다중 프로젝트 대상 최적 리모델링 계획 의사결정 지원 시스템
- Author(s)
- 박한빈
- Issued Date
- 2024
- Abstract
- Since the recovery of the trend of rising blood pressure in 1992, many companies have maintained general shareholder meetings and are trying to increase their contribution to greenhouse gases. The Korean government officially declared in October 2020 that it will abolish the carbon amount, which consists of equal carbon amount and carbon absorption, to zero by 2050. In addition, we have a 2030 national greenhouse gasket goal that supports coordination of 26.3% of sneakers by 2030 with 26.3% of sneakers as of 2018, and cost aspects such as submission to post-change relations, urban regeneration-linked business model, and open building energy utilization performance. A description of greenhouse gases was agreed upon. As a result, the provisional greenhouse gas emissions for 2022 will begin to be adjusted to maintain 3.5%, and will account for 10% of the variable compared to the 2018 greenhouse gas adjustment. Nevertheless, greenhouse gas emissions from the construction sector in 2022 increased compared to the previous year, and the cause was the increase in city gas consumption. Heating consumption may have increased due to the effects of rapid climate change, but the best way to reduce consumption itself is to increase the energy saving performance of existing and aging buildings through remodeling. Accordingly, green remodeling projects to improve the energy saving performance of existing buildings are active. In addition, a green remodeling project to improve the energy performance of old buildings is also underway, but due to the nature of old buildings, the remodeling process is difficult due to the absence or change of existing drawings. To solve this problem, research is currently underway to conduct reverse engineering using cutting-edge equipment, including 3D scanners, to conduct remodeling based on reverse-engineered drawings, and to find the optimal remodeling alternative for a single building. However, the number of aging buildings in Korea is very high, accounting for more than 30% of all buildings, and because the country went through rapid industrialization in the 1980s, there are many old public houses completed on residential land and city level around the same time. In order to improve energy saving performance in line with the demand for remodeling old buildings, efforts are needed to improve energy saving performance at the city level. Developed countries, including the EU, are promoting various smart city projects and are working to improve energy saving performance at the city level, and the results are being reported. However, in Korea, there is a lack of research on the selection of multiple remodeling alternatives to improve energy saving performance at the city level. Therefore, in this study, we process reverse engineering using copper equipment that can select a 3D scanner, create a BIM model based on the reverse engineering, and then obtain the necessary information from the grid. After data selection, we fuse the modules to find selectable clusters for a single mixture, find selection ranges for multiple aspects, and compare alternatives for both cases to see which one we think is more advantageous for greenhouse gas rate characterization. Analyze, do better. thinking about you This could help the surveyor find a more reasonable alternative, one that could provide favorable greenhouse gas performance in a large area such as a city.
- Alternative Title
- Decision Support System for Developing The Optimal Renovation Strategy of The Multiple Renovation Projects
- Alternative Author(s)
- Hanbin Park
- Affiliation
- 조선대학교 일반대학원
- Department
- 일반대학원 건축공학과
- Advisor
- 조규만
- Awarded Date
- 2024-02
- Table Of Contents
- 제 1장 서론 1
1.1. 연구 배경 및 목적 1
1.2. 연구 범위 및 방법 4
제 2장 선행연구 분석 및 고찰 7
2.1. 리모델링 대안 선정 방안 7
2.1.1 그린 리모델링 대안의 기술요소 8
2.1.2 리모델링 대안 선정 방안 관련 기존 연구고찰 10
2.2. 리모델링 방안의 에너지 절감 성능 계산 방법 14
2.2.1 에너지 절감 성능 산정 기술 14
2.2.2 에너지 절감 성능 산정 관련 기존 연구고찰 16
제 3장 다중 프로젝트 대상 최적 리모델링 계획 의사 결정 지원 시스템 19
3.1. 리모델링 대안 선정을 위한 요구 데이터 21
3.2. 요구 데이터 획득 방법 23
3.2.1 일반적인 Scan to BIM 방식 23
3.2.2 최적 대안 선정을 위한 Scan to BIM 기술 변형 25
3.3. 리모델링 계획에 따른 에너지 성능 및 비용 평가 방법 27
3.3.1 Building renovation solution selection module 27
3.3.2 Energy evaluation module 29
3.3.3 Cost estimating module 34
3.4. 리모델링 계획 최적화 방법 38
제 4장 사례 적용 43
4.1. 사례 적용 대상 43
4.2. 데이터 획득 및 BIM 모델링 44
4.3. 개발 기술의 정확도 분석 50
4.4. 단일 건물 최적 대안과 다중 건물 최적 대안 비교분석 53
4.4.1 단일 건물 최적 리모델링 대안 선정 53
4.4.2 다중 건물 최적 리모델링 대안 선정 66
4.4.3 대안별 비교분석 68
제 5장 결론 71
참고문헌 74
- Degree
- Master
- Publisher
- 조선대학교 대학원
- Citation
- 박한빈. (2024). 다중 프로젝트 대상 최적 리모델링 계획 의사결정 지원 시스템.
- Type
- Dissertation
- URI
- https://oak.chosun.ac.kr/handle/2020.oak/18000
http://chosun.dcollection.net/common/orgView/200000720074
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