• Computers and Concrete
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• v.31 no.2
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• pp.85-95
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• 2023

Natural pozzolans are used as additives in cement to develop more durable and high-performance concrete. Pozzolanic activity index (PAI) is important for assessing the performance of a pozzolan as a binding material and has an important effect on the compressive strength, permeability, and chemical durability of concrete mixtures. However, the determining of the 28 days (short term) and 90 days (long term) PAI of concrete mixtures is a time-consuming process. In this study, to reduce extensive experimental work, it is aimed to predict the short term and long term PAIs as a function of the chemical compositions of various natural pozzolans. For this purpose, the chemical compositions of various natural pozzolans from Central Anatolia were determined with X-ray fluorescence spectroscopy. The mortar samples were prepared with the natural pozzolans and then, the short term and the long term PAIs were calculated based on compressive strength method. The effect of the natural pozzolans' chemical compositions on the short term and the long term PAIs were evaluated and the PAIs were predicted by using multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) model. The prediction model results show that both reactive SiO₂ and SiO₂+Al₂O₃+Fe₂O₃ contents are the most effective parameters on PAI. According to the performance of prediction models determined with metrics such as root mean squared error (RMSE) and coefficient of correlation (R²), ANFIS models are more feasible than the multiple regression model in predicting the 28 days and 90 days pozzolanic activity. Estimation of PAIs based on the chemical component of natural pozzolana with high-performance prediction models is going to make an important contribution to material engineering applications in terms of selection of favorable natural pozzolana and saving time from tedious test processes.

• Computers and Concrete
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• v.34 no.2
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• pp.247-265
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• 2024

Measuring the fracture toughness of concrete in laboratory settings is challenging due to various factors, such as complex sample preparation procedures, the requirement for precise instruments, potential sample failure, and the brittleness of the samples. Therefore, there is an urgent need to develop innovative and more effective tools to overcome these limitations. Supervised learning methods offer promising solutions. This study introduces seven machine learning algorithms for predicting concrete's effective fracture toughness (K-eff). The models were trained using 560 datasets obtained from the central straight notched Brazilian disc (CSNBD) test. The concrete samples used in the experiments contained micro silica and powdered stone, which are commonly used additives in the construction industry. The study considered six input parameters that affect concrete's K-eff, including concrete type, sample diameter, sample thickness, crack length, force, and angle of initial crack. All the algorithms demonstrated high accuracy on both the training and testing datasets, with R² values ranging from 0.9456 to 0.9999 and root mean squared error (RMSE) values ranging from 0.000004 to 0.009287. After evaluating their performance, the gated recurrent unit (GRU) algorithm showed the highest predictive accuracy. The ranking of the applied models, from highest to lowest performance in predicting the K-eff of concrete, was as follows: GRU, LSTM, RNN, SFL, ELM, LSSVM, and GEP. In conclusion, it is recommended to use supervised learning models, specifically GRU, for precise estimation of concrete's K-eff. This approach allows engineers to save significant time and costs associated with the CSNBD test. This research contributes to the field by introducing a reliable tool for accurately predicting the K-eff of concrete, enabling efficient decision-making in various engineering applications.

• International Journal of Highway Engineering
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• v.18 no.6
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• pp.123-130
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• 2016

OBJECTIVES : The objective of this research is to determine the moisture resistance of the freeze-thaw process occurring in low-noise porous pavement using either hydrated-lime or anti-freezing agent. Various additives were applied to low-noise porous asphalt, which is actively paved in South Korea, to overcome its disadvantages. Moreover, the optimum contents of hydrated-lime and anti-freezing agent and behavior properties of low-noise porous asphalt layer are determined using dynamic moduli via the freeze-thaw test. METHODS : The low-noise porous asphalt mixtures were made using gyratory compacters to investigate its properties with either hydrated-lime or anti-freezing agent. To determine the dynamic moduli of each mixture, impact resonance test was conducted. The applied standard for the freeze-thaw test of asphalt mixture is ASTM D 6857. The freeze-thaw and impact resonance tests were performed twice at each stage. The behavior properties were defined using finite element method, which was performed using the dynamic modulus data obtained from the freeze-thaw test and resonance frequencies obtained from non-destructive impact test. RESULTS : The results show that the coherence and strength of the low-noise porous asphalt mixture decreased continuously with the increase in the temperature of the mixture. The dynamic modulus of the normal low-noise porous asphalt mixture dramatically decreased after one cycle of freezing and thawing stages, which is more than that of other mixtures containing additives. The damage rate was higher when the freeze-thaw test was repeated. CONCLUSIONS : From the root mean squared error (RMSE) and mean percentage error (MPE) analyses, the addition rates of 1.5% hydrated-lime and 0.5% anti-freezing agent resulted in the strongest mixture having the highest moisture resistance compared to other specimens with each additive in 1 cycle freeze-thaw test. Moreover, the freeze-thaw resistance significantly improved when a hydrated-lime content of 0.5% was applied for the two cycles of the freeze-thaw test. Hence, the optimum contents of both hydrated-lime and anti-freezing agent are 0.5%.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.291-295
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• 2007

