• Korean Journal of Construction Engineering and Management
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• v.19 no.4
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• pp.43-51
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• 2018

As the inventory of aged apartments is expected to increase explosively, the importance of maintenance to improve the durability of concrete facilities is increasing. Concrete compressive strength is a representative index of durability of concrete facilities, and is an important item in the precision safety diagnosis for facility maintenance. However, existing methods for measuring the concrete compressive strength and determining the maintenance of concrete facilities have limitations such as facility safety problem, high cost problem, and low reliability problem. In this study, we proposed a model that can predict the concrete compressive strength through images by using deep convolution neural network technique. Learning, validation and testing were conducted by applying the concrete compressive strength dataset constructed through the concrete specimen which is produced in the laboratory environment. As a result, it was found that the concrete compressive strength could be learned by using the images, and the validity of the proposed model was confirmed.

• Water for future
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• v.24 no.1
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• pp.63-71
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• 1991

This study represents the methodology for feasibility analysis of small hydro power plants. Cumulative density function of Weibull distribution and Thi-essen method were adopted to beside flow duration curve at candidate sites. The performance prediction model and construction cost estimation model for tunnel-type small hydro power plants were developed. Eight candidate sites existing on Han river selected and surveyed for actual sites reconnaissance. The performance characteristics and economical feasibility for these sites were analyzed by using developed models. As a result, it was found that the optimum design flowrates with the lowest unit generation cost for tunnel-type small hydro power plants were the flowrate concerning with between 20 % and 30 % of time ratio on the flow duration curve. Additionally, primary design specifications such as design flowrate, effective head, capacity, annual average load factor, annual electricity production were estimated and discussed for surveyed sites.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.353-358
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• 2001

Since the financial crisis broke out, liquidity has become the critical issue in housing construction industry. In order to secure liquidity, it is prerequisite to precisely forecast cash flow. However, construction companies have failed to come up with a systematic process to manage and forecast cash flow. Until now, companies have solely relied on the prediction of profits and losses, which is carried out as they review business feasibility. To obtain more accurate cash flow forecast model, practical pattern of payments should be taken into account. In this theory, basic model that analyzes practical housing payment collection pattern resulting from prepayments and arrears is described. This model is to complement conventional cash flow forecast scheme in the phase of business feasibility review. Analysis result on final losses in cash that occur as a result of prepayment and arrears is considered in this model. Additionally, in the estimation of construction cost in the phase of business feasibility review, real construction prices instead of official prices are applied to enhance accuracy of cash outflow forecast. The proportion of payment made by a bill and changes in payment date caused by rescheduling of a bill are also factored in to estimate cash outflow. This model would contribute to achieving accurate cash flow forecast that better reflect real situation and to enhancing efficiency in capital management by giving a clear picture with regard to the demand and supply timing of capital.

• Journal of Korean Society of Transportation
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• v.23 no.3 s.81
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• pp.125-136
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• 2005

The current traffic accident reduction procedure in economic feasibility study does not consider the characteristics of road and V/C ratio. For solving this problem, this paper suggests methods to be able to evaluate safety of each road in construction and improvement through developing accident Prediction model in reflecting V/C ratio Per road types and traffic characters. In this paper as primary process, model is made by tke object of urban roads. Most of all, factor effecting on accident relying on road types is selected. At this point, selecting criteria chooses data obtained from road planning procedure, traffic volume, existence or non-existence of median barrier, and the number of crossing point, of connecting road. and of traffic signals. As a result of analyzing between each factor and accident. all appear to have relatives at a significant level of statistics. In this research, models are classified as 4-categorized classes according to roads and V/C ratio and each of models draws accident predicting model through Poisson regression along with verifying real situation data. The results of verifying models come out relatively satisfactory estimation against real traffic data. In this paper, traffic accident prediction is possible caused by road's physical characters by developing accident predicting model per road types resulted in V/C ratio and this result is inferred to be used on predicting accident cost when road construction and improvement are performed. Because data using this paper are limited in only province of Jeollabuk-Do, this paper has a limitation of revealing standards of all regions (nation).

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

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.5
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• pp.1392-1400
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• 1996

The need of companies shorten the design-to-manufacturing process for new products with improved quality in cost effective manner places increasing demends on engineers to simulate the performance characteristics of a design before it is built of a prototype is developed. For theses demands CAE(Computer-Aided Engineering) offers engineers not only giving confidence of their design but also eliminating potential errors due totesting prototypes in small numbers. This paper present the method to predict the fatigue life using dynamics simulation and FEA(Finite Element Analysis) for construciton equipment in the computer before building prototype. The dynamicsimulatio is to get the load-time history corresponding to the maneuvering and driving of the construction equipment. The FEA is to build a model of the structure and then analyse to define the local stress response to applied loadings using linear static analysis.

