• 한국BIM학회 논문집
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• 제10권3호
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• pp.22-32
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• 2020

Apartment building site planning is one of time consuming and labor-intensive tasks in architectural design field, due to its complexity in zoning regulations, building codes, local restrictions, and site-specific conditions. In other words, the process can be seen as a very complicated mathematical function with layers of variables and parameters, which ironically can be automated using computational methods on parametric tools. In this paper, a practical method of automating site planning of an apartment complex has been proposed by utilizing parametric approaches in Rhino 3D and Grasshopper. Two primary parameters, building heights and positions, determine the efficacy of building layouts under all regulatory standards, thus testing out numerous combinations of the two will produce some successful layout alternatives. For this, equation solver has been used for iterating the parametric model to sort out meaningful results among others. It also has been proven that the proposed process significantly reduced the time in site planning down to less than an hour on most cases, and many successful alternatives could be obtained by using multiple computers. Post evaluation processes such as day light and view shed analysis helped sort out the best performing ones out of functioning alternatives.

• 한국건축시공학회지
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• 제12권6호
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• pp.664-673
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• 2012

Determining the layout of temporary facilities that support construction activities at a site is an important planning activity, as layout can significantly affect cost, quality of work, safety, and other aspects of the project. The construction site layout problem involves difficult combinatorial optimization. Recently, various artificial intelligence(AI)-based algorithms have been applied to solving many complex optimization problems, including neural networks(NN), genetic algorithms(GA), and swarm intelligence(SI) which relates to the collective behavior of social systems such as honey bees and birds. This study proposes a site facility layout optimization algorithm based on self-organizing maps(SOM). Computational experiments are carried out to justify the efficiency of the proposed method and compare it with particle swarm optimization(PSO). The results show that the proposed algorithm can be efficiently employed to solve the problem of site layout.

• Journal of Construction Engineering and Project Management
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• 제7권2호
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• pp.1-10
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• 2017

The Clonal Selection Algorithm (CSA) is an algorithm inspired by the human immune system mechanism. In CSA, several parameters needs to be optimized by large amount of sensitivity analysis for the optimal results. They limit the accuracy of the results due to the uncertainty and subjectivity. Adaptive Clonal Selection (ACS), a modified version of CSA, is developed as an algorithm without controls by pre-defined parameters in terms of selection process and mutation strength. In this paper, we discuss the ACS in detail and present its implementation in construction site utilization planning (CSUP). When applied to a developed model published in research literature, it proves that the ACS are capable of searching the optimal layout of temporary facilities on construction site based on the result of objective function, especially when the parameterization process is considered. Although the ACS still needs some improvements, obtaining a promising result when working on a same case study computed by Genetic Algorithm and Electimze algorithm prove its potential in solving more complex construction optimization problems in the future.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2018년도 춘계 학술논문 발표대회
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• pp.39-40
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• 2018

The goal of site layout planning(SLP) is to maximize the productivity and efficiency of the construction by reducing travel distance and material handling cost and manpower. However, SLPs are static layout schemes, which cannot be reorganized during the construction process to correspond with errors, phase transition, changing working environments on the site. To solve this problem, researches on dynamic site layout planning(DSLP) are emerging. This preliminary study clarifies characteristics of temporary facility's variables to develop the vertical DSLP algorithm of high-rise building construction.

• 한국환경복원기술학회지
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• 제21권2호
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• pp.27-38
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• 2018

Environmental planning includes resource allocation and spatial planning process for the conservation and management of environment. Because the spatialization of the environmental planning is not specifically addressed in the relevant statutes, it actually depends on the qualitative methodology such as expert judgement. The results of the qualitative methodology have the advantage that the accumulated knowledge and intuition of the experts can be utilized. However, it is difficult to objectively judge whether it is enough to solve the original problem or whether it is the best of the possible scenarios. Therefore, this study proposed a methodology to quantitatively and objectively spatialize various environmental planning. At first, we suggested a quantitative spatial planning model based on an optimization algorithm. Secondly, we applied this model to two kinds of environmental planning and discussed about the model performance to present the applicability. Since the models were developed based on conceptual study site, there was a limitation in showing possibility of practical use. However, we expected that this study can contribute to the fields related to environmental planning by suggesting flexible and novel methodology.

