• Steel and Composite Structures
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• v.50 no.4
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• pp.419-428
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• 2024

Prefabricated partially-encased composite (PEC) structural component is widely used in construction industry due to its superior structural performance and easy assembly characteristic. However, the solid web in traditional PEC components tends to split concrete into two halves, thus potentially reduces structural integrity and requires double concrete pouring. To overcome the above disadvantages, a new PEC beam with open-web π-shaped steel is proposed in this paper. Four open-web PEC beams with varying sectional height, flange thickness and web void rate were constructed and tested under flexural loads. During experimental tests, all beams exhibited typical flexural failure modes with strong moment capacities and excellent ductility. Owing to the unique construction form of web opening, steel-concrete bonding properties were enhanced and very small relative steel-concrete slips were observed. Experimental results also showed that the flexural capacity of such PEC beams increased with the increase of the sectional height and flange thickness, while was not affected by the web void rate. At last, a flexural capacity formula of the open-web PEC beam was proposed based on the whole section plastic rule. The formula results agreed well with experimental results.

• Steel and Composite Structures
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• v.50 no.4
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• pp.443-458
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• 2024

The construction industry, one of the biggest producers of greenhouse emissions, is under a lot of pressure as a result of growing worries about how climate change may affect local communities. Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues connected to the manufacture of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete, which might be used in lieu of traditional concrete to reduce CO₂ emissions in the building industry. In the present work, the compressive strength (f_c) of GPC is calculated using random forests regression (RFR) methodology where natural zeolite (NZ) and silica fume (SF) replace ground granulated blast-furnace slag (GGBFS). From the literature, a thorough set of experimental experiments on GPC samples were compiled, totaling 254 data rows. The considered RFR integrated with artificial hummingbird optimization (AHA), black widow optimization algorithm (BWOA), and chimp optimization algorithm (ChOA), abbreviated as ARFR, BRFR, and CRFR. The outcomes obtained for RFR models demonstrated satisfactory performance across all evaluation metrics in the prediction procedure. For R² metric, the CRFR model gained 0.9988 and 0.9981 in the train and test data set higher than those for BRFR (0.9982 and 0.9969), followed by ARFR (0.9971 and 0.9956). Some other error and distribution metrics depicted a roughly 50% improvement for CRFR respect to ARFR.

• Structural Engineering and Mechanics
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• v.89 no.3
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• pp.309-321
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• 2024

In this paper, the effect of semi-rigid connections on the stability bearing capacity of cross-bracings in steel tubular transmission towers is investigated. Herein, a prediction method based on the hybrid model which is a combination of particle swarm optimization (PSO) and backpropagation neural network (BPNN) is proposed to accurately predict the stability bearing capacity of cross-bracings with semi-rigid connections and to efficiently conduct its probabilistic assessment. Firstly, the establishment of the finite element (FE) model of cross-bracings with semi-rigid connections is developed on the basis of the development of the mechanical model. Then, a dataset of 7425 samples generated by the FE model is used to train and test the PSO-BPNN model, and the accuracy of the proposed method is evaluated. Finally, the probabilistic assessment for the stability bearing capacity of cross-bracings with semi-rigid connections is conducted based on the proposed method and the Monte Carlo simulation, in which the geometric and material properties including the outer diameter and thickness of cross-sections and the yield strength of steel are considered as random variables. The results indicate that the proposed method based on the PSO-BPNN model has high accuracy in predicting the stability bearing capacity of cross-bracings with semi-rigid connections. Meanwhile, the semi-rigid connections could enhance the stability bearing capacity of cross-bracings and the reliability of cross-bracings would significantly increase after considering semi-rigid connections.

