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지진의 위험요인을 고려한 공동주택의 내진보강 우선순위 결정에 관한 연구
한범진,Han, Bum-Jin 한국건축시공학회 2023 한국건축시공학회지 Vol.23 No.4
Recent seismic activities in countries like China and Turkey have underscored the widespread and severe damages that earthquakes can inflict globally. Being situated in a seismically active zone, South Korea can no longer regard itself as immune to earthquake hazards, necessitating the urgent adoption of proactive measures against such threats. The government has been proactive in evaluating, formulating processes, and methods for the seismic retrofitting of public buildings lacking in earthquake resistance. However, enforcement mechanisms for privately-owned apartment complexes are absent, and in the face of insufficient previous research and guidelines, preemptive measures for public safety remain alarmingly inadequate. With over 48% of residential structures in Korea aged over 30 years, and apartment complexes constituting more than 80% of these, the gravity of the situation is undeniable. This study deduces key factors for seismic retrofitting of apartment buildings like earthquake zones, soil type, building significance, aging degree, vulnerability, etc., based on building seismic design codes. It further proposes an algorithm for a more succinct and efficient determination of the priority of seismic reinforcements for apartment buildings.
건설 안전관리를 위한 Safety Climate 평가요인별 중요도 분석 연구
한범진 ( Han¸ Bum-jin ),김태희 ( Kim¸ Taehui ),손승현 ( Son¸ Seunghyun ) 한국건축시공학회 2023 한국건축시공학회지 Vol.23 No.5
Pervasive research underscores the direct correlation between an enhanced safety climate and a marked reduction in accidents. The intricacies of safety climate are governed by three pivotal strata: organizational management, on-site operations, and the broader enterprise framework. Within an organizational context, sustaining optimal performance across these layers poses a considerable challenge, often attributable to the constraints of available managerial bandwidth. It becomes imperative, then, to conceive a phased enhancement blueprint for the safety climate. To orchestrate this blueprint with precision, a discerning understanding of the hierarchy of safety climate metrics is essential, which subsequently guides judicious managerial resource allocation. This investigation is anchored in elucidating the hierarchical significance of safety climate metrics through the Analytical Hierarchy Process(AHP). Implementing the AHP framework, both a questionnaire was disseminated and a subsequent analysis undertaken, culminating in the extraction of relative priorities of safety climate determinants. Consequent to this analysis, “workers’ safety prioritization and risk aversion” emerged as the foremost dimension, holding a significance weight of 0.1900. Furthermore, within the detailed elements, “unwavering adherence to safety mandates amidst demanding operational constraints” ranked supreme, manifesting a weight of 0.6663. The findings encapsulated in this study are poised to be foundational in sculpting improvements at an institutional level and devising policies, all with the end goal of fostering an exemplar safety climate within construction arenas.
손승현 ( Son Seung-hyun ),김지명 ( Kim Ji-myong ),한범진 ( Han Bum-jin ),나영주 ( Na Young-ju ),김태희 ( Kim Tae-hee ) 한국건축시공학회 2021 한국건축시공학회 학술발표대회 논문집 Vol.21 No.1
The sale price of apartment buildings is a key factor in the success or failure of apartment projects, and the factors that affect the sale price of apartments vary widely, including location, environmental factors, and economic conditions. Existing methods of predicting the sale price do not reflect the nonlinear characteristics of apartment prices, which are determined by the complex impact factors of reality, because statistical analysis is conducted under the assumption of a linear model. To improve these problems, a new analysis technique is needed to predict apartment sales prices by complex nonlinear influencing factors. Using machine learning techniques that have recently attracted attention in the field of engineering, it is possible to predict the sale price reflecting the complexity of various factors. Therefore, this study aims to conduct a basic study for the development of a machine learning-based prediction model for apartment sale prices.
대형 공공연구인프라의 운영 효율성 제고를 위한 운영 및 유지관리비 평가모델 개발 기초연구
최선아 ( Choi Sun-ah ),손승현 ( Son Seung-hyun ),이성호 ( Lee Sung-ho ),오엄중 ( Oh Oum-joong ),한범진 ( Han Bum-jin ) 한국건축시공학회 2021 한국건축시공학회 학술발표대회 논문집 Vol.21 No.1
The Korean government has invested a tremendous amount of money in the last 10 years to build large public research infrastructures (LPRI). For efficient operation and maintenance of LPRI built with expensive equipment and professional engineers, reasonable budget needs to be allocated. However, it is difficult to fulfill sustainable operation and maintenance (O&M) because there is no standard on budgeting for efficient LPRI operation, including expensive equipment and manpower allocation. There have been a lot of cost assessment studies regarding O&M of high-demand facilities such as hospitals, hotels and residential buildings, but a very few on sustainable O&M of LPRI. Therefore, mid/long-term budget establishment plans for efficient LPRI O&M are required from the initial planning stage and a cost assessment model to support the plans should be developed. The objective of this paper is to propose a cost assessment model for sustainable operation and maintenance of large public research infrastructures. To do so, actual O&M data of 6 LPRI types in operation are collected, and regression analysis model (RAM) is used for development and evaluation a cost assessment model. The study result will support sustainable operation of LPRI from a business perspective and be used as basic data for continuous development of cost assessment models to establish budgets for LPRI operation from an academic perspective.