• Nuclear Engineering and Technology
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• 제55권5호
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• pp.1838-1854
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• 2023

This paper presents a hybrid algorithm to solve the multi-objective path planning (MOPP) problem for mobile robots in a static nuclear accident environment. The proposed algorithm mimics a real nuclear accident site by modeling the environment with a two-layer cost grid map based on geometric modeling and Monte Carlo calculations. The proposed algorithm consists of two steps. The first step optimizes a path by the hybridization of improved ant colony optimization algorithm-modified A^* (IACO-A^*) that minimizes path length, cumulative radiation dose and energy consumption. The second module is the high radiation dose rate avoidance strategy integrated with the IACO-A^* algorithm, which will work when the mobile robots sense the lethal radiation dose rate, avoiding radioactive sources with high dose levels. Simulations have been performed under environments of different complexity to evaluate the efficiency of the proposed algorithm, and the results show that IACO-A^* has better path quality than ACO and IACO. In addition, a study comparing the proposed IACO-A^* algorithm and recent path planning (PP) methods in three scenarios has been performed. The simulation results show that the proposed IACO-A^* IACO-A^* algorithm is obviously superior in terms of stability and minimization the total cost of MOPP.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2002년도 춘계 학술발표회 논문집
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• pp.284-293
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• 2002

Using the life cycle cost concept, optimum maintenance and retrofit planning for reliable seismic performance is suggested the overall life cycle cost to be minimized including the initial cost, the costs of inspection, repair, and failure. Limit states of the bridges are defined. And failure probabilities are computed through crossing theory. The effect of maintenance and retrofit is represented using the probability of damage detection and event tree analysis. Optimization of maintenance and retrofit planning method proposed from this research was applied to numerical examples. The analysis incorporates the acceleration and site conditions prescribed in the code, and the quality of inspection methods.

• 농촌계획
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• 제12권4호
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• pp.77-82
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• 2006

The objective of public works planning is being converted from economic growth to sustainable development. So, the demand for considering social & ecological influences as well as economic components had been increased in evaluation of public works planning. In evaluation of public works, its components related with benefit and cost in feasibility analysis can be classified to qualitative and qualitative elements. Qualitative elements are evaluated by qualitative methods which can manage various items, be commonly applied nationwide, and consider elements that can be calculated numerically such as environments, willingness, etc. In this study, using the concept of 'Information measure', a method to design planning of rural works is proposed. 'Information benefit model' for rural works can evaluate present plan in the side of provider's and demander's 'benefit'. And, optimizing method of rural works by 'Information benefit' can simulate present state and optimize the site and route of rural works.

• 한국건설관리학회논문집
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• 제22권1호
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• pp.36-46
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• 2021

최근 모듈러 건축의 고층화와 함께 현장에서의 T/C 사용도 증가하였다. 이러한 T/C의 대수, 제원 및 위치를 결정하는 TCLP는 프로젝트의 공사비용, 공사기간, 안전, 생산성에 큰 영향을 미친다. 특히 모듈러 건축 프로젝트에서 조건에 부합하지 않는 TCLP는 추가 장비 투입, 재설계 등의 작업으로 이어져 공사기간과 공사비 증가가 발생할 수 있다. 따라서 현장관리자는 현장조건, 양중대상, T/C 제원 등 수많은 제약을 고려하여 프로젝트에 적합한 TCLP를 수립해야 한다. 하지만 다중 T/C를 사용하는 건설현장의 경우 추가적인 고려사항과 변수들로 인해 경험과 직관을 통해 TCLP를 수립하는데 어려움이 있다. 이를 위해 본 연구에서는 비용과 간섭면적의 최소화를 목적으로 하는 다중 최적화(multi-objective optimization) 알고리즘을 개발하고, 이를 활용하여 T/C 대수, 제원 및 위치를 선정하는 모델을 제안한다. 본 연구에서는 기존의 단일 최적화 방식과 달리 다중 최적화를 통한 TCLP 수립 프로세스를 제안함으로써, 비용과 간섭면적의 trade-off를 고려한 경제적이고 효율적인 프로젝트 완료를 기대할 수 있음을 보여주었다.

• 한국건설관리학회논문집
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• 제24권5호
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• pp.73-82
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• 2023

Optimization of Construction Site Layout Planning (CSLP) heavily relies on workers' travel paths. However, traditional path generation approaches predominantly focus on the shortest path, often neglecting critical variables such as individual wayfinding tendencies, the spatial arrangement of site objects, and potential hazards. These oversights can lead to compromised path simulations, resulting in less reliable site layout plans. While Deep Reinforcement Learning (DRL) has been proposed as a potential alternative to address these issues, it has shown limitations. Despite presenting more realistic travel paths by considering these variables, DRL often struggles with efficiency in complex environments, leading to extended learning times and potential failures. To overcome these challenges, this study introduces a refined model that enhances spatial navigation capabilities and learning performance by integrating workers' visibility into the reward functions. The proposed model demonstrated a 12.47% increase in the pathfinding success rate and notable improvements in the other two performance measures compared to the existing DRL framework. The adoption of this model could greatly enhance the reliability of the results, ultimately improving site operational efficiency and safety management such as by reducing site congestion and accidents. Future research could expand this study by simulating travel paths in dynamic, multi-agent environments that represent different stages of construction.