• Computers and Concrete
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• v.16 no.5
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• pp.741-757
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• 2015

In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.

• Advances in concrete construction
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• v.7 no.4
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• pp.211-218
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• 2019

Predictions about shrinkage and creep of concrete are very important for evaluating time-dependent effects on structural performance. Some prediction models and formulas of concrete shrinkage and creep have been proposed with diversity. However, the influence of reinforcement ratio on shrinkage and creep of concrete has been ignored in most prediction models and formulas. In this paper, the concrete shrinkage and creep with different ratios of reinforcement were studied. Firstly, the shrinkage performance was tested by the 10 reinforced concrete beams specimens with different reinforcement ratios for 200 days. Meanwhile, the creep performance was tested by the 5 reinforced concrete beams specimens with different ratios of reinforcement under sustained load for 200 days. Then, the test results were compared with the prediction models and formulas of CEB-FIP 90, ACI 209, GL 2000 and JTG D 62-2004. At last, based on ACI 209, an improved prediction models and formulas of concrete shrinkage and creep considering reinforcement ratio was derived. The results from improved prediction models and formulas of concrete shrinkage and creep are in good agreement with the experimental results.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.1-12
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• 2016

In this paper, we propose an approach that efficiently builds regional hazardous weather prediction models based on past weather data. Doing so requires finding the proper weather attributes that strongly affect hazardous weather for each region, and that requires a large number of experiments to build and test models with different attribute combinations for each kind of hazardous weather in each region. Using our proposed method, we reduce the number of experiments needed to find the correct weather attributes. Compared to the traditional method, our method decreases the number of experiments by about 45%, and the average prediction accuracy for all hazardous weather conditions and regions is 79.61%, which can help forecasters predict hazardous weather. The Korea Meteorological Administration currently uses the prediction models given in this paper.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.161-166
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• 2012

This Study, as part of a study on the application plan of overseas noise prediction models suitable for making domestic noise maps, analyzed the correlation between the differences in predicted noise levels by individual noise prediction model and surveyed data on General roads and Expressways. Separation distances of 5m and 10m, respectively were set from the ends of the general roads and the expressways at the points of measurements and to check the distribution patterns of sound power levels, the levels were measured at the heights of 1.5m and 3m, respectively. The latest revised versions of the five models (CRTN, RLS90, NMPB, Nord2000, ASJ2008) suggested in The Method of making Noise Maps were used as prediction models, and predicted noise levels were calculated by using commercial software SoundPLAN (Ver 7.1).

• Journal of Satellite, Information and Communications
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• v.12 no.4
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• pp.34-41
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• 2017

In this paper, the I/O parameters of existing predictive models were analyzed to construct a composite disaster prediction model that incorporates a previously developed natural disaster prediction model and a prediction of social disaster prediction models. A complex disaster prediction model indicates a combination of multiple disasters, not a single disaster. Such a complex disaster was mainly linked to a social disaster caused by natural disasters resulting from natural disasters, so it conducted a study of natural disasters and social disaster prediction models. Several estimates were analyzed based on several predictive models of prediction models, and the I/O parameters applied universally were derived by the types of disaster types. In this paper, It will help develop a study aimed at building a complex disaster prediction model.

• International Journal of Concrete Structures and Materials
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• v.3 no.1
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• pp.57-61
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• 2009

The present work deals with the prediction, through models and experimental evaluation, of the bending strength of roller compacted concrete (RCC) for pavement applications. This concrete was manufactured using low cement proportioning (150 to $250\;kg/m^3$). The characterization of hardened RCC was carried out by experimental measurements of bending strengths. The predictions of these characteristics were achieved using empirical models. Comparison, of the values found in experiments with those empirically obtained, was made in order to choose and to propose the adapted and the most reliable models of prediction. The study showed that the bending strengths of the RCC mixture, experimentally found, can be also identified by models.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2075-2083
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• 2016

The real time IAQ (Indoor Air Quality) management is very important for large buildings and underground facilities such as subways because poor IAQ is immediately harmful to human health. Such IAQ management requires monitoring, prediction and control in an integrated and real time manner. In this paper, we present three PM10 hourly prediction models for such realtime IAQ management as both Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models. Both MLR and ANN models show good performances between 0.76 and 0.88 with respect to R (correlation coefficient) between the measured and predicted values, but the MLR models outperform the corresponding ANN models with respect to RMSE (root mean square error).

• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.39-57
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• 2021

Time series prediction is an area of great interest to many people. Algorithms for time series prediction are widely used in many fields such as stock price, temperature, energy and weather forecast; in addtion, classical models as well as recurrent neural networks (RNNs) have been actively developed. After introducing the attention mechanism to neural network models, many new models with improved performance have been developed; in addition, models using attention twice have also recently been proposed, resulting in further performance improvements. In this paper, we consider time series prediction by introducing attention twice to an RNN model. The proposed model is a method that introduces H-attention and T-attention for output value and time step information to select useful information. We conduct experiments on stock price, temperature and energy data and confirm that the proposed model outperforms existing models.

• Advances in concrete construction
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• v.10 no.6
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• pp.463-478
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• 2020

Multiple mathematical modeling for prediction of slump, compressive strength and depth of water penetration at 28 days were performed using statistical analysis for the concrete containing waste limestone powder as partial replacement of sand obtained from experimental program reported in this research. To extract experimental data, 180 concrete cubic samples with 20 different mix designs were investigated. The twenty non-linear regression models were used to predict each of the concrete properties including slump, compressive strength and water depth penetration of concrete with waste limestone powder. Evaluation of the models using numerical methods showed that the majority of models give acceptable prediction with a high accuracy and trivial error rates. The 15-term regression models for predicting the slump, compressive strength and water depth were found to have the best agreement with the tested concrete specimens.

• Journal of Sensor Science and Technology
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• v.32 no.3
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• pp.164-168
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• 2023

Accurate and reliable weather forecasts for temperature, relative humidity, and precipitation using advanced transformer models and IoT are essential in various fields related to global climate change. We propose a novel weather prediction device that integrates state-of-the-art transformer models and IoT techniques to improve prediction accuracy and real-time processing. The proposed system demonstrated high reliability and performance, offering valuable insights for industries and sectors that rely on accurate weather information, including agriculture, transportation, and emergency response planning. The integration of transformer models with the IoT signifies a substantial advancement in weather and climate modeling.