The Integrated 3D As-Built Representation of Underground MRT Construction Sites

This study facilitates the scalability of as-built data from an earlier street level to underground transportation sites from the life-cycle perspective of urban information maintenance. As-built 3D scans of a 6 km street were made at different time periods, and of 3 underground Mass Rapid Transit (MRT) stations under construction in Taipei. A scanned point cloud was used to create a Building Information Modeling (BIM) Level of Development (LOD) 500 as-built point cloud model, with which topographic utility data were integrated and the model quality was investigated. The complex underground models of the transportation stations are proofed to be in correct relative locations to the street entrances on ground level. In the future the 3D relationship around the station will facilitate new designs or excavations in the neighborhood urban environment.

Cross-Verification of As-Built Point Cloud and GIS-Related Map Data

This study cross-validates existing urban maps using point cloud models to update GIS related data. The model, as-built 3D data, is created to integrate with maps in an architectural CAD platform. The clouds are referred by existing vector maps to verify inconsistency and to update 3D spatial relationships between subjects and environment. The cloud model shows its top reference hierarchy as the updated data for topographic-derived urban maps.

Towards Automatic Generation of As-built BIM:3D Building Facade Modeling and Material Recognition from Images

As-built building information model （BIM） is an urgent need of the architecture, engineering, construction and facilities management （AEC/FM） community. However, its creation procedure is still labor-intensive and far from maturity. Taking advantage of prevalence of digital cameras and the development of advanced computer vision technology, the paper proposes to reconstruct a building facade and recognize its surface materials from images taken from various points of view. These can serve as initial steps towards automatic generation of as-built BIM. Specifically, 3D point clouds are generated from multiple images using structure from motion method and then segmented into planar components, which are further recognized as different structural components through knowledge based reasoning. Windows are detected through a multilayered complementary strategy by combining detection results from every semantic layer. A novel machine learning based 3D material recognition strategy is presented. Binary classifiers are trained through support vector machines. Material type at a given 3D location is predicted by all its corresponding 2D feature points. Experimental results from three existing buildings validate the proposed system.

洁净室洁净度分级标准探究

鉴于科技的进步和实验经验,ISO/TC 209出台ISO14644-1:2015《按粒子浓度划出空气洁净度等级》Classification of air cleanliness by particle concentration比ISO14644-1:1999《空气洁净度等级》Classification of air cleanliness技术概念更清晰,使用更方便;实事求是,更赋灵活性:分级表中,所有浓度值都是累积,包括所有大于等于关注粒径(Considered particle size)的粒子的最大允许浓度值(Maximun allowable concentration siae),浓度限值。区域粒子浓度太高,浓度限值不适用;或者由于低浓度时采样和统计方法的局限性区域分级不适用。按统计学技术概念,决定检测洁净度最少采样点数N_(L);N_(L)值与洁净度无直接关联。作为标准应用的补充,超净环境检测需关注超高过滤器滤材最易穿透粒径MPPS,Most penetrating particle size。