• Fire Science and Engineering
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• v.33 no.6
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• pp.20-27
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• 2019

In this study, a new function with higher accuracy for fire heat release rate prediction was developed. The 'αt²' curve, which is the major exponential function currently used for fire engineering calculations, must be improved to minimize the prediction gap that causes fire system engineering inefficiency and lower cost-effectiveness. The newly developed prediction function was designed to cover the initial fire stage that features rapid growth based on logistic function theory, which has a more logical background and graphical similarity compared to conventional exponential function methods for 'αt²'. The new function developed in this study showed apparently higher prediction accuracy over wider range of fire growth durations. With the progress of fire growth pattern studies, the results presented herein will contribute towards more effective fire protection engineering.

• Steel and Composite Structures
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• v.47 no.6
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• pp.709-728
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• 2023

Experimental and analytical studies were conducted to clarify the influencing mechanisms of the longitudinal reinforcement on performance of axially loaded Reinforced Concrete-Filled Steel Tube (R-CFST) short columns. The longitudinal reinforcement ratio was set as parameter, and 10 R-CFST specimens with five different ratios and three Concrete-Filled Steel Tube (CFST) specimens for comparison were prepared and tested. Based on the test results, the failure modes, load transfer responses, peak load, stiffness, yield to strength ratio, ductility, fracture toughness, composite efficiency and stress state of steel tube were theoretically analyzed. To further examine, analytical investigations were then performed, material model for concrete core was proposed and verified against the test, and thereafter 36 model specimens with four different wall-thickness of steel tube, coupling with nine reinforcement ratios, were simulated. Finally, considering the experimental and analytical results, the prediction equations for ultimate load bearing capacity of R-CFSTs were modified from the equations of CFSTs given in codes, and a new equation which embeds the effect of reinforcement was proposed, and equations were validated against experimental data. The results indicate that longitudinal reinforcement significantly impacts the behavior of R-CFST as steel tube does; the proposed analytical model is effective and reasonable; proper ratios of longitudinal reinforcement enable the R-CFSTs obtain better balance between the performance and the construction cost, and the range for the proper ratios is recommended between 1.0% and 3.0%, regardless of wall-thickness of steel tube; the proposed equation is recommended for more accurate and stable prediction of the strength of R-CFSTs.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.72-77
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• 2002

The development of a Pay Adjustment (PA) procedure for implementing Performance-related Specifications (PRS) is known to be a difficult task faced by most State Highway Agencies (SHAs) due to the difficulty in such areas as selecting pay factor items, modeling the relationship between stochastic variability of pay factor items and pavement performance, and determining an overall lot pay adjustment. This led to the need for an effective way of developing a scientific pay adjustment procedure by incorporating Life Cycle Cost Analysis (LCCA) embedded Monte Carlo approach. In this work, we propose a prototype system to determine a PA specifically using the data in the pavement management information systems at Wisconsin Department of Transportation (WisDOT) as an exemplary to other SHAs. It is believed that the PRS methodology demonstrated in this study can be used in real projects by incorporating the more accurate and reliable performance prediction models and LCC model.

• Structural Engineering and Mechanics
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• v.91 no.6
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• pp.583-589
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• 2024

Accurately predicting the strength of concrete is vital for ensuring the safety and durability of structures, thereby contributing to time and cost savings throughout the design and construction phases. The compressive strength of concrete is determined by various material factors, including the type of cement, composition ratios of concrete mixtures, curing time, and environmental conditions. While mix design establishes the proportions of each material for concrete, predicting strength before experimental measurement remains a challenging task. In this study, Abrams's law was chosen as a representative investigative approach to estimating concrete compressive strength. Abrams asserted that concrete compressive strength depends solely on the water-cement ratio and proposed a logarithmic linear relationship. However, Abrams's law is only applicable to concrete using cement as the sole binding material and may not be suitable for modern concrete mixtures. Therefore, this research aims to predict concrete compressive strength by applying various conventional regression analyses and machine learning methods. Six models were selected based on performance experiment data collected from various literature sources on different concrete mixtures. The models were assessed using Root Mean Squared Error (RMSE) and coefficient of determination (R²) to identify the optimal model.