• Steel and Composite Structures
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• v.46 no.1
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• pp.75-92
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• 2023

The novel composite cold-formed steel (CFS)/engineered cementitious composites (ECC) beams have been recently presented. The new composite section exhibited superior structural performance as a flexural member, benefiting from the lightweight thin-walled CFS sections with improved buckling and torsional properties due to the restraints provided by thinlayered ECC. This paper investigated the shear performance of the new composite CFS/ECC section. Twenty-eight simply supported beams, with a shear span-to-depth ratio of 1.0, were assembled back-to-back and tested under a 3-point loading scheme. Bare CFS, composite CFS/ECC utilising ECC with Polyethylene fibres (PE-ECC), composite CFS/MOR, and CFS/HSC utilising high-strength mortar (MOR) and high-strength concrete (HSC) as replacements for PE-ECC were compared. Different failure modes were observed in tests: shear buckling modes in bare CFS sections, contact shear buckling modes in composite CFS/MOR and CFS/HSC sections, and shear yielding or block shear rupture in composite CFS/ECC sections. As a result, composite CFS/ECC sections showed up to 96.0% improvement in shear capacities over bare CFS, 28.0% improvement over composite CFS/MOR and 13.0% over composite CFS/HSC sections, although MOR and HSC were with higher compressive strength than PE-ECC. Finally, shear strength prediction formulae are proposed for the new composite sections after considering the contributions from the CFS and ECC components.

• Transactions of Materials Processing
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• v.32 no.1
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• pp.12-19
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• 2023

The armature core is a part responsible for the skeleton of the steering wheel. Currently, in the case of commercial trucks, the main parts of the parts are manufactured separately and then the product is produced through welding. In the case of this production method, quality and cost problems of the welded parts occur, and an integrated armature core made of magnesium alloy is used in passenger vehicles. However, in the case of commercial trucks, there is no application case and research is insufficient. Therefore, this study aims to develop an all-in-one armature core that simultaneously applies a magnesium alloy material and a die casting method to reduce the weight and improve the quality of the existing steel armature core. The product was modeled based on the shape of a commercial product, and finite element analysis (FEA) was performed through Ls-dyna, a general-purpose analysis program. Through digital image correlation (DIC) and uniaxial tensile test, the accurate physical properties of the material were obtained and applied to the analysis. A total of four types of compression were applied by changing the angle and ground contact area of the product according to the actual reliability test conditions. analysis was carried out. As a result of FEA, it was confirmed that damage occurred in the spoke area, and spoke thickness (t_spoke), base thickness (t_base), and rim and spoke connection (R) were designated as design variables, and the total weight and maximum equivalent stress occurring in the armature core We specify an objective function that simultaneously minimizes . A prediction function was derived using the sequential response surface method to identify design variables that minimized the objective function, and it was confirmed that it was improved by 22%.

• Journal of the Korean GEO-environmental Society
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• v.24 no.8
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• pp.13-20
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• 2023

Tunnelling through the geological anomalies has widely known to have many difficulties such as bottom heave, crack of lining, squeezing and so on. To stabilize the tunnel during the construction or after construction, various reinforcing methods have been introduced and applied such as micropiling at the bottom of tunnel to prevent the bottom heave. In this study, long-term behavior of tunnel in the presence of geological anomalies was predicted using numerical analyses. To this end, material properties for swelling rock model capable of representing the rock swelling behavior was obtained using matching process with measured data to validate the adopted model. After the model validation, simulations were performed to predict the long-term behavior of tunnel in the geological anomalies.

• Journal of the Korean Wood Science and Technology
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• v.51 no.4
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• pp.239-252
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• 2023

In this study, bending and compression strength tests were performed to investigate effect of composition of layer layout of Larix cross-laminated timber (CLT) on mechanical properties. The Larix CLT consists of five laminae, and specimens were classified into four types according to grade and composition of layer. The layer's layout were composited as follows 1) cross-laminating layers in major and minor direction (Type A), and 2) cross-laminating external layer in major direction and internal layer applied grade of layer in minor direction (Type B). E12 and E16 were used as grades of lamina for major direction layer of Type A and external layer of Type B according to KS F 3020. In results of the bending test of CLT using same grade layer according to layer composition, the modulus of elasticity (MOE) of Type B was higher than Type A. In case of prediction of bending MOE of Larix CLT, the experimental MOE was higher than 1.00 to 1.09 times for Shear analogy method and 1.14 to 1.25 times for Gamma method. Therefore, it is recommended to predict the bending MOE for Larix CLT by shear analogy method. Compression strength of CLT in accordance with layer composition was measured to be 2% and 9% higher for Type A using E12 and E16 layers than Type B, respectively. In failure mode of Type A, progress direction of failure generated under compression load was confirmed to transfer from major layer to minor layer by rolling shear or bonding line failure due to the middle lamina in major direction.