• Advances in nano research
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• v.16 no.3
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• pp.251-263
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• 2024

This article investigates the free vibration behavior of carbon nanotube reinforced composite (CNTRC) beams embedded using variational analytical methods and artificial neural networks (ANN). The material properties of layered functionally graded CNTRC (FG-CNTRC) beams are estimated using nonlocal parameters modified power-law with different types of CNT distributions through the thickness direction of the beam. Adopting Eringen's nonlocal elasticity theory to capture the small size effects, the nonlocal governing equations are derived and solved using the analytical method. And also, the problem was analyzed using the ANN method. The architecture of the proposed ANN model is 3-9-1. In the experiments, we used 112 different data to predict the natural frequency using ANN. Based on the nonlocal differential constitutive relations of Eringen, the equations of motion as well as the boundary conditions of the beam are derived using Hamilton's principle. The classical beam theory is used to formulate a governing equation for predicting the free vibration of laminated CNTRC beams. According to the experimental results, the prediction ability of the ANN model is very good and the natural frequency can be predicted in ANN without attempting any experiments.

• Journal of Sensor Science and Technology
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• v.33 no.3
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• pp.139-146
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• 2024

Surface-enhanced Raman scattering (SERS) enables the detection of various types of π-conjugated biological and chemical molecules owing to its exceptional sensitivity in obtaining unique spectra, offering nondestructive classification capabilities for target analytes. Herein, we demonstrate an innovative strategy that provides significant machine learning (ML)-enabled predictive SERS platforms through surface-engineered graphene via complementary hybridization with Au nanoparticles (NPs). The hybridized Au NPs/graphene SERS platforms showed exceptional sensitivity (10^-7 M) due to the collaborative strong correlation between the localized electromagnetic effect and the enhanced chemical bonding reactivity. The chemical and physical properties of the demonstrated SERS platform were systematically investigated using microscopy and spectroscopic analysis. Furthermore, an innovative strategy employing ML is proposed to predict various analytes based on a featured Raman spectral database. Using a customized data-preprocessing algorithm, the feature data for ML were extracted from the Raman peak characteristic information, such as intensity, position, and width, from the SERS spectrum data. Additionally, sophisticated evaluations of various types of ML classification models were conducted using k-fold cross-validation (k = 5), showing 99% prediction accuracy.

• Journal of the Korean Geotechnical Society
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• v.32 no.7
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• pp.5-14
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• 2016

The use of geothermal energy has increased for economically and environmentally friendly utilization, and a geothermal heat pump (GSHP) system for space heating and cooling is being used widely. As ground thermal properties such as ground thermal conductivity and ground thermal diffusivity are substantial parameters in the design of geothermal heat pump system, ground thermal conductivity should be obtained from in-situ thermal response test (TRT). This paper presents an experimental study of ground thermal properties of U and 2U type ground heat exchangers (GHEs) measured by TRTs. The U and 2U type GHEs were installed in a partially saturated dredged soil deposit, and TRTs were conducted for 48 hours. A method to derive the thermal diffusivity as well as thermal conductivity was proposed from a non-linear regression analysis. In addition, remolded soil samples from different layers were collected from the field, and soil specimens were reconstructed according to the field ground condition. Then equivalent ground thermal conductivity and ground thermal diffusivity were calculated from the lab test results and they were compared with the in-situ TRT results.

• Korean Journal of Soil Science and Fertilizer
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• v.31 no.2
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• pp.120-127
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• 1998

This study was performed to produce basic data for developing prediction techniques of desalinization through analyzing soil properties of reclaimed tidal lands, using soil samples collected in 11 units of tidal land reclamation projects. The average apparent specific gravity (bulk density), real specific gravity (particle density), porosity, and saturation percentage were measured to be 1.33, 2.64, 49.6%, and 56.3%, respectively. It was estimated that the soil texture class of reclaimed tidal lands would be silt or silt loam. The electrical conductivity and exchangeable sodium percentage were estimated to be $20{\sim}40dS\;m^{-1}$ and 30~50% in the beginning of tidal land reclamation, and the value of pH was measured to be 6.5~7.9. In conclusion, the soil properties of reclaimed tidal lands could be descrived to be saline-sodic soils with the high electrical conductivity and exchangeable sodium percentage.

