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홍승호 연구실
한양대학교 건설환경공학과 홍승호 교수
River scour
Levee failure
Hydraulic safety
기본 정보
연구 분야
프로젝트
논문
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홍승호 연구실

한양대학교 건설환경공학과 홍승호 교수

홍승호 연구실은 하천과 도시 인프라에서 발생하는 침수 및 세굴의 수리·환경 안전성을 중심으로 연구를 수행합니다. 유동과 지반의 상호작용을 규명하기 위해 플룸 실험과 이론 모델을 결합하여 임계 전단응력, 세굴깊이, 침수 수심을 분석합니다. 또한 교량 주변 지형 및 파형과 같은 시계열 예측에는 머신러닝·딥러닝 모델을 적용합니다. 일부 연구에서는 농업부산물을 활용한 친환경 나노흡착제로 수계 중금속 제거 성능을 평가합니다.

River scourLevee failureHydraulic safetyDeep learning forecastingAir quality forecasting
대표 연구 분야
연구 영역 전체보기
하천·제방·교량 세굴 및 침수 수리안전성 평가 연구 thumbnail
하천·제방·교량 세굴 및 침수 수리안전성 평가 연구
Hydraulic safety assessment of river, levee, and bridge scour and inundation
연구 분야 상세보기
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.

5개년 연도별 논문 게재 수

12총합

5개년 연도별 피인용 수

166총합
주요 논문
5
논문 전체보기
1
article
|
·
인용수 1
·
2025
A numerical study of nonlinear periodic waves generated by bottom wavemakers using a high-order spectral method and a linear analytical solution
Jae-Sang Jung, Jun-Whan Lee, Seung-Ho Hong
IF 4.3 (2025)
Physics of Fluids
This study numerically investigates characteristics of nonlinear periodic waves generated by a moving bottom wavemaker. A high-order spectral (HOS) method incorporating the moving bottom boundary condition was adopted for simulations of waves initiated by triangular- and rectangular-shaped bottom wavemakers in both two- and three-dimensional settings, and a linear analytical solution was also derived. Results indicate that larger oscillation amplitudes intensify nonlinear effects, producing sharper crests, flatter troughs, and significantly higher harmonics. In three dimensions, snake-type oscillations successfully reproduced obliquely propagating waves. Shadow zones formed perpendicular to the propagation direction, accompanied by diffraction-induced energy transfer and oscillations along crests and troughs. The HOS model closely matched the analytical solution at weak forcing, but under strong nonlinearity, it captured harmonic generation and waveform deformation beyond the linear prediction. This work extends the application of bottom wavemakers from impulsive tsunami-type motions to periodic oscillations, providing new theoretical insights into nonlinear wave dynamics.
https://doi.org/10.1063/5.0300573
Nonlinear system
Amplitude
Oscillation (cell signaling)
Waveform
Spectral method
Perpendicular
Boundary value problem
Wave propagation
Work (physics)
2
article
|
·
인용수 3
·
2024
Deep learning models for air quality forecasting based on spatiotemporal characteristics of data
Khawar Rehman, Irfan Abid, Seung-Ho Hong
IF 4.3 (2024)
Physics of Fluids
The distribution of air-borne pollutants is governed by complex fluid dynamics processes involving convection and diffusion. The process is further affected by the characteristics of emission sources, meteorological parameters, socioeconomic factors, and land use patterns. Compared to deterministic and probabilistic air quality forecasting methods, data driven modeling of air quality parameters can address the large degree of freedom in air quality influencing parameters as well as offer interpretability and understanding of air pollutants' distribution at an increased spatial and temporal resolutions. This study focuses on the citywide prediction of air quality index (AQI) based on observations of pollutant concentrations, meteorological parameters, and spatiotemporal data. The study area includes Ansan city in South Korea, which has been observed as a hotspot for high concentrations of particulate matter. The air quality and meteorological were collected from 16 monitoring stations located in Ansan city. A detailed spatiotemporal analysis was performed to investigate the correlation between AQI records