Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms—specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms—to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

This study was conducted to determine the effect of mathematical transformation on near infrared spectroscopy (NIRS) calibrations for the prediction of chemical composition and fermentation parameters in corn silage. Corn silage samples (n=407) were collected from cattle farms and feed companies in Korea between 2014 and 2015. Samples of silage were scanned at 1 nm intervals over the wavelength range of 680~2,500 nm. The optical data were recorded as log 1/Reflectance (log 1/R) and scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with several spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation (R2 cv) and the lowest standard error of cross validation (SECV). Results of this study revealed that the NIRS method could be used to predict chemical constituents accurately (correlation coefficient of cross validation, R2 cv, ranging from 0.77 to 0.91). The best mathematical treatment for moisture and crude protein (CP) was first-order derivatives (1, 16, 16, and 1, 4, 4), whereas the best mathematical treatment for neutral detergent fiber (NDF) and acid detergent fiber (ADF) was 2, 16, 16. The calibration models for fermentation parameters had lower predictive accuracy than chemical constituents. However, pH and lactic acids were predicted with considerable accuracy (R2 cv 0.74 to 0.77). The best mathematical treatment for them was 1, 8, 8 and 2, 16, 16, respectively. Results of this experiment demonstrate that it is possible to use NIRS method to predict the chemical composition and fermentation quality of fresh corn silages as a routine analysis method for feeding value evaluation to give advice to farmers.

본 논문은 외국거래소에 주식예탁증서(DR)을 교차상장한 국내기업들을 대상으로 해외교차상장이 증권사 애널리스트의 리서치 커버리지와 이익 예측력, 그리고 기업가치에 미치는 영향을 분석하였다. 주요 분석결과는 다음과 같다. 첫째, 국내기업의 경우 교차상장에 따라 해당기업에 대한 애널리스트 커버리지는 교차상장 이전에 비해 감소하는 것으로 나타났다. 둘째, 교차상장 이후 기업공시 등 정보환경이 나아짐에 따라 교차상장기업에 대한 애널리스트의 이익예측 정확성이 높아질 것이라는 가설과는 반대로 교차상장 이후에 이익예측 오차는 더 증가하는 것으로 나타났다. 셋째, 교차상장으로 인해 교차상장기업의 기업가치가 높아질 것이라는 가설은 지지되어, 교차상장 이전보다 교차상장 이후에 기업가치는 더 상승하였다. 넷째, 교차상장 전후를 비교하여 애널리스트들이 목표주가를 상향 내지 하향 조정할 경우의 누적초과수익률을 비교한 결과, 목표주가를 상향한 경우 교차상장 이전과 이후 주가 영향력은 차이가 없는 것으로 나타났다. 이에 대해 애널리스트들이 목표주가를 하향한 경우에는 교차상장 이후에 주가에 더욱 부정적인 영향을 미치는 것으로 나타났다. 그러므로 교차상장 기업의 경우 목표주가의 하향 등 애널리스트의 부정적인 의견 제시는 주가에 미치는 부정적인 영향이 더욱 큰 만큼, 기업실적의 관리 외에 애널리스트와의 관계를 중시할 필요가 있음을 시사한다.

In the capacity spectrum method (CSM), the peak response of an inelastic system under a given earthquake load is estimated transforming the system into the equivalent elastic one. This paper presented estimating the peak inelastic response is evaIuated by the CSM. The equivalent period and damping are calculated using the ATC-40, Gülkan, Kowalsky, and Iwan methods, and the performance points are obtained according the procedure B of ATC-40. Analysis results indicate that the ATC-40 method generaIly underestimates the peak response, while the Gülkan and Kowalsky methods overestimate the responses. The Iwan method produces the values between those by the ATC-40 method and the Gülkan and Kowalsky methods, and estimates the reponses relatively closer to the exact ones. Further, it is found that the Kowalsky method gives the negative equivalent damping ratios depending on the hardening ratios, and thereby can not be used to estimate the responses in some cases.

The assessment of wind resources must be carried out to choose wind farm sites adequately. Additionally, input data on surface roughness maps and topographic maps are required to evaluate wind resources, where input data accuracy determines the accuracy of their overall analysis. To estimate this accuracy, we used met-mast data in Jeju and produced the ground roughness value for the Jeju region. To determine these values, an unsupervised classification method using SPOT-5 images was carried out for image classification. The wind resources of the northeastern part of Jeju were predicted, and the ground roughness map of the region was calculated by the WindPRO software. The wind speed of the Pyeongdae region of Jeju from the ground roughness map was calculated using WindPRO as 8.51 m/s. The wind speed calculated using the remote sensing technology presented in this study was 8.69 m/s. To assess the accuracy of the measured WindPro and the remote sensing technology values, we compared these results to the observed values in the Pyeongdae region using met-mast. This comparison shows that remote sensing data are more accurate than the WindPro data. We also found that the ground roughness map calculated in this study is useful for generating an accurate wind resource map of Jeju Island.

목적: 본 실험의 목적은 자극의 속도와 방향이 예측타이밍 반응시에 발생하는 안구운동패턴과 반응의 시공간적 정확성, 그리고 눈과 손의 협응패턴에 미치는 영향을 분석함으로써 시공간적 정확성을 요구하는 예측타이밍 과제를 수행할 때 자극에 대한 시각적 지각과 활동이 결합되는 과정을 연구하는 것이다. 방법: 12명의 오른손잡이 남자피험자가 다양한 속도와 방향으로 이동하는 자극이 목표영역에 도달하는 시점과 일치되게 철필로 자극을 치도록 요구받았다. 피험자가 과제를 수행할 때 안구운동패턴과 반응결과를 측정하였으며, 자극의 속도와 방향에 따른 주시점 이동패턴의 차이, 동작종료시점에서의 주시점 위치와 자극 위치 사이의 간격, 그리고 주시점과 철필위치의 시공간적 관계를 분석하였다. 결과: 예측타이밍 반응의 시공간적 오차는 자극속도가 빠를수록 증가하였으며, 단속성 안구운동의 잠복기와 빈도, 그리고 주시점 고정기간은 자극속도가 느릴수록 증가하였다. 모든 자극속도 조건에서 주시점은 반응이 종료되기 전에 목표영역에 먼저 도달하였으며, 자극속도가 느릴수록 주시점과 철필의 시간적 연결은 증가하였으나, 주시점과 자극, 그리고 주시점과 철필의 공간적 간격은 감소되었다. 결론: 이러한 결과는 자극의 속도와 방향에 대한 시각적 지각에 근거를 두고 발생하는 주시점의 이동패턴이 예측타이밍 반응의 시공간적 정확성을 결정하는 과정에서 주요 기준으로 작용할 수 있으며 손의 반응과 시공간적으로 연결되어 있음을 시사한다.

Carbonation of reinforced concrete is a major factor in the deterioration of reinforced concrete, and prediction of the resistance to carbonation is important in determining the durability life of reinforced concrete structures. In this study, basic research on the prediction of carbonation penetration depth of concrete using Deep Learning algorithm among artificial neural network theory was carried out. The data used in the experiment were analyzed by deep running algorithm by setting W/B, cement and blast furnace slag, fly ash content, relative humidity of the carbonated laboratory, temperature, CO2 concentration, Deep learning algorithms were used to study 60,000 times, and the analysis of the number of hidden layers was compared.

This paper aims at developing a methodology for estimating the bridge life based on a deterioration model associated with inspection accuracy. This study used condition rating results of bridges and developed a non-linear regression model taking into account the IRI (Inspection Reliability Index).