• Structural Engineering and Mechanics
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• v.85 no.4
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• pp.511-529
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• 2023

This paper aims to better understand the bonding behavior in Reinforced Concrete beams strengthened with an Ultra-High Performance Fiber Reinforced Concrete (RCUHPFRC) layer on the compression side using experimental tests and numerical analyses. The UHPFRC mix design was obtained through an optimization procedure, and the characterization of the materials included compression and slant shear tests. Flexural tests were carried out in RC beams and RC-UHPFRC beams. The tests demonstrated a debonding of the UHPFRC layer. In addition, 3D finite element analyses were carried out in the Abaqus CAE program, in which the interface is modeled considering a zero-thickness cohesive-contact approach. The cohesive parameters are investigated, aiming to calibrate the numerical models, and a sensitivity analysis is performed to check the reliability of the assumed cohesive parameters and the mesh size. Finally, the experimental and numerical values are compared, showing a good approximation for both the RC beams and the RC strengthened beams.

• Steel and Composite Structures
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• v.45 no.4
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• pp.605-619
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• 2022

The tensile behavior of the Thread-fixed One-side Bolt (TOB) at high temperatures was studied using the Finite Element Modeling (FEM) to explore the structural responses that could not be measured in tests. The accuracy of the FEM was verified using the test results from the failure mode, load-displacement curve as well as yielding load. Three typical failure modes of TOB connected T-stubs were observed, which were the Flange Yielding (FY), the Bolt Failure (BF) and the Coupling Failure mode (CF). The influence of the flange thickness tb and the temperature θ on the tensile behavior of the T-stub were discussed. The initial stiffness and the yielding load decreased with the increase of the temperature. The T-stubs almost lost their resistance when the temperature exceeded 700℃. The failure modes of T-stubs were mainly decided by the flange thickness, which relates to the anchorage of the hole threads and the bending resistance of flange. The failure mode could also be changed by the high temperature. Design equations in EN 1993-1-8 were modified and verified by the FEM results. The results showed that these equations could predict the failure mode and the yielding load at different temperatures with satisfactory accuracy.

• Steel and Composite Structures
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• v.49 no.1
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• pp.65-79
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• 2023

This paper presents a development of empirical evaluations, which can be used to evaluate the damage of fiber-reinforced concrete composites (FRC) wall subjected to close-in blast loads. For this development, a combined application of numerical simulation and machine learning approaches are employed. First, finite element modeling of FRC wall under blast loading is developed and verified using experimental data. Numerical analyses are then carried out to investigate the dynamic behavior of the FRC wall under blast loading. In addition, a data set of 384 samples on the damage of FRC wall due to blast loads is then produced in order to develop machine learning models. Second, three robust machine learning models of Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) are employed to propose empirical evaluations for predicting the damage of FRC wall. The proposed empirical evaluations are very useful for practical evaluation and design of FRC wall subjected to blast loads.

• Computers and Concrete
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• v.32 no.6
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• pp.625-637
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• 2023

The application of ML approaches in determining the resisting capacity of fire damaged RC columns is introduced in this paper, on the basis of analysis data driven ML modeling. Considering the characteristics of the structural behavior of fire damaged RC columns, the representative five approaches of Kernel SVM, ANN, RF, XGB and LGBM are adopted and applied. Additional partial monotonic constraints are adopted in modelling, to ensure the monotone decrease of resisting capacity in RC column with fire exposure time. Furthermore, additional suggestions are also added to mitigate the heterogeneous composition of the training data. Since the use of ML approaches will significantly reduce the computation time in determining the resisting capacity of fire damaged RC columns, which requires many complex solution procedures from the heat transfer analysis to the rigorous nonlinear analyses and their repetition with time, the introduced ML approach can more effectively be used in large complex structures with many RC members. Because of the very small amount of experimental data, the training data are analytically determined from a heat transfer analysis and a subsequent nonlinear finite element (FE) analysis, and their accuracy was previously verified through a correlation study between the numerical results and experimental data. The results obtained from the application of ML approaches show that the resisting capacity of fire damaged RC columns can effectively be predicted by ML approaches.

• Transactions of Materials Processing
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• v.33 no.3
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• pp.169-177
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• 2024

Recently, hydrogen energy has been widely used because of strict regulations on greenhouse gas emissions. For using the hydrogen energy, it is required to supply hydrogen through a tube trailer. However hydrogen tube trailer can have excessive load problems during transportation due to reasons such as road shape and driving method, which may lead a risk of hydrogen leakage. So it is necessary to secure a high level of safety. The purpose of this study is to evaluate structural safety for the conservative design of hydrogen tube trailer. First, finite element(FE) modeling of the designed hydrogen tube trailer was performed. After that, safety evaluation method was established through static structural simulation based on the standard GC207 conditions. In addition, effectiveness of the designed model was confirmed through the results of the structural safety evaluation. Finally, driving simulation was used to derive acceleration graph according to time, which was considered as a dynamic property for the evaluation of conservative tube trailer safety evaluation. And dynamic structural simulation was conducted as a condition for actual transportation of tube trailer by applying dynamic properties. As a results, conservative safety was evaluated through dynamic structural simulation and the safety of hydrogen tube trailer was confirmed through satisfaction of the safety rate.

• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.4
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• pp.175-184
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• 2006

Two methods of the nonlinear static pushover analysis have been presented for the performance-based seismic design and evaluation of MDOF continuous bridges. Guidelines for buildings presented in FEMA-273 applying the Displacement Coefficient Method (DCM) and in ATC applying the Capacity Spectrum Method(CSM) have been modified for MDOF bridges. Two methods are compared with the time- history analysis. The lateral load distribution pattern for seismic loads has been examined in the static pushover analysis. The force-based fiber frame finite element has been implemented in the modeling of reinforced concrete piers.

• Earthquakes and Structures
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• v.27 no.2
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• pp.97-111
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• 2024

In this paper, buckling conditions and retrofitting of cylindrical steel water storage tanks with different roof types and wall thicknesses were investigated by using finite element method. Four roof types of cylindrical steel tanks which are open-top, flat-closed, conical-closed and torispherical-closed and three wall thicknesses of 4, 6 and 8 mm were considered in FE modeling of cylindrical steel tanks. The roof shapes significantly affect load distribution on the tank shell under the seismic action. Composite FRP materials are widely used for winding thin-walled cylindrical steel structures. The retrofitting efficiency of cylindrical steel water tank is tested under the seismic loading with the externally bonded CFRP laminates. In retrofitting of cylindrical steel tank, the CFRP composite material coating method was used to improve of seismic performance of cylindrical steel tanks. ANSYS software was used to analyze the cylindrical steel tanks and maximum equivalent (von-Mises) and directional deformation were obtained. Equivalent (von-Mises) stresses significantly decreased due to the coating of the tank shell with FRP composite material. In thin-walled steel structures, excessive stress causes buckling and deformations. Therefore, retrofitting led to decrease in stress, reductions in directional and buckling deformation of the open-top, flat-closed, conical-closed and torispherical-closed tanks.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.473-481
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• 2024

In this paper, we introduce a newly developed winding model to simulate the motion of underwater cable consisting of winch drums. It is assumed that only tension affects the underwater cable motion. This assumption is suitable for simulating the underwater cable motion towed by a navel vessel in a straight ahead maneuver. The underwater cable is discretized using Nodal Position Finite Element Method. This numerical method is known to be suitable for predicting the underwater cable motion with large deformation because it can express geometric nonlinearity. In this paper, the validity of the numerical method was secured by comparing it with the depth information of towing cable measured through sea experiments.

• Earthquakes and Structures
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• v.27 no.4
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• pp.303-315
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• 2024

This study proposes a comprehensive methodology for estimating accumulative damage of bridge structures under multiple seismic excitations, in the framework of site-specific probabilistic hazard analysis. Specifically, a typical earthquake-prone region in China is chosen to perform probabilistic seismic hazard analysis (PSHA) to find the mean annual rate (MAR) of ground motion intensity at a specific level, based on which, a mass of ground motion observations is selected to construct random earthquake sequences with various number of shocks. Then, nonlinear time history analysis is implemented on the finite element (FE) model of a RC girder bridge at the site of interest, to investigate structural responses under different earthquake sequences, and to develop predictive model for cumulative damage computation, in which, a scalar seismic intensity measure (IM) is adopted and its performance in damage prediction is discussed by an experimental column. Furthermore, a mathematic model is established to calculate occurrence probability of earthquakes with various number of shocks, based on PSHA and homogenous Poisson random process, and a modified cumulative damage indicator is proposed, accounting for probabilistic occurrence of various earthquake scenarios. At end, the applicability of the proposed methodology to main shock and aftershock scenarios is validated, and characteristics of damage accumulation under different multiple earthquake scenarios are discussed.

• Geomechanics and Engineering
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• v.38 no.4
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• pp.439-453
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• 2024

The research findings of two nonlinear machine learning and soft computing models- the Cuckoo optimization algorithm (COA) and the Teaching-learning-based optimization (TLBO) in combination with artificial neural network (ANN)-are presented in this article. Detailed finite element modeling (FEM) of a shallow footing on two layers of cohesionless soil provided the data sets. The models are trained and tested using the FEM outputs. Additionally, various statistical indices are used to compare and evaluate the predicted and calculated models, and the most precise model is then introduced. The most precise model is recommended to estimate the solution after the model assessment process. When the anticipated findings are compared to the FEM data, there is an excellent agreement, which indicates that the TLBO-MLP solutions in this research are reliable (R²=0.9816 for training and 0.99366 for testing). Additionally, the optimized COA-MLP network with a swarm size of 500 was observed to have R² and RMSE values of (0.9613 and 0.11459) and (0.98017 and 0.09717) for both the normalized training and testing datasets, respectively. Moreover, a straightforward formula for the soft computing model is provided, and an excellent consensus is attained, indicating a high level of dependability for the suggested model.