A systematic review of methods for building layout plans generation

This paper formulates the building layout generation problem and its constraints. The most common solutions are given, the difficulties faced by designers are described. Due to the high labor intensity of classical approaches to solving this problem, the necessity of reviewing models, methods and systems for generating building layout plans is justified. Based on published literature reviews, three main approaches to solving the problem are highlighted: form grammars, genetic algorithms, and deep learning. For each approach the basic concepts and specifics of their use in the problem under consideration are described, comparative tables of features of models and methods in different works are given: types of grammars (context-free, context-sensitive), initial shapes and transformation rules for shape grammars, algorithms (NSGA-II, SPEA-II), populations and optimization criteria for genetic algorithms, architectures (CGAN, CGLO, Pix2Pix, Pix2PixHD) and characteristics of training samples for deep learning (image sizes, conditional channels, rasterization). Dataset preparation in various works devoted to solving the problem of generating building layout plans are described. We systematize the satisfied constraints of the problem in the considered approaches: spatial (site boundaries, street-road network, building heights and layout density) in all cases and architectural (building morphologies, land use types, architectural style and plans of adjacent territories) for genetic algorithms and deep learning models, and compare the reproducibility of solutions on other sites (cross-validation) and visualization capabilities in different works. The key trends and directions for further research highlighted by the authors of the reviewed studies are identified.