• Computers and Concrete
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• v.30 no.5
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• pp.301-310
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• 2022

Cement-based sensors have been widely used as structural health monitoring systems, however, their long-term sensing performance have not actively investigated. In this study, a deep learning-based methodology is adopted to predict the long-term piezoresistive properties of cement-based sensors. Samples with different multi-walled carbon nanotube contents (0.1, 0.3, and 0.5 wt.%) are fabricated, and piezoresistive tests are conducted over 10,000 loading cycles to obtain the training data. Time-dependent degradation is predicted using a modified long short-term memory (LSTM) model. The effects of different model variables including the amount of training data, number of epochs, and dropout ratio on the accuracy of predictions are analyzed. Finally, the effectiveness of the proposed approach is evaluated by comparing the predictions for long-term piezoresistive sensing performance with untrained experimental data. A sensitivity of 6% is experimentally examined in the sample containing 0.1 wt.% of MWCNTs, and predictions with accuracy up to 98% are found using the proposed LSTM model. Based on the experimental results, the proposed model is expected to be applied in the structural health monitoring systems to predict their long-term piezoresistice sensing performances during their service life.

• Advances in concrete construction
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• v.14 no.3
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• pp.153-167
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• 2022

The interaction between blast load and structures, as well as the interaction among structural members may well affect the structural response and damages. Therefore, it is necessary to analyse more realistic reinforced concrete structures in order to gain an extensive knowledge on the possible structural response under blast load effect. Among all the civilian structures, columns are considered to be the most vulnerable to terrorist threat and hence detailed investigation in the dynamic response of these structures is essential. Therefore, current research examines the effect of blast loads on the reinforced concrete columns via development of Pressure- Impulse (P-I) diagrams. In the finite element analysis, the level of damage on each of the aforementioned RC column will be assessed and the response of the RC columns when subjected to explosive loads will also be identified. Numerical models carried out using LS-DYNA were compared with experimental results. It was shown that the model yields a reliable prediction of damage on all RC columns. Validation study is conducted based on the experimental test to investigate the accuracy of finite element models to represent the behaviour of the models. The blast load application in the current research is determined based on the Lagrangian approach. To develop the designated P-I curves, damage assessment criteria are used based on the residual capacity of column. Intensive investigations are implemented to assess the effect of column dimension, concrete and steel properties and reinforcement ratio on the P-I diagram of RC columns. The produced P-I models can be applied by designers to predict the damage of new columns and to assess existing columns subjected to different blast load conditions.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.33 no.6
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• pp.276-281
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• 2023

With the high design freedom of the additive manufacturing process, there is a growing interest in multi-dimensional lattice structures among researchers, who are studying intricate structural modeling that is challenging to produce using conventional manufacturing processes. In the case of titanium alloy implants for human insertion, a multi-dimensional lattice structure is employed to ensure compatibility with bones, adjusting strength and elastic modulus to levels similar to those of bones. Therefore, securing a database on the mechanical properties based on lattice structure design variables and the development of related simulation techniques are believed to efficiently facilitate the customization of implants. In this study, lattice structures were additively manufactured using Ti-6Al-4V alloy, and the elastic modulus was measured based on design parameters. The results were compared with simulations, and an approach to finite element analysis for accurate prediction of the elastic modulus was proposed.

• Journal of the Korea Institute of Building Construction
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• v.23 no.6
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• pp.683-692
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• 2023

This research assesses the mechanical properties of concrete, utilizing both normal and lightweight aggregates, through measurements of compressive strength and ultrasonic pulse velocity. The study observed that concrete with normal aggregates exhibited higher compressive strength in its initial stages, whereas concrete with lightweight aggregates showed increased strength over time, likely attributed to the higher water absorption rate of lightweight aggregates. Ultrasonic pulse velocity generally registered higher in normal aggregate concrete, barring a specific duration, presumably due to variations in the internal pore structure of the aggregates. The correlation coefficient(R²) for the strength prediction equation, derived from the relationship between compressive strength and ultrasonic pulse velocity, exceeds 0.95. This high correlation suggests that the predictive equation based on these experimental findings is a reliable method for estimating concrete strength.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.101-106
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• 2023

Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]

• Journal of Korean Society on Water Environment
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• v.39 no.6
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• pp.465-474
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• 2023

The chemical composition and molecular weight characteristics of dissolved organic matter (DOM) exert a profound influence on the efficiency of organic matter removal in water treatment systems, acting as efficiency predictive indicators. This research evaluated the primary chemical and molecular weight properties of DOM derived from diverse sources, including rivers, lakes, and biomasses, and assessed their relationship with the efficiency of coagulation/flocculation treatments. Dissolved organic carbon (DOC) removal efficiency through coagulation/flocculation exhibited significant correlations with DOM's hydrophobic distribution, the ratio of humic-like to protein-like fluorescence, and the molecular weight associated with humic substances (HS). These findings suggest that the DOC removal rate in coagulation/flocculation processes is enhanced by a higher presence of HS in DOM, an increased influence of externally sourced DOM, and more presence of high molecular weight compounds. The results of this study further posit that the efficacy of water treatment processes can be more accurately predicted when considering multiple DOM characteristics rather than relying on a singular trait. Based on major results from this study, a predictive model for DOC removal efficiency by coagulation/flocculation was formulated as: 24.3 - 7.83 × (fluorescence index) + 0.089 × (hydrophilic distribution) + 0.102 × (HS molecular weight). This proposed model, coupled with supplementary monitoring of influent organic matter, has the potential to enhance the design and predictive accuracy for coagulation/flocculation treatments targeting DOC removal in future applications.