• 대기
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• 제33권4호
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• pp.355-365
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• 2023

To proactively manage climate risk, near-term climate predictions on annual to decadal time scales are of great interest to various communities. This study evaluates the near-term climate prediction skills in East Asia with DePreSys4 retrospective decadal predictions. The model is initialized every November from 1960 to 2020, consisting of 61 initializations with ten ensemble members. The prediction skill is quantitatively evaluated using the deterministic and probabilistic metrics, particularly for annual mean near-surface temperature, land precipitation, and sea level pressure. The near-term climate predictions for May~September and November~March averages over the five years are also assessed. DePreSys4 successfully predicts the annual mean and the five-year mean near-surface temperatures in East Asia, as the long-term trend sourced from external radiative forcing is well reproduced. However, land precipitation predictions are statistically significant only in very limited sporadic regions. The sea level pressure predictions also show statistically significant skills only over the ocean due to the failure of predicting a long-term trend over the land.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2008년도 추계학술대회 논문집
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• pp.278-281
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• 2008

In these days the installation of wind turbines or wind parks includes a high financial risk. So for the planning and the constructing of wind farms, long-term data of wind speed and wind direction is required. However, in most cases only few data are available at the designated places. Traditional Measure-Correlate-Predict (MCP) can extend this data by using data of nearby meteorological stations. But also Neural Networks can create such long-term predictions. The key issue of this paper is to demonstrate the possibility and the quality of predictions using Neural Networks. Thereto this paper compares the results of different MCP Models and Neural Networks for creating long-term data with various indexes.

• Computers and Concrete
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• 제30권5호
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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.

• 동력기계공학회지
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• 제13권2호
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• pp.63-70
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• 2009

This paper discusses the influence on long-tenn predictions of the ship response in ocean by using the Global Wave Statistics data, GWS, and wave information from the remote sensing satellites. GWS's standard scatter diagrams of significant wave height and zero-crossing wave period are suggested to be corrected to a round number of 0.01/1000 fitted with a statistical analytic model of the conditional lognormal distribution for zero-crossing wave period. The GEOSAT satellite data are utilized which presented by I. R. Young and G. J. Holland (1996, named as GEOSAT data). At first, qualities of this data are investigated, and statistical characteristic trends are studied by means of applying known probability distribution functions. The wave height data of GEOSAT are compared to the data observed onboard merchant ships, the data observed by measure instrument installed on the ocean-going container ship and so on. To execute a long-tenn prediction of ship response, joint probability functions between wave height and wave period are introduced, therefore long-term statistical predictions are executed by using the functions.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2006년도 추계 학술발표회 논문집
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• pp.1-12
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• 2006

For advanced civil engineering structures a service life of hundred up to hundred fifty and even two hundred years is sometimes required. The prediction of the performance of concrete structures over such a long period requires accurate and reliable predictive models. Most of the presently used, mostly experience based models don't have the quality and reliability that is required for reliable long-term predictions. The models designers are searching for should be based on an accurate description of the relevant degradation mechanisms. The starting point of such models is a realistic description of the microstructure of the concrete. In this presentation the need and the role of fundamental microstructural models for predicting the performance of concrete structures will be presented. An example will be given of a microstructural model with a proven potential for long-term predictions. Besides this also the role of models in general, i.e. in the whole design and execution process of concrete structures, will be dealt with. Finally recent trends in concrete research will be presented, like the research on self-healing cement-bases systems.

• Journal of the Korean Wood Science and Technology
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• 제17권4호
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• pp.35-43
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• 1989

Nailed joints, which are commonly used in Wooden structures, transmit loads from one member to another and induce partial composite actions between members. Long-term loads induce creep slip in nailed joints and affect load sharing and partial composite action, which may reduce joint stiffness. Two theoretical viscous-viscoelastic models were developed for nailed joints to predict creep behavior under long-term variable loads. Those models were also used to predict stiffness changes under long-term variable loads. The stiffness of nailed joint is defined as a Secant modulus which is called the joint modulus or slip modulus. Input data for the models are the results of constant load tests under three different load levels. To verify the models, nailed joints were also tested under two long-term variable load functions. The predictions of the models were very close to the experimental data. Therefore, the theoretical viscous-viscoelastic models and procedures developed in this study can be applied to predict creep slip and the changes in joint moduli of nailed joints under long-term variable loads.

