• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2017년도 추계 학술논문 발표대회
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• pp.83-84
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• 2017

In the construction site, it is necessary to estimate the compressive strength of concrete in order to adjust the demolding time of the form, and establish and adjust the construction schedule. The compressive strength of concrete is determined by various influencing factors. However, the conventional method for estimating the compressive strength of concrete has been suggested by considering only 1 to 3 specific influential factors as variables. In this study, seven influential factors (W/B ratio, Water, Cement, Fly ash, Blast furnace slag, Curing temperature, and humidity) of papers opened for 10 years were collected at three conferences in order to know the various correlations among data and the tendency of data. The purpose of this paper is to estimate compressive strength more accurately by applying it to algorithm of the Deep learning.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.1047-1052
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• 2009

As buildings nowadays become taller in height and larger in size the safety review of lifting plan takes larger portion in construction project management. However, the cost and safety in lifting plan have a contradictory effect on each other. Therefore, an optimization algorithm needs devising as a solution of the contradictory problem. In many cases at construction sites, selections and stability review of tower cranes are assigned to equipment suppliers or field managers, which cause the problems in safety and cost of the projects. To improve the part of the current situation, a study on the optimization algorithm for designing the foundation of tower cranes is conducted in this study, which can be utilized by equipment suppliers or field managers to check the stability of tower cranes easily and promptly without substantial knowledge.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2015년도 추계 학술논문 발표대회
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• pp.185-186
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• 2015

The Standard penetration test determines the type of soil according to soil bearing capacity, and this classifies the subsoil into many layers. Construction project managers are willing to know the depth of the present types of subsoil on site in order to make plans on earthwork stage during excavation. However the standard penetration test may not provide accurate information on subsoil type due to incorrect spacing. To solve this problem, this study propose a conceptual algorithm for determining the spacing of standard penetration test spots to essentially tests relevant locations on which to be applied the standard penetration test. This provides the acquirement of the accurate layered model volume of earthwork revised into geological columnar section. This algorithm will determine the appropriate standard penetration test spots spacing on a given size of site to optimize the accuracy of the earthwork volume, time and cost.

• 인터넷정보학회논문지
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• 제5권5호
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• pp.119-127
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• 2004

언어 기반 프로그래밍 환경의 발전에 따라 점진적 파싱에 대한 연구는 핵심적인 분야가 되었다. 본 논문의 목적은 파싱 속도(lime)와 기억장소가 많이 요구하는 기존의 알고리즘들보다 효율적인 점진적 파싱 알고리즘을 제시하는데 있다. 본 논문에서는 스택을 이용하지 않는 스레드 트리 구성 알고리즘을 제안하였다. 또한 노드의 재파싱 과정을 없애기 위해 스택을 이용하지 않는 노드 생성 알고리즘과 점진적 스레드 트리 구성 알고리즘을 제안하였다.

• 인터넷정보학회논문지
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• 제7권4호
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• pp.91-99
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• 2006

점진적 파싱 기법은 프로그램의 점진적 구성을 허용하는 언어기반 환경의 중요한 부분이며, 프로그램의 변경된 부분에 대해서만 구문분석을 다시 함으로써 시스템의 성능을 향상 시킨다. 기존의 점진적 파싱은 파싱 정보를 저장하기 위해 스택 자료구조를 사용한다. 본 논문에서는 스택 자료구조를 사용하지 않고 노드 주소로 스레드를 추가하여 스레드 트리 구성 알고리즘을 제안한다. 또한 구성된 스레드 트리를 사용하여 5단계의 점진적 파싱 과정으로 나누어 점진적 스레드 트리 구성 알고리즘을 제안한다.

