-response equation, the differential equation is solved with the assumption of small displacement, damping coefficient, and excitation amplitude by the multiple scale method. A parametric sensitivity analysis is carried out to reveal the influence of different parameters, such as nanocomposite pipe properties, fluid velocity and nonlinear viscoelastic foundation coefficients, on the primary resonance and linear natural frequency. Results indicate that the GPLs weight fraction porosity coefficient, fractional derivative order and the retardation time have substantial influences on the dynamic response of the system.

• Nuclear Engineering and Technology
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• v.56 no.1
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• pp.78-91
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• 2024

Double steel plate concrete composite shear wall (SCSW) has been widely utilized in nuclear power plants and high-rise structures, and its shear connectors have a substantial impact on the seismic performance of SCSW. Therefore, in this study, the mechanical properties of SCSW with angle stiffening ribs as shear connections were parametrically examined for the reactor containment structure of nuclear power plants. The axial compression ratio of the SCSW, the spacing of the angle stiffening rib arrangement and the thickness of the angle stiffening rib steel plate were selected as the study parameters. Four finite element models were constructed by using the finite element program named ABAQUS to verify the experimental results of our team, and 13 finite element models were established to investigate the selected three parameters. Thus, the shear capacity, deformation capacity, ductility and energy dissipation capacity of SCSW were determined. The research results show that: compared with studs, using stiffened ribs as shear connectors can significantly enhance the mechanical properties of SCSW; When the axial compression ratio is 0.3-0.4, the seismic performance of SCSW can be maximized; with the lowering of stiffener gap, the shear bearing capacity is greatly enhanced, and when the gap is lowered to a specific distance, the shear bearing capacity has no major affect; in addition, increasing the thickness of stiffeners can significantly increase the shear capacity, ductility and energy dissipation capacity of SCSW. With the rise in the thickness of angle stiffening ribs, the improvement rate of each mechanical property index slows down. Finally, the shear bearing capacity calculation formula of SCSW with angle stiffening ribs as shear connectors is derived. The average error between the theoretical calculation formula and the finite element calculation results is 8% demonstrating that the theoretical formula is reliable. This study can provide reference for the design of SCSW.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.307-321
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• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

• 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.

• Earthquakes and Structures
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• v.26 no.3
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• pp.239-249
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• 2024

A new layered shear continuum model box was developed to address the dynamic response issues of buried oil and gas pipelines under multi-point excitation. Vibration table tests were conducted to investigate the seismic response of buried pipelines and the surrounding soil under longitudinal multi-point excitation. A nonlinear model of the pipeline-soil interaction was established using ABAQUS finite element software for simulation and analysis. The seismic response characteristics of the pipeline and soil under longitudinal multi-point excitation were clarified through vibration table tests and simulation. The results showed good consistency between the simulation and tests. The acceleration of the soil and pipeline exhibited amplification effects at loading levels of 0.1 g and 0.2 g, which significantly reduced at loading levels of 0.4 g and 0.62 g. The peak acceleration increased with increasing loading levels, and the peak frequency was in the low-frequency range of 0 Hz to 10 Hz. The amplitude in the frequency range of 10 Hz to 50 Hz showed a significant decreasing trend. The displacement peak curve of the soil increased with the loading level, and the nonlinearity of the soil resulted in a slower growth rate of displacement. The strain curve of the pipeline exhibited a parabolic shape, with the strain in the middle of the pipeline about 3 to 3.5 times larger than that on both sides. This study provides an effective theoretical basis and test basis for improving the seismic resistance of buried oil and gas pipelines.

• Structural Engineering and Mechanics
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• v.89 no.6
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• pp.589-599
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• 2024

This study presents the usability of the high-rate single-frequency Precise Point Positioning (SF-PPP) technique based on 20 Hz Global Positioning Systems (GPS)-only observations in detecting dynamic motions. SF-PPP solutions were obtained from post-mission and real-time GNSS corrections. These include the International GNSS Service (IGS)-Final, IGS real-time (RT), real-time MADOCA (Multi-GNSS Advanced Demonstration tool for Orbit and Clock Analysis), and real-time products from the Australian/New Zealand satellite-based augmentation systems (SBAS, known as SouthPAN). SF-PPP results were compared with LVDT (Linear Variable Differential Transformer) sensor and single-frequency relative positioning (SF-RP) solutions. The findings show that the SF-PPP technique successfully detects the harmonic motions, and the real-time products-based PPP solutions were as accurate as the final post-mission products. In the frequency domain, all GNSS-based methods evaluated in this contribution correctly detect the dominant frequency of short-term harmonic oscillations, while the differences in the amplitude values corresponding to the peak frequency do not exceed 1.1 mm. However, evaluations in the time domain show that SF-PPP needs high-pass filtering to detect accurate displacement since SF-PPP solutions include trends and low-frequency fluctuations, mainly due to atmospheric effects. Findings obtained in the time domain indicate that final, real-time, and MADOCA-based PPP results capture short-term dynamic behaviors with an accuracy ranging from 3.4 mm to 8.5 mm, and SBAS-based PPP solutions have several times higher RMSE values compared to other methods. However, after high-pass filtering, the accuracies obtained from PPP methods decreased to a few mm. The outcomes demonstrate the potential of the high-rate SF-PPP method to reliably monitor structural and earthquake-induced ground motions and vibration frequencies of structures.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.4
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• pp.141-148
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• 2008

In the recent construction industry, Carbon Fiber Reinforced Polymers(CFRPs) have been highly considered as innovative strengthening materials for civil structures due to their superior material properties. This paper is to offer design data and strengthening efficiency of reinforced concrete beams strengthened with CFRP Plate. Static tests were carried out to evaluate failure modes and strengthening capacity. Displacements and strains of steel and CFRP plates were obtained and analyzed through a series of fatigue tests. Also, Those evaluated the energy dissipation. Results of the tests showed increase in strengthening ratios caused debonding failure at the end of beams. For the beams wrapped with CFRP sheets around the end of the plates, debonding failure mode that was induced from flexural cracks was indicated. Through the fatigue tests, it was observed that displacements, strains of steel and CFRP plates converged into certain values. It is also proved that the beams strengthened with CFRP plates are able to resist fatigue loading under serviceability.