Modern architectures are developing in the direction of tall buildings and complex structures,and the theoretical analysis and the design experience have seriously lagged behind the construction of super high-rise str...Modern architectures are developing in the direction of tall buildings and complex structures,and the theoretical analysis and the design experience have seriously lagged behind the construction of super high-rise structures.Structural form selection,especially the case based reasoning (CBR) based structural form selection,is a promising tool for the construction of high-rise structures.In view of the limit of cognitive ability of domain experts,a BP (back propagation)-PSO (particle swarm optimization)-based intelligence case retrieval method for high-rise structural form selection is proposed.The CBR-based case retrieval method and the construction of the BP-PSO neutral network are introduced.And then the BP-PSO-based case retrieval method is validated by some engineering cases.The results of training and prediction indicate that the proposed method has good ability to retrieve the cases of high-rise structures.展开更多
Building pattern recognition is important for understanding urban forms,automating map generalization,and visualizing 3D city models.However,current approaches based on object-independent methods have limitations in c...Building pattern recognition is important for understanding urban forms,automating map generalization,and visualizing 3D city models.However,current approaches based on object-independent methods have limitations in capturing all visually aware patterns due to the part-based nature of human vision.Moreover,these approaches also suffer from inefficiencies when applying proximity graph models.To address these limitations,we propose a framework that leverages multi-scale data and a knowledge graph,focusing on recognizing C-shaped building patterns.We first employ a specialized knowledge graph to represent the relationships between buildings within and across various scales.Subsequently,we convert the rules for C-shaped pattern recognition and enhancement into query conditions,where the enhancement refers to using patterns recognized at one scale to enhance pattern recognition at other scales.Finally,rule-based reasoning is applied within the constructed knowledge graph to recognize and enrich C-shaped building patterns.We verify the effectiveness of our method using multi-scale data with three levels of detail(LODs)collected from AMap,and our method achieves a higher recall rate of 26.4%for LOD1,20.0%for LOD2,and 9.1%for LOD3 compared to existing methods with similar precisionrates.We,also achieve recognition efficiency improvements of 0.91,1.37,and 9.35 times,respectively.展开更多
First, the high-rise building structure design process is divided into three relevant steps, that is, scheme generation and creation, performance evaluation, and scheme optimization. Then with the application of relat...First, the high-rise building structure design process is divided into three relevant steps, that is, scheme generation and creation, performance evaluation, and scheme optimization. Then with the application of relational database, the case database of high-rise structures is constructed, the structure form-selection designing methods such as the smart algorithm based on CBR, DM, FINS, NN and GA is presented, and the original forms system of this method and its general structure are given. CBR and DM are used to generate scheme candidates; FINS and NN to evaluate and optimize the scheme performance; GA to create new structure forms. Finally, the application cases are presented, whose results fit in with the real project. It proves by combining and using the expert intelligence, algorithm intelligence and machine intelligence that this method makes good use of not only the engineering project knowledge and expertise but also much deeper knowledge contained in various engineering cases. In other words, it is because the form selection has a strong background support of vast real cases that its results prove more reliable and more acceptable. So the introduction of this method provides an effective approach to improving the quality, efficiency, automatic and smart level of high-rise structures form selection design.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 61040031)the Technological Project of Henan Province (Grant No. 082102210066)
文摘Modern architectures are developing in the direction of tall buildings and complex structures,and the theoretical analysis and the design experience have seriously lagged behind the construction of super high-rise structures.Structural form selection,especially the case based reasoning (CBR) based structural form selection,is a promising tool for the construction of high-rise structures.In view of the limit of cognitive ability of domain experts,a BP (back propagation)-PSO (particle swarm optimization)-based intelligence case retrieval method for high-rise structural form selection is proposed.The CBR-based case retrieval method and the construction of the BP-PSO neutral network are introduced.And then the BP-PSO-based case retrieval method is validated by some engineering cases.The results of training and prediction indicate that the proposed method has good ability to retrieve the cases of high-rise structures.
基金supported by The National Natural Science Foundation of China(No.41871378)The Youth Inno-vation Promotion Association Foundation of Chinese Academic of Sciences(No.Y9C0060)+1 种基金Fundamental Research Funds for the Central Universities(No.070323006)State Key Laboratory of Networking and Switching Tech-nology(No.600123442).
文摘Building pattern recognition is important for understanding urban forms,automating map generalization,and visualizing 3D city models.However,current approaches based on object-independent methods have limitations in capturing all visually aware patterns due to the part-based nature of human vision.Moreover,these approaches also suffer from inefficiencies when applying proximity graph models.To address these limitations,we propose a framework that leverages multi-scale data and a knowledge graph,focusing on recognizing C-shaped building patterns.We first employ a specialized knowledge graph to represent the relationships between buildings within and across various scales.Subsequently,we convert the rules for C-shaped pattern recognition and enhancement into query conditions,where the enhancement refers to using patterns recognized at one scale to enhance pattern recognition at other scales.Finally,rule-based reasoning is applied within the constructed knowledge graph to recognize and enrich C-shaped building patterns.We verify the effectiveness of our method using multi-scale data with three levels of detail(LODs)collected from AMap,and our method achieves a higher recall rate of 26.4%for LOD1,20.0%for LOD2,and 9.1%for LOD3 compared to existing methods with similar precisionrates.We,also achieve recognition efficiency improvements of 0.91,1.37,and 9.35 times,respectively.
文摘First, the high-rise building structure design process is divided into three relevant steps, that is, scheme generation and creation, performance evaluation, and scheme optimization. Then with the application of relational database, the case database of high-rise structures is constructed, the structure form-selection designing methods such as the smart algorithm based on CBR, DM, FINS, NN and GA is presented, and the original forms system of this method and its general structure are given. CBR and DM are used to generate scheme candidates; FINS and NN to evaluate and optimize the scheme performance; GA to create new structure forms. Finally, the application cases are presented, whose results fit in with the real project. It proves by combining and using the expert intelligence, algorithm intelligence and machine intelligence that this method makes good use of not only the engineering project knowledge and expertise but also much deeper knowledge contained in various engineering cases. In other words, it is because the form selection has a strong background support of vast real cases that its results prove more reliable and more acceptable. So the introduction of this method provides an effective approach to improving the quality, efficiency, automatic and smart level of high-rise structures form selection design.