강수진단모형을 이용하여 저수지 이수운영을 위한 실시간 유량예측기법을 개발하였다. 강수진단모형은 현재 기상청 현업에서 수행중인 강우수치예보를 기반으로 상세 지역의 지형 효과에 의한 강수를 예측하는 정량강수예측모형(QPM; Quantitative Precipitation Model)으로서 부경대학교 환경대기과학과에서 개발된 모형이다. QPM은 중규모 예측 모형으로부터 계산된 수평 바람, 고도, 기온, 강우 강도, 그리고 상대습도 등의 예측 자료를 이용하고, 소규모 상세지형 효과를 고려함으로써 중규모 예측 모형에서 생산된 강수량 예측 값을 상세 지역의 지형을 고려한 강수량 예측 값으로 재구성하여 결과적으로 3km 간격의 상세지역 강우산출과 지형에 따른 강수량의 분포 파악이 용이할 뿐만 아니라 계산 효율성을 개선된 모형이다. QPM 검증을 위하여 기상학적 평가와 수문학적 평가를 수행하였다. 호우 사례별 일강수량의 시공간 분포로 부터, QPM을 활용한 시스템에 의한 예측결과가 원시자료 RDAPS 보다 고해상도의 예측 및 지형효과의 반영도가 높았으며, AWS의 관측자료와 비교하여 보다 높은 예측성을 보여 주었다. 대상기간인 2006년 1월 1일부터 6월 20일까지 관측강우는 총 391.5mm 였으며 RQPM은 실적강우에 비하여 119.5mm 정도 과소산정하고 있으나 분위사상과정을 거치게 되면 351.7mm로서 실적강우에 불과 10.2% 못미치고 있다. 이는 고무적인 결과로 볼 수 있으며 현업에서의 활용성이 기대되는 수준이라 볼 수 있다. 강우-유출모의를 위한 QPM신뢰도를 높이기 위하여 분위사상법(Quantile Mapping)을 이용하여 QPM모의에 존재할 수 있는 계통오차에 대한 추가적인 보정을 수행하였다. 수문학적 평가를 위하여는 장기연속유출모형인 SSARR모형을 기반으로 개발된 RRFS(Rainfall-Runoff Forecast System)을 이용하여 2006년 1월${\sim}$9월까지의 용담댐 유입량에 대하여 모의예측결과와 관측유입량 비교를 통한 검증을 수행하였다. 위 기간중 예측유입량의 RMSE(Root Mean Squared Error), COE(Sutcliffe Coefficient of Efficiency), MAE(Mean Absolute Error), $R^2$값은 각각 7.50, 0.68, 2.59, 0.69 값을 보이고 있다. 본 연구에서는 QPM에 의한 예측성의 향상 및 구축된 시스템에 의한 일강수량의 장기예측 가능성을 확인하였고, 향후 시스템을 현업에 활용하기 위해서 생산된 예측자료의 보다 장기적인 검증을 통한 시스템의 안정화가 필요할 것으로 사료된다.

• Economic and Environmental Geology
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• v.55 no.4
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• pp.353-366
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• 2022

Spatial estimation of geoscience data (geo-data) is challenging due to spatial heterogeneity, data scarcity, and high dimensionality. A novel spatial estimation method is needed to consider the characteristics of geo-data. In this study, we proposed the application of Gaussian Mixture Model (GMM) among machine learning algorithms with multivariate data for robust spatial predictions. The performance of the proposed approach was tested through soil chemical concentration data from a former smelting area. The concentrations of As and Pb determined by ex-situ ICP-AES were the primary variables to be interpolated, while the other metal concentrations by ICP-AES and all data determined by in-situ portable X-ray fluorescence (PXRF) were used as auxiliary variables in GMM and ordinary cokriging (OCK). Among the multidimensional auxiliary variables, important variables were selected using a variable selection method based on the random forest. The results of GMM with important multivariate auxiliary data decreased the root mean-squared error (RMSE) down to 0.11 for As and 0.33 for Pb and increased the correlations (r) up to 0.31 for As and 0.46 for Pb compared to those from ordinary kriging and OCK using univariate or bivariate data. The use of GMM improved the performance of spatial interpretation of anthropogenic metals in soil. The multivariate spatial approach can be applied to understand complex and heterogeneous geological and geochemical features.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4B
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• pp.363-374
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• 2008

