• Structural Engineering and Mechanics
• /
• v.77 no.4
• /
• pp.441-461
• /
• 2021

Reinforced concrete (RC) columns are crucial in building structures and they are of higher vulnerability to terrorist threat than any other structural elements. Thus it is of great interest and necessity to achieve a comprehensive understanding of the possible responses of RC columns when exposed to high intensive blast loads. The primary objective of this study is to derive analytical formulas to assess vulnerability of RC columns using an advanced numerical modelling approach. This investigation is necessary as the effect of blast loads would be minimal to the RC structure if the explosive charge is located at the safe standoff distance from the main columns in the building and therefore minimizes the chance of disastrous collapse of the RC columns. In the current research, finite element model is developed for RC columns using LS-DYNA program that includes a comprehensive discussion of the material models, element formulation, boundary condition and loading methods. Numerical model is validated to aid in the study of RC column testing against the explosion field test results. Residual capacity of RC column is selected as damage criteria. Intensive investigations using Arbitrary Lagrangian Eulerian (ALE) methodology are then implemented to evaluate the influence of scaled distance, column dimension, concrete and steel reinforcement properties and axial load index on the vulnerability of RC columns. The generated empirical formulae can be used by the designers to predict a damage degree of new column design when consider explosive loads. With an extensive knowledge on the vulnerability assessment of RC structures under blast explosion, advancement to the convention design of structural elements can be achieved to improve the column survivability, while reducing the lethality of explosive attack and in turn providing a safer environment for the public.

• Genomics & Informatics
• /
• v.19 no.4
• /
• pp.46.1-46.11
• /
• 2021

Moringa oleifera is nowadays raising as the most preferred medicinal plant, as every part of the moringa plant has potential bioactive compounds which can be used as herbal medicines. Some bioactive compounds of M. oleifera possess potential anti-cancer properties which interact with the apoptosis protein p53 in cancer cell lines of oral squamous cell carcinoma. This research work focuses on the interaction among the selected bioactive compounds derived from M. oleifera with targeted apoptosis protein p53 from the apoptosis pathway to check whether the bioactive compound will induce apoptosis after the mutation in p53. To check the toxicity and drug-likeness of the selected bioactive compound derived from M. oleifera based on Lipinski's Rule of Five. Detailed analysis of the 3D structure of apoptosis protein p53. To analyze protein's active site by CASTp 3.0 server. Molecular docking and binding affinity were analyzed between protein p53 with selected bioactive compounds in order to find the most potential inhibitor against the target. This study shows the docking between the potential bioactive compounds with targeted apoptosis protein p53. Quercetin was the most potential bioactive compound whereas kaempferol shows poor affinity towards the targeted p53 protein in the apoptosis pathway. Thus, the objective of this research can provide an insight prediction towards M. oleifera derived bioactive compounds and target apoptosis protein p53 in the structural analysis for compound isolation and in-vivo experiments on the cancer cell line.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
• /
• v.18 no.3
• /
• pp.1-6
• /
• 2022

Ground source heat pump (GSHP) system is highly efficient and environment-friendly and supplies heating, cooling and hot water to buildings. For an optimal design of the GSHP system, the ground thermal properties should be determined to estimate the heat exchange rate between ground and borehole heat exchangers (BHE) and the system performance during long-term operating periods. However, the process increases the initial cost and construction period, which causes the system to be hindered in distribution. On the other hand, much research has been applied to the artificial neural network (ANN) to solve problems based on data efficiently and stably. This research proposes the predictive performance model utilizing ANN considering local characteristics and weather data for the predictive performance model. The ANN model predicts the entering water temperature (EWT) from the GHEs to the heat pump for the modular GHEs, which were developed to reduce the cost and spatial disadvantages of the vertical-type GHEs. As a result, the temperature error between the data and predicted results was 3.52%. The proposed approach was validated to predict the system performance and EWT of the GSHP system.