at the air quality monitoring stations. Based on strong spatiotemporal correlations observed between stations, several deep learning (DL) models were proposed, and their performance was investigated for different scenarios. It was observed that the selection of appropriate DL models should be based on (1) understanding of the underlying fluid dynamics process that control pollutant distribution and (2) spatiotemporal characteristics of data. Additionally, the complexity of DL models does not always guarantee the accuracy of the forecasts, and simple models can give good performance if the predictors are selected carefully to reflect the underlying physical process.
https://doi.org/10.1063/5.0207834
Air quality index
Interpretability
Environmental science
Meteorology
Data assimilation
Computer science
Machine learning
Geography
3
article
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·
인용수 8
·
2023
Tsunami waveform forecasting at cooling water intakes of nuclear reactors with deep learning model
Byung-Ho Kim, Khawar Rehman, Yong-Sik Cho, Seung-Ho Hong
IF 4.1 (2023)
Physics of Fluids
The Fukushima nuclear disaster highlights the importance of accurate and fast predictions of tsunami hazard to critical coastal infrastructure to devise mitigation strategies in both long-term and real-time events. Recently, deep learning models allowed us to make accurate and rapid forecasts on high dimensional, non-linear, and non-stationary time series data such as that associated with tsunami waveforms. Thus, this study uses a one-dimensional convolutional neural network (CNN) model to predict waveforms at cooling water intakes of nuclear power plant at Uljin in South Korea. The site is particularly vulnerable to tsunamis originating from the west coast of Japan. Data for the CNN model are generated by numerical simulation of 1107 cases of tsunami propagation initiating from fault locations. The time series data for waveforms were predicted at 13 virtual gauges located in the nearshore region of the study area, 10 of which were classified as observation points and 3 gauges situated at the cooling water intakes were categorized as target locations. The performance assessment of the model's forecasts showed excellent results with rapid predictions. The study highlights two main points: (i) deep learning models can be based on sparse waveform in situ data (such as that recorded by deep-ocean assessment and reporting of tsunamis or any locally operating monitoring stations for ocean waves) or numerically simulated data at only a few points along the dominant wave propagation direction, and (ii) deep learning models are fully capable of accurate and fast predictions of complex geo-hazards that prompt rapid emergency response to coordinate mitigation efforts.
https://doi.org/10.1063/5.0156882
Waveform
Meteorology
Convolutional neural network
Deep learning
Deep water
Seismology
Time series
Environmental science
Computer science
Machine learning
최신 정부 과제
4
과제 전체보기
1
2024년 3월-2028년 12월
|1,725,974,000
도시침수대응 지하 인프라 유지관리 고도화 기술 개발
대규모 빗물 처리 지하 인프라 시설 통합유지관리 시스템 실증 및 적용성 검증[배수, 저류 등 성능 기존 대비 100% 확보 및 유지관리 비용 30% 절감] ○ 구조적 상태 모니터링 및 진단 시스템 개발 ○ 안전환경 유지관리 시스템 개발 ○ 자산관리 솔루션 개발 및 필드데이터 기반 통합유지관리 시스템 개발 ○ 대규모 빗물 처리 지하 인프라 시설(신월 대심도...
통합 유지관리 시스템
도시침수
홍수방어
대심도 빗물터널
자산관리
2
2024년 3월-2027년 12월
|2,049,000,000
수리실험 기반 하천의 수리·환경적 안전성 향상 기술 개발
■ 수리실험 기반 하천의 수리·환경적 안전성 향상 기술 개발
수리
환경
보/낙차공
교량
친수시설
3
2024년 3월-2028년 12월
|1,386,392,000
도시침수대응 지하 인프라 유지관리 고도화 기술 개발
대규모 빗물 처리 지하 인프라 시설 통합유지관리 시스템 실증 및 적용성 검증[배수, 저류 등 성능 기존 대비 100% 확보 및 유지관리 비용 30% 절감] ○ 구조적 상태 모니터링 및 진단 시스템 개발 ○ 안전환경 유지관리 시스템 개발 ○ 자산관리 솔루션 개발 및 필드데이터 기반 통합유지관리 시스템 개발 ○ 대규모 빗물 처리 지하 인프라 시설(신월 대심도...
통합 유지관리 시스템
도시침수
홍수방어
대심도 빗물터널
자산관리

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