Intensity-Duration-Frequency (IDF) curve that is essential to calculate rainfall quantiles for designing hydraulic structures in Korea is generally formulated by regression analysis. In this study, IDF curve derived by the cumulative distribution function ("IDF by CDF") of the proper probability distribution function (PDF) of each site is suggested, and the corresponding parameters of IDF curve are computed using genetic algorithm (GA). For this purpose, IDF by CDF and the conventional IDF derived by regression analysis ("IDF by REG") were computed for 22 Korea Meteorological Administration (KMA) rainfall recording sites. Comparisons of RMSE (root mean squared error) and RRMSE (Relative RMSE) of rainfall intensities computed from IDF by CDF and IDF by REG show that IDF by CDF is more accurate than IDF by REG. In order to accommodate the effect of the recent intensive rainfall of Korea, the rainfall intensities computed by the two IDF curves are compared with that by at-site frequency analysis using the rainfall data recorded by 2006, and the result from IDF by CDF show the better performance than that from IDF by REG. As a result, it can be said that the suggested IDF by CDF curve would be the more efficient IDF curve than that computed by regression analysis and could be applied for Korean rainfall data.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Journal of Korea Water Resources Association
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• v.51 no.11
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• pp.1021-1029
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• 2018

A primary objective of this study is to develop a drought forecasting technique based on groundwater which can be exploit for water supply under drought stress. For this purpose, we explored the lagged relationships between regionalized SGI (standardized groundwater level index) and SPI (standardized precipitation index) in view of the drought propagation. A regional prediction model was constructed using a NARX (nonlinear autoregressive exogenous) artificial neural network model which can effectively capture nonlinear relationships with the lagged independent variable. During the training phase, model performance in terms of correlation coefficient was found to be satisfactory with the correlation coefficient over 0.7. Moreover, the model performance was described by root mean squared error (RMSE). It can be concluded that the proposed approach is able to provide a reliable SGI forecasts along with rainfall forecasts provided by the Korea Meteorological Administration.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.129-134
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• 2018

Recently, the artificial neural network (ANN) model is a promising technique in the prediction, numerical control, robot control and pattern recognition. We predicted the outside temperature of greenhouse using ANN and utilized the model in greenhouse control. The performance of ANN model was evaluated and compared with multiple regression model(MRM) and support vector machine (SVM) model. The 10-fold cross validation was used as the evaluation method. In order to improve the prediction performance, the data reduction was performed by correlation analysis and new factor were extracted from measured data to improve the reliability of training data. The backpropagation algorithm was used for constructing ANN, multiple regression model was constructed by M5 method. And SVM model was constructed by epsilon-SVM method. As the result showed that the RMSE (Root Mean Squared Error) value of ANN, MRM and SVM were 0.9256, 1.8503 and 7.5521 respectively. In addition, by applying the prediction model to greenhouse heating load calculation, it can increase the income by reducing the energy cost in the greenhouse. The heating load of the experimented greenhouse was 3326.4kcal/h and the fuel consumption was estimated to be 453.8L as the total heating time is $10000^{\circ}C/h$. Therefore, data mining technology of ANN can be applied to various agricultural fields such as precise greenhouse control, cultivation techniques, and harvest prediction, thereby contributing to the development of smart agriculture.

• Land and Housing Review
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• v.12 no.3
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• pp.109-119
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• 2021

Global warming caused by greenhouse gas emissions has rapidly increased abnormal climate events and geotechnical engineering hazards in terms of their size and frequency accordingly. Biopolymer-based soil treatment (BPST) in geotechnical engineering has been implemented in recent years as an alternative to reducing carbon footprint. Furthermore, thermo-gelating biopolymers, including agar gum, gellan gum, and xanthan gum, are known to strengthen soils noticeably. However, an explicitly detailed evaluation of the correlation between the factors, that have a significant influence on the strengthening behavior of BPST, has not been explored yet. In this study, machine learning regression analysis was performed using the UCS (unconfined compressive strength) data for BPST tested in the laboratory to evaluate the factors influencing the strengthening behavior of gellan gum-treated soil mixtures. General linear regression, Ridge, and Lasso were used as linear regression methods; the key factors influencing the behavior of BPST were determined by RMSE (root mean squared error) and regression coefficient values. The results of the analysis showed that the concentration of biopolymer and the content of clay have the most significant influence on the strength of BPST.