• Journal of the Korean Recycled Construction Resources Institute
• /
• v.10 no.3
• /
• pp.235-242
• /
• 2022

The percolation threshold of the CNT-reinforced cement paste is closely related to the optimal CNT amount to maximize the sensing ability of self-sensing concrete. However, the percolation threshold has various values depending on the cement, CNT, and water-to-cement ratio used. In this study, a percolation simulation model was proposed to predict the percolation threshold of the CNT-reinforced cement paste. The proposed model can simulate the percolation according to the amount of CNT using only the properties of CNT and cement, and for this, the concept of the number of aggregated CNT particles was used. The percolation simulation consists of forming a pre-hydrated cement paste model, random dispersion of CNTs, and percolation investigation. The simulation used CNT-reinforced cement paste with a water-cement ratio of 0.4 to 0.6, and the simulated percolation threshold point showed high accuracy with a simulation residual ratio of up to 7.5 % compared to the literature results.

• Steel and Composite Structures
• /
• v.42 no.3
• /
• pp.407-426
• /
• 2022

Superior to traditional welded studs, high strength friction-grip bolted shear connectors facilitate the assembling and demounting of the composite members, which maximizes the potential for efficiency in the construction and retrofitting of new and old structures respectively. Hence, it is necessary to investigate the structural properties of high strength friction-grip bolts used in steel concrete composite beams. By means of push-out tests, an experimental study was conducted on post-installed high strength friction-grip bolts, considering the effects of different bolt size, concrete strength, bolt tensile strength and bolt pretension. The test results showed that bolt shear fracture was the dominant failure mode of all specimens. Based on the load-slip curves, uplifting curves and bolt tensile force curves between the precast concrete slab and steel beam obtained by push-out tests, the anti-slip performance of steel-concrete interface and shear behavior of bolt shank were studied, including the quantitative analysis of anti-slip load, and anti-slip stiffness, frictional coefficient, shear stiffness of bolt shank and ultimate shear capacity. Meanwhile, the interfacial anti-slip stiffness and shear stiffness of bolt shank were defined reasonably. In addition, a total of 56 push-out finite element models verified by the experimental results were also developed, and used to conduct parametric analyses for investigating the shear behavior of high-strength bolted shear connectors in steel-concrete composite beams. Finally, on ground of the test results and finite element simulation analysis, a new design formula for predicting shear capacity was proposed by nonlinear fitting, considering the bolt diameter, concrete strength and bolt tensile strength. Comparison of the calculated value from proposed formula and test results given in the relevant references indicated that the proposed formulas can give a reasonable prediction.

• Geomechanics and Engineering
• /
• v.29 no.3
• /
• pp.291-300
• /
• 2022

Tunnel boring machines combined with the earth pressure balanced shield method (EPB shield TBMs) have been adopted in urban areas as they allow excavation of tunnels with limited ground deformation through continuous and repetitive excavation and support. Nevertheless, the expansion of TBM construction requires much more minor and exquisitely controlled surface settlement to prevent economic loss. Several parametric studies controlling the tunnel's geometry, ground properties, and TBM operational factors assuming ordinary conditions for EPB shield TBM excavation have been conducted, but the impact of excessive excavation on the induced settlement has not been adequately studied. This study conducted a numerical evaluation of surface settlement induced by the ground loss from face imbalance, excessive excavation, and tail void grouting. The numerical model was constructed using FLAC3D and validated by comparing its result with the field data from literature. Then, parametric studies were conducted by controlling the ground stiffness, face pressure, tail void grouting pressure, and additional volume of muck discharge. As a result, the contribution of these operational factors to the surface settlement appeared differently depending on the ground stiffness. Except for the ground stiffness as the dominant factor, the order of variation of surface settlement was investigated, and the volume of additional muck discharge was found to be the largest, followed by the face pressure and tail void grouting pressure. The results from this study are expected to contribute to the development of settlement prediction models and understanding the surface settlement behavior induced by TBM excavation.

