• The Plant Pathology Journal
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• v.38 no.4
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• pp.395-402
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• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

• Structural Engineering and Mechanics
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• v.92 no.2
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• pp.207-226
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• 2024

Recently, the phenomenon of disproportionate structural failure caused by blast load has grown more common in the field of engineering design. Blast-resistant analyses and designs have been developed by many structural techniques and methodologies to forecast the loads produced by a high explosive charge on structures with complicated geometry. These techniques are based on a good understanding of blast phenomena to analyze structures exposed to blast load. This paper provides a current state-of-the-art review of blast prediction and simulation methods to predict the design blast loads that are used to assess the structural response and damage level to an existing or new building. The damage criteria from the general design approach relevant to civil design applications in forecasting blast loads as well as structural system responses will be provided. Identifying the structures' expected damage class would aid in providing extra reinforcing or strengthening for damaged elements to meet the acceptance criteria or minimize damage by a suitable blast mitigation strategy. Based on identifying the damage class expected of a structure subjected to an explosion, blast mitigation strategies could be used to minimize damage and maximize the ability of the structure to function even after the explosion.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.11a
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• pp.107-108
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• 2012

The study on the strength prediction using Maturity is mainly focused on, but the study on the concrete mixing blast furnace slag powder is insufficient. The purpose of this study is to investigate the relationships between compressive strength and equivalent age by Maturity function and is to compare and examine the strength prediction of concrete mixing Blast Furnace Slag Power using ACI and Logistic Curve prediction equation. So it is intended that fundamental data are presented for quality management and process management of concrete mixing Blast Furnace Slag Power in the construction field.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.357.2-357
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• 2002

The blast vibration can generate occupants dissatisfy as well as damage of physics nearby building. Then blast vibration estimation issue important problems. But, now blast vibration prediction inside-outside country not established objective method according to blast number to express magnitude of vibration. In this study, Our propoes show our country problem of blast vibration about blast vibration measurement and this problems be able to find improve method. (omitted)

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.05a
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• pp.251-252
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• 2012

The exiting studies on the strength prediction by maturity method is mainly focused on concrete using OPC, meanwhile the study on the concrete mixing blast furnace slag powder (BFSP) is insufficient. The purpose of this study is to investigate the relationships between compressive strength and equivalent age by existing Maturity functions, i.e., Nurse-saul function Arrhenius function. This study also compared and examined the strength prediction of concrete mixing BGSP using ACI model and Logistic Curve prediction equation. Therefore, it is intended that fundamental data are presented for quality management and process management of concrete mixing BFSP.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2013.05a
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• pp.89-90
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• 2013

This study is to predict the compressive strength for the concrete of ground granulated blast-furnace slag, and use Plowman's, Gompertz's model. The results are as follows; The prediction compressive strength were simiar using Rastrup's equivalent age model. but The prediction compressive strength using Freiesleben's equivalent age model weren't simiar in bfs replacement Ratio of 50%, because it is analyzed as the activation energy.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.101-102
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• 2021

The compressive strength of concrete is greatly affected by the temperature inside the concrete at the initial age immediately after pouring. The apparent activation energy of cement and the setting time of concrete are major factors influencing the development of compressive strength of concrete. This study measured the apparent activation energy and setting time according to the change in W/B for each mixing rate of Ground Granulated Blast-Furnace Slag (GGBFS). And after calculating the compressive strength prediction model, the accuracy of the prediction model was evaluated by comparing the predicted compressive strength and the compressive strength.

• Tunnel and Underground Space
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• v.24 no.2
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• pp.131-142
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• 2014

This study is using electronic-delay system detonator which can input an accurate detonating delay, compare predicted blasting vibration level derived from vibration 3D modelling with real measured blasting vibrations, and then considered modelling results are able to apply blast design. It confirmed there are certain relations between modelling and real vibration data, so modelling prediction method also can be apply design various blast conditions and prediction equation of blast vibration.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.553-558
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• 2002

The blast vibration can generate occupants dissatisfy as well as damage of physics nearby building. Then blast vibration estimation issue important problems. But, now blast vibration prediction inside-outside country not established objective method to express magnitude of vibration according to blast number. In this study, Our propose show our country problem of blast vibration about blast vibration measurement and this problems be able to find improve method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.3
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• pp.573-582
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• 1994

Blast vibration equations proposed previously are investigated. Special attention is given to the blast vibration equation which shows the best fitting to the geologic condition of Korea. The fittness of proposed blast vibration equation is analyzed and examined using many field data measured in Korea. The prediction of blast vibration equation using field data was performed by linear regression analysis. Moreover, after the prediction of each blast vibration equation, vibration velocity is recalculated on the basis of scaled distance at each equation. Reliability of regressioned blast vibration equation is observed by comparing predicted and measured velocity, which is divided into small-scale blasting of city and large-scale blasting of quarry. Based on this study, the best fitting equation to the Korean geologic condition is ROOT SCALING & CUBE ROOT SCALING proposed by USBM(United Nations Bureau of Mines). Also representative blast vibration equations depending on the different kinds of rock mass are proposed using measured and existing field data.