Won‐Hwa Hong
Datos Biográficos
| ID | 6841209 |
|---|---|
| NOMBRE | Won‐Hwa Hong |
| NOMBRES | Won‐Hwa |
| APELLIDO | Hong |
| FIRMA | HONG W H |
| AFILIACIONES | Kyungpook National University |
| ORCID | 0000-0002-8684-5295 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 12 |
| TOTAL DE CITAS | 1 |
| TOTAL COMO AUTOR | 12 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2016 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2024 |
| ÍNDICE H | 1 |
Evacuation information methodology that combined a flooded environment and pedestrian behavioral model
Developing a Prediction Model of Demolition-Waste Generation-Rate via Principal Component Analysis
Construction and demolition waste accounts for a sizable proportion of global waste and is harmful to the environment. Its management is therefore a key challenge in the construction industry. Many researchers have utilized waste generation data for waste management, and more accurate and efficient waste management plans have recently been prepared using artificial intelligence models. Here, we developed a hybrid model to forecast the demolition-…
Method for prioritising buildings for seismic reinforcement based on prediction of earthquake-induced building collapse and evacuation routes
Seismic design and seismic reinforcement of buildings are important for reducing seismic damage. Buildings have been prioritised for seismic reinforcement from various perspectives, but approaches considering the reduction in the effective road width after building collapse and the evacuation demand for each road link have not been developed. In this study, the effective road width was calculated by estimating the width of collapsed building debr…
Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas
Owing to a rapid increase in waste, waste management has become essential, for which waste generation (WG) information has been effectively utilized. Various studies have recently focused on the development of reliable predictive models by applying artificial intelligence to the construction and prediction of WG information. In this study, research was conducted on the development of machine learning (ML) models for predicting the demolition wast…
Quantifying asbestos fibers in post-disaster situations
Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets
Recently, artificial intelligence (AI) technologies have been employed to predict construction and demolition (C&D) waste generation. However, most studies have used machine learning models with continuous data input variables, applying algorithms, such as artificial neural networks, adaptive neuro-fuzzy inference systems, support vector machines, linear regression analysis, decision trees, and genetic algorithms. Therefore, machine learning algo…
A hierarchical flood shelter location model for walking evacuation planning
Prior planning of shelters and evacuation routes is the foundation of effective and safe post flood management. In this study, a hierarchical model for emergency shelter location selection in preparation for immediate, short-term, and long-term floods was developed. To ensure the safety of evacuation routes, levels of walking evacuation risk were classified based on inundation depth and flow rate, and high-risk areas were set as barriers in the n…
Retracted
Analysis of Waste Generation Characteristics during New Apartment Construction—Considering the Construction Phase
The waste generation rate (WGR) is used to predict the generation of construction and demolition waste (C&DW) and has become a prevalent tool for efficient waste management systems. Many studies have focused on deriving the WGR, but most focused on demolition waste rather than construction waste (CW). Moreover, previous studies have used theoretical databases and thus were limited in showing changes in the generated CW during the construction per…
Exploiting IoT and big data analytics
The Effects of Data Collection Method and Monitoring of Workers’ Behavior on the Generation of Demolition Waste
The roles of both the data collection method (including proper classification) and the behavior of workers on the generation of demolition waste (DW) are important. By analyzing the effect of the data collection method used to estimate DW, and by investigating how workers' behavior can affect the total amount of DW generated during an actual demolition process, it was possible to identify strategies that could improve the prediction of DW. Theref…
Estimating the Additional Greenhouse Gas Emissions in Korea
When asbestos containing materials (ACM) must be removed from the building before demolition, additional greenhouse gas (GHG) emissions are generated. However, precedent studies have not considered the removal of ACM from the building. The present study aimed to develop a model for estimating GHG emissions created by the ACM removal processes, specifically the removal of asbestos cement slates (ACS). The second objective was to use the new model …
A hierarchical flood shelter location model for walking evacuation planning