• Wind and Structures
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• 제2권3호
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• pp.189-200
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• 1999

In this paper, a new algorithm for active control design of structures is proposed and investigated. The algorithm preserves the decoupling property of the modal vibration equation and eliminates the spillover problem, which is the main shortcoming in the independent modal space control(IMSC) algorithm. With linear quadratic regulator(LQR) control law, the analytical solution of algebraic Riccati equation and the optimal actuator control force are obtained, and the control design procedure is significantly simplified. A numerical example for the control design of a tall building subjected to wind loads demonstrates the effectiveness of the proposed algorithm in reducing the acceleration and displacement responses of tall buildings under wind actions.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.656-657
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• 2015

Recently, Multi-Objective Optimization of design elements is an important issue in building design. Design variables that considering the specificities of the different environments should use the appropriate algorithm on optimization process. The purpose of this study is to compare and analyze the optimal solution using three evolutionary algorithms and energy modeling simulation. This paper consists of three steps: i)Developing three evolutionary algorithm model for optimization of design elements ; ii) Conducting Multi-Objective Optimization based on the developed model ; iii) Conducting comparative analysis of the optimal solution from each of the algorithms. Including Non-dominated Sorted Genetic Algorithm (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO) and Random Search were used for optimization. Each algorithm showed similar range of result data. However, the execution speed of the optimization using the algorithm was shown a difference. NSGA-II showed the fastest execution speed. Moreover, the most optimal solution distribution is derived from NSGA-II.

• 한국건축시공학회지
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• 제23권4호
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• pp.497-508
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• 2023

Manual estimation of rebar quantities by contractors often yields discrepancies between projected and actual amounts used in the construction phase, leading to cost inaccuracies and potential logistical challenges. To address these issues, there is a clear need for a method that allows precise estimation of rebar quantities during the design phase. Such a method would enhance contractor competitiveness during project bids, promote accurate cost calculations, and avert superfluous rebar purchases on-site. Given that columns are the primary structural components in reinforced concrete(RC) buildings and necessitate considerable amounts of rebar, this study focuses on creating an automated algorithm for estimating column rebar quantities. An analysis of the accurate quantities obtained via the study and those derived from manual estimation reveals a discrepancy of 0.346 tons or 2.056%. This comparison affirms the proposed algorithm's efficacy in eliminating errors from overestimation or underestimation of rebar quantities. The practical implications of this study are significant for construction companies as it fosters efficient and precise estimation of rebar quantities, ensuring compliance with related specifications and governing regulations.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2022년도 가을 학술논문 발표대회
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• pp.223-224
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• 2022

The occurrence of defect, one of the major risk elements, gives rise to construction delays and additional costs. Although construction companies generally prefer to use a method of identifying and classifying the causes of defects, a system for predicting the rise of defects becomes important matter to reduce this harmful issue. However, the currently used methods are kinds of reactive systems that are focused on the defects which occurred already, and there are few studies on the occurrence of defects with prediction systems. This paper is about preliminary study on the development of judgemental algorithm that informs us whether additional works related to defect issue are needed or not. Among machine learning techniques, deep neural network was utilized as prediction model which is a major component of algorithm. It is the most suitable model to be applied to the algorithm when there are 8 hidden layers and the average number of nodes in each hidden layer is 70. Ultimately, the algorithm can identify and defects that may arise in later and contribute to minimize defect frequency.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2023년도 봄 학술논문 발표대회
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• pp.21-22
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• 2023

In reinforced concrete constructions, reinforcing bar generates more CO₂ per unit weight than other construction materials. In particular, cutting and bending rebar is the main source of rebar waste in the construction industry. Rebar-cutting waste is inevitable during the construction of a reinforced concrete structure since the rebar is not manufactured as designed. Large amounts of waste can be avoided by utilizing optimal cutting patterns and schedules. This research provides a fundamental analysis of the automatic calculation of column rebar length using BIM-based shape codes to minimize cutting waste to near zero. By employing this approach in practice, it is possible to minimize the rate of rebar-cutting waste, reduce costs, shorten construction duration, and reduce CO₂ emissions. In addition, the development of this research will serve as a clue for the development of BIM-based rebar layout automation algorithms.