The high dimensionality of the modern remote sensing data of construction land makes it complicated to extract image data. This paper proposes a dimensionality reduction and extraction strategy for the remote sensing data of construction land, with the aid of building information modeling (BIM) and geographical information system (GIS). Firstly, the BIM was employed to reduce the size of the remote sensing data of construction land and to obtain the information of each element. Next, the remote sensing data of construction land were parsed, and the key BIM elements were extracted through semantic filtering. In addition, the remote sensing data were converted into a triangulated irregular network (TIN), which can be processed by the geographical information system (GIS). In the end, random projection was utilized to reduce the dimensionality and compress the remote sensing data, and realize the data extraction. Experimental results show that our approach can compress and extract the information from construction land images in the remote sensing data with a high accuracy.
For water purification plants and sewage treatment plants, there is no reference optimal steel content for the wall of reinforced concrete water pool. Focusing on the reinforced concrete water pool, this paper explores the optimal wall thickness and optimal reinforcement area at different bending moments and identifies the optimal steel content. To solve the problem, the authors established the discrete distribution function for the engineering cost per unit length of pool wall and steel content. The theoretical model was verified by numerous data through Excel numerical simulation. The research results provide a reference optimal steel content for designers and help to save the engineering cost of pool wall.
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