Prior planning of shelters and evacuation routes is the foundation of effective and safe post flood management. In this study, a hierarchical model for emergency shelter location selection in preparation for immediate, short-term, and long-term floods was developed. To ensure the safety of evacuation routes, levels of walking evacuation risk were classified based on inundation depth and flow rate, and high-risk areas were set as barriers in the n…
Estimating the Additional Greenhouse Gas Emissions in Korea
When asbestos containing materials (ACM) must be removed from the building before demolition, additional greenhouse gas (GHG) emissions are generated. However, precedent studies have not considered the removal of ACM from the building. The present study aimed to develop a model for estimating GHG emissions created by the ACM removal processes, specifically the removal of asbestos cement slates (ACS). The second objective was to use the new model …
The Effects of Data Collection Method and Monitoring of Workers’ Behavior on the Generation of Demolition Waste
The roles of both the data collection method (including proper classification) and the behavior of workers on the generation of demolition waste (DW) are important. By analyzing the effect of the data collection method used to estimate DW, and by investigating how workers' behavior can affect the total amount of DW generated during an actual demolition process, it was possible to identify strategies that could improve the prediction of DW. Theref…
Exploiting IoT and big data analytics
Retracted
Analysis of Waste Generation Characteristics during New Apartment Construction—Considering the Construction Phase
The waste generation rate (WGR) is used to predict the generation of construction and demolition waste (C&DW) and has become a prevalent tool for efficient waste management systems. Many studies have focused on deriving the WGR, but most focused on demolition waste rather than construction waste (CW). Moreover, previous studies have used theoretical databases and thus were limited in showing changes in the generated CW during the construction per…
Quantifying asbestos fibers in post-disaster situations
Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets
Recently, artificial intelligence (AI) technologies have been employed to predict construction and demolition (C&D) waste generation. However, most studies have used machine learning models with continuous data input variables, applying algorithms, such as artificial neural networks, adaptive neuro-fuzzy inference systems, support vector machines, linear regression analysis, decision trees, and genetic algorithms. Therefore, machine learning algo…
A hierarchical flood shelter location model for walking evacuation planning
Prior planning of shelters and evacuation routes is the foundation of effective and safe post flood management. In this study, a hierarchical model for emergency shelter location selection in preparation for immediate, short-term, and long-term floods was developed. To ensure the safety of evacuation routes, levels of walking evacuation risk were classified based on inundation depth and flow rate, and high-risk areas were set as barriers in the n…
Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas
Owing to a rapid increase in waste, waste management has become essential, for which waste generation (WG) information has been effectively utilized. Various studies have recently focused on the development of reliable predictive models by applying artificial intelligence to the construction and prediction of WG information. In this study, research was conducted on the development of machine learning (ML) models for predicting the demolition wast…
Developing a Prediction Model of Demolition-Waste Generation-Rate via Principal Component Analysis
Construction and demolition waste accounts for a sizable proportion of global waste and is harmful to the environment. Its management is therefore a key challenge in the construction industry. Many researchers have utilized waste generation data for waste management, and more accurate and efficient waste management plans have recently been prepared using artificial intelligence models. Here, we developed a hybrid model to forecast the demolition-…
Method for prioritising buildings for seismic reinforcement based on prediction of earthquake-induced building collapse and evacuation routes
Seismic design and seismic reinforcement of buildings are important for reducing seismic damage. Buildings have been prioritised for seismic reinforcement from various perspectives, but approaches considering the reduction in the effective road width after building collapse and the evacuation demand for each road link have not been developed. In this study, the effective road width was calculated by estimating the width of collapsed building debr…
Evacuation information methodology that combined a flooded environment and pedestrian behavioral model
Engineering (11 obras) · Computer Science (8 obras) · Civil engineering (7 obras) · Environmental Science (7 obras) · Demolition (5 obras) · Recycled Aggregate Concrete Performance (5 obras) · Waste management (4 obras) · Artificial Intelligence (3 obras) · BIM and Construction Integration (3 obras) · Demolition waste (3 obras)