Practical study on the application of embedded machine learning method in generative design at block level
China's urban development is transformed from rapid growth to optimization of built area,paying more attention to the improvement of spatial quality.Accordingly,urban design work also needs to be more refined and high-quality.The form of urban blocks is closely related to the spatial quality.Block morphological design is a process of creating urban space at block scale.Compared with macro-scale studies,block-scale morphological studies have the characteristics of scale,diverse and discrete,which not only highlight the subtle relationship of urban spatial forms,but also reflect the characteristics and styles of design,which brings challenges to generative models based on preset rules.In the traditional design method,the design of urban form is realized by the designer according to the aesthetics rules and experiences.On the one hand,it is limited by subjective cognition,and on the other hand,it is difficult to define the subtle relationship of spatial form.The development of new data environment and artificial intelligence technology has brought a new vision for the form generation of block scale.In urban design,the generation of block form with the help of intelligent tools is a typical application to assist design decisions,which provides a basis for creating reasonable space forms and enables designers to propose more effective design strategies.The existing intelligent generation methods mainly have three approaches:1)parametric modeling,generating results according to morphological rules;2)form-flow reasoning,through the establishment of form-flow mechanism,according to the flow elements to deduce specific forms;3)image generation algorithm,which directly generates design schemes based on image samples through deep learning.However,due to the lack of learning process of block-scale morphological law,it is difficult to deal with complex and precise spatial morphological relations and generate satisfactory morphological results.On the basis of the existing generative methods,this study draws on the embedded system theory,and proposes a comprehensive method to optimize the block form generation results by embedding machine learning training modules(referred to as"embedded machine learning method").The proposed method is based on the demand analysis for block form design,and is explained from three aspects:morphological knowledge extraction,gain innovation and elastic generation.It is deduced that multiple optimized machine learning modules need to be integrated in the embedded form on the existing generation system,so that the system can meet the requirements of the generative algorithm for block form design.Increase the flexibility and adaptability of the model and promote the man-machine collaborative design process.This method includes five elements:high-quality training data,balanced model with complexity and performance,feature engineering and model optimization capabilities,independently running learning modules,and support for open interface and deployment,to achieve an artificial neural network with increased flexibility and controllability in traditional generation rules.Training samples and models can be freely combined according to design requirements.The generation effect of the existing algorithm is improved.This method also increases the flexibility of the design process.Designers can configure appropriate training datasets according to the needs of the site,and embed the results into the generative system through pre-training method,so as to realize fast and convenient three-dimensional form generation and assist the block form design process.In this paper,an exploratory practice case is presented,which integrates the learning module of the architectural texture characteristics of the riverfront area in the city into the block form generation model.The research process of this case is described from the aspects of model objectives,data and training process.The morphological data of waterfront urban areas of the same type are taken as samples,and the machine learning and image transfer learning models are embedded into the generative algorithm of block morphology.By comparing the generated results,the improvement of this method over the traditional generative method is discussed.The results show that the embedded machine learning method can effectively improve the feature details of the three-dimensional form,generate a design scheme that is more suitable for the characteristics of the site,and has excellent optimization performance in the view of the riverside landscape,which brings beneficial enlightenment for designers.The paper presents the future development potential of embedded machine learning,including multi-modal data fusion machine learning,flexible and diverse combination of algorithm modules,and higher quality man-machine collaborative design processes.The author believes that combining the advantages of artificial intelligence with the classical urban model to create a new flexible integrated model to cope with the complex and diverse design environment can provide new ideas and possibilities for the problem of form creation in block or urban design,and hopes to promote more discussions and practices on embedded machine learning methods and enrich the theory and technology in urban design practice.