• Journal of the Society of Naval Architects of Korea
• /
• v.59 no.6
• /
• pp.385-392
• /
• 2022

Naval ship could face a variety of threats in isolated seas. In particular, fires and flooding are defined as disasters that are very likely to cause irreparable damage to ships. These disasters have a very high risk of personal injury as well. Therefore, when a disaster occurs, it must be quickly suppressed, but if there are people in the disaster area, the protection of life must be given priority. In order to quickly evacuate the ship crew in case of a disaster, we would like to propose a plan to quickly explore the evacuation route even in urgent situations. Using commercial escape simulation software, we obtain the data for deep neural network learning with simulations according to aisle characteristics and the properties and number of evacuation person. Using the obtained data, the passage prediction model is trained with a deep learning, and the passage time is predicted through the learned model. Construct a numerical map of a naval ship and construct a distance matrix of the vessel using predicted passage time data. The distance matrix configured in one of the path search algorithms, the Dijkstra algorithm, is applied to explore the evacuation path of naval ship.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.35 no.2
• /
• pp.134-142
• /
• 2022

As industry and technology go through advancement, it is hard to search new materials which satisfy various standards through conventional trial-and-error based research methods. Crystal Graph Convolutional Neural Network(CGCNN) is a neural network which uses material's features as train data, and predicts the material properties(formation energy, bandgap, etc.) much faster than first-principles calculation. This report introduces how to train the CGCNN model which predicts the formation energy using open database. It is anticipated that with a simple programming skill, readers could construct a model using their data and purpose. Developing machine learning model for materials science is going to help researchers who should explore large chemical and structural space to discover materials efficiently.

• Steel and Composite Structures
• /
• v.44 no.3
• /
• pp.309-324
• /
• 2022

The post-fire elastic stiffness and performance of concrete-filled steel tube (CFST) columns containing recycled aggregate concrete (RAC) has rarely been addressed, particularly in terms of material properties. This study was conducted with the aim of assessing the modulus of elasticity of recycled aggregate concrete-filled steel tube (RACFST) stub columns following thermal loading. The test data were employed to model and assess the elastic modulus of circular RACFST stub columns subjected to axial loading after exposure to elevated temperatures. The length/diameter ratio of the specimens was less than three to prevent the sensitivity of overall buckling for the stub columns. The gene expression programming (GEP) method was employed for the model development. The GEP model was derived based on a comprehensive experimental database of heated and non-heated RACFST stub columns that have been properly gathered from the open literature. In this study, by using specifications of 149 specimens, the variables were the steel section ratio, applied temperature, yielding strength of steel, compressive strength of plain concrete, and elastic modulus of steel tube and concrete core (RAC). Moreover, parametric and sensitivity analyses were also performed to determine the contribution of different effective parameters to the post-fire elastic modulus. Additionally, comparisons and verification of the effectiveness of the proposed model were made between the values obtained from the GEP model and the formulas proposed by different researchers. Through the analyses and comparisons of the developed model against formulas available in the literature, the acceptable accuracy of the model for predicting the post-fire modulus of elasticity of circular RACFST stub columns was seen.

• Earthquakes and Structures
• /
• v.22 no.1
• /
• pp.1-13
• /
• 2022

When a building suffers damages under moderate to severe loading condition, its physical properties such as damping and stiffness parameters will change. There are different practical methods besides various numerical procedures that have successfully detected a range of these changes. Almost all the previous proposed methods used to work with translational components of mode shapes, probably because extracting these components is more common in vibrational tests. This study set out to investigate the influence of using both rotational and translational components of mode shapes, in detecting damages in 3-D steel structures elements. Three different sets of measured components of mode shapes are examined: translational, rotational, and also rotational/translational components in all joints. In order to validate our assumptions two different steel frames with three damage scenarios are considered. An iterative model updating program is developed in the MATLAB software that uses the OpenSees as its finite element analysis engine. Extensive analysis shows that employing rotational components results in more precise prediction of damage location and its intensity. Since measuring rotational components of mode shapes still is not very convenient, modal dynamic expansion technique is applied to generate rotational components from measured translational ones. The findings indicated that the developed model updating program is really efficient in damage detection even with generated data and considering noise effects. Moreover, methods which use rotational components of mode shapes can predict damage's location and its intensity more precisely than the ones which only work with translational data.