高精緻度房屋模型結合蟻群演算法於室內最佳路徑選擇之應用;High Level of Detail of Building Models and Ant Colony Optimization for Indoor Routes Selection Applications

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题名:	高精緻度房屋模型結合蟻群演算法於室內最佳路徑選擇之應用;High Level of Detail of Building Models and Ant Colony Optimization for Indoor Routes Selection Applications
作者:	鄭閔隆;Cheng,Min-Lung
贡献者:	土木工程研究所
关键词:	蟻群演算法;最佳路徑;空間應用與分析;建物屬性;建築資訊模型;Spatial Applications and Analysis;Building Information Modeling;Attributes
日期:	2012-07-30
上传时间:	2012-09-11 18:04:33 (UTC+8)
出版者:	國立中央大學
摘要:	由於多樣化的功能與應用，三維數位建物模型在近年來已成為一個新興的研究領域。在CityGML的規範中，針對不同的尺度與應用建議，可以用細節層級 (Level Of Detail, LOD)表達數位建築物模型的細緻程度。三維房屋模型的建置方法主要來自空間資訊領域的由上而下法與建築資訊模型 (Building Information Modeling, BIM) 的由下而上法，本研究初期整合兩個不同領域的建模方法並歸納出一套高精緻度房屋模型的重建程序，並將建築物的屬性資料與模型結合以提供資料展示、查詢以及應用功能。基於公共空間安全議題，本研究在空間應用與分析方面結合房屋資訊模型與蟻群演算法模擬室內搜救行動與逃生疏散最佳路徑的選擇。研究成果顯示，房屋屬性資料可以反應不同路徑的成本，供使用者在不同狀況下選擇。在火災搜救案例中，藉由房屋模型所提供之資訊與不同使用設備搜尋不同路徑比較成本大小，並選擇成本較低之路徑進行搜救任務。於逃生與疏散案例，則比較不同時間點與不同路徑成本的變化，藉由比較成本的大小選擇當下的最佳逃生與疏散路徑。Recently, 3D building models has become a new research focus for its diversified functionalities and applications. In CityGML specification, the level of detail (LOD)can be used to represent city models in different scales and for different applications. There are mainly two approaches to reconstruct building models. The first (Geoinformatic)uses a top-down strategy; and the other one (Building Information Modeling, BIM) opts the bottom-up method. This research integrates the two approaches and provides an efficiency way to reconstruct high level of detail building models. In addition to the functionality of visualization, this research also adds information-based attributes into building models for applications . Two spatial analysis application examples are presented in this paper, which are designed to address two safety issues in public spaces. These two examples are carried out based on the reconstructed building models and using ant colony optimization algorithms for indoor search-and-rescue and evacuation routes selections. The results show that building models with information-based attributes can be used to determine the costs for best route selection effectively. In the search-and-rescue example, fire fighters may compute the costs of different routes and choose the best one for rescue mission. For the evacuation simulation example, the ant colony algorithm is modified to dynamically calculate the costs of different evacuation routes in order to provide the best options for the passengers in a complicated train station.
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