• 환경영향평가
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• 제6권2호
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• pp.81-92
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• 1997

This paper presents the results from the comparison analysis and evaluation between the air pollutant dispersion modeling results and the observation data in the area within a 10 km radius from the Boryong thermal power plants. The observation data used in this study were the air pollutant concentrations which had been continuously measured from 8 locations around the Boryong power plants by TMS(tele-monitoring system) for 3 months from September to November, 1996. The short-term and long-term predictions were carried out using ISC3 model and LPDM(Lagrangian Panicle Dispersion Model). The results of ISC3 modeling in a short-term showed highly as 0.7 in a correlation coefficient, but in a long-term showed just 0.54. On the other hand, LPDM showed 0.78 in a correlation coefficient for a long-term, but in a short-term showed highly value than the observation concentrations.

• Computers and Concrete
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• 제33권4호
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• pp.445-469
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• 2024

The Federal Highway Administration (FHWA) has recommended the use of AASHTOWare Pavement Mechanistic-Empirical Design (PMED) software for Roller-Compacted Concrete (RCC) pavement design, but specific calibration for RCC is missing. This study investigates the software's capacity to predict the long-term performance of RCC roadways within the framework of conventional concrete pavement calibration. By reanalyzing existing RCC projects in several U.S. states: Colorado, Arkansas, South Carolina, Texas, and Illinois, the study highlights the need for specific calibration tailored to the unique characteristics of RCC. Field observations have emphasized occurrence of early distresses in RCC pavements, particularly transverse-cracking and joint-related issues. Despite data challenges, the AASHTOWare PMED software exhibits notable correlation between its long-term predictions and actual field performance in RCC roadways. This study stresses that RCC applications with insufficient joint spacing and thickness are prone to premature cracking. To enhance the accuracy of RCC pavement design, it is essential to discuss the inclusion of RCC as a dedicated rigid pavement option in AASHTOWare PMED. This becomes particularly crucial when the rising popularity of RCC roadways in the U.S. and Canada is considered. Such an inclusion would solidify RCC as a viable third option alongside Jointed Plain Concrete Pavements (JPCP) and Continuously Reinforced Concrete Pavements (CRCP) for design and deployment of rigid pavements. The research presents a roadmap for future calibration endeavors and advocates for the integration of RCC pavement as a distinct pavement type within the software. This approach holds promise for achieving more precise RCC pavement design and performance predictions.

• Management Science and Financial Engineering
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• 제17권2호
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• pp.85-97
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• 2011

In this study we examine the long term behavior of stock returns. The analysis reveals that negative autocorrelations of the returns exist for a super-long horizon as long as 10 years. This pattern, however, contrasts to predictions of previous stock price models which include random walks. We suggest the introduction of a fractionally integrated process into a nonstationary component of stock prices, and demonstrate empirically the existence of the process in NYSE stock returns. The predicted values of autocorrelation from our stock price model confirm the super-long term behavior of the returns observed in regression, indicating that inefficiency in the stock market could remain for a long time.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1998년도 가을 학술발표논문집(II)
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• pp.462-467
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• 1998

A rational method for prediction of long-term deflections of reinforced concrete beams under sustained loads was proposed. Strain and stress distributions of uncracked and fully cracked sections after creep and shrinkage were determined from the requirements of strain compatibility and force equilibrium of a section, and then long-term deflections were calculated from the section analysis results. In fully cracked section analysis, noncoincidence of the neutral axis of strain and the neutral axis of stress after creep and shrinkage was taken into account. The accuracy of the proposed method was verified by comparison with several experimental measurements of beam deflections. The proposed approximate procedure gave the better predictions than the existing approximate methods. At the same time, the proposed method also retained simplicity of the calculation, since maximum long-term deflection could be obtained without tedious integration of the curvatures.