• Journal of the Korean Society of Food Science and Nutrition
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• v.36 no.11
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• pp.1465-1471
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• 2007

Response surface methodology (RSM) was employed to optimize extraction conditions in order to find the maximal functional properties of fluid Cheonggukjang. Based on central composite design, a study plan was established with variations of microwave power, ethanol concentration, and extraction time. Regression analysis was applied to obtain a mathematical model. The maximum inhibitory of tyrosinase activity was found as 26.75% at the conditions of 30.56W microwave power, 2.40 g/mL of ratio of solvent to sample content and 10.00 min extraction time, respectively. The maximum superoxide dismutase (SOD)-like activity was 53.23% under the extraction conditions of 108.42 W, 4.38 g/mL and 7.84 min. Based on superimposition of three dimensional RSM with respect to extraction yield, inhibitory of tyrosinase activity and SOD-like activity obtained under the various extraction conditions, the optimum ranges of extraction conditions were found to be microwave power of $55{\sim}75$ W, ratio of solvent to sample content of $2{\sim}5$ g/mL and extraction time of $3.5{\sim}15$ min, respectively.

• Korean Journal of Food Science and Technology
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• v.25 no.6
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• pp.620-625
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• 1993

This experiment was carried out to predict the sensory properties of yogurt by instrumental methodology. Sensory attributes such as viscosity, mouth-feel, taste and quality were investigated. Instrumental parameters were measured with refractometer, viscometer, consistometer and rheometer. Sensory data showed that viscosity of peach yogurt was higher than that of strawberry and tropical-fruit-mixed (TFM) yogurts (p<.05). All instrumental parameters of peach yogurt were higher than those of strawberry and TFM yogurts, except cohesiveness and elasticity (p<.05). Viscosity measured by panelists was significantly correlated with instrumental viscosity, consistency, hardness, adhesiveness and gumminess in the fruit yogurts (p<.05). But mouth-feel and quality of yogurts showed poor relationships with instrumental parameters. The effective instrumental parameters for predicting sensory viscosity ($Y_{1}$) of yogurts were consistency ($X_{1}$), viscosity ($X_{2}$) and cohesiveness ($X_{3}$). And those for predicting mouth-feel ($Y_{2}$) were consistency. The estimated regression equations were as follows; $Y_{1}=4.968-0.0486X_{1}+0.00012X_{2}+0.0348X_{3},\;Y_{2}=5.701+0.0154X_{1}$.

• International Journal of Highway Engineering
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• v.19 no.6
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• pp.67-74
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• 2017

PURPOSES : In this study, a three-dimensional nonlinear finite element analysis (FEA) model for airport concrete pavement was developed using the commercial program ABAQUS. Users can select an analysis method and set the range of input parameters to reflect actual conditions such as environmental loading. METHODS : The geometrical shape of the FEA model was chosen by considering the concrete pavement located in the third-stage construction site of Incheon International Airport. Incompatible eight-node elements were used for the FEA model. Laboratory test results for the concrete specimens fabricated at the construction site were used as material properties of the concrete slab. The material properties of the cement-treated base suggested by the Federal Aviation Administration(FAA) manual were used as those of the lean concrete subbase. In addition, preceding studies and pavement evaluation reports of Incheon International Airport were referred for the material properties of asphalt base and subgrade. The kinetic friction coefficient between the concrete slab and asphalt base acquired from a preceding study was used for the friction coefficient between the layers. A nonlinear temperature gradient according to slab depth was used as an input parameter of environmental loading, and a quasistatic method was used to analyze traffic loading. The average load transfer efficiency obtained from an Heavy falling Weight Deflectomete(HWD) test was converted to a spring constant between adjacent slabs to be used as an input parameter. The reliability of the FEA model developed in this study was verified by comparing its analysis results to those of the FEAFAA model. RESULTS : A series of analyses were performed for environmental loading, traffic loading, and combined loading by using both the model developed in this study and the FEAFAA model under the same conditions. The stresses of the concrete slab obtained by both analysis models were almost the same. An HWD test was simulated and analyzed using the FEA model developed in this study. As a result, the actual deflections at the center, mid-edge, and corner of the slab caused by the HWD loading were similar to those obtained by the analysis. CONCLUSIONS : The FEA model developed in this study was judged to be utilized sufficiently in the prediction of behavior of airport concrete pavement.