AI in architecture and engineering from misconceptions to game-changing prospects

Abstract

Artificial intelligence invades our lives and professions at an ever-increasing pace and intensity. Architecture, engineering, construction, and operation of the real estate have been joining the trend only timidly and belatedly. The paper overviews the basic concepts, methods, general background, and results of artificial intelligence in architecture to date, discusses the achievements and prospects, and concludes the perspectives on the deployment of machine learning in the field. The record of some of the most recent “famous achievements” in the field is set straight and challenged, the flawed idea of a (truly) creative potential of the technology is debunked. Its roots equidistributed both in a farsighted vision of the next workflow of both productive and creative architectural and engineering designing, and construction and real estate management on the one hand and state-of-the-art machine learning on the other, an ambitious though realistic blueprint for R&D of AI-fostered architectural creativity, building design, planning, and operation is tabled for discussion. The attention turns to open-source patterns platforms, generative patterns processing, generative pre-design, parametric evaluation and optimization, latest achievements in machine learning building on reinforcement learning, imitation-based learning, learning a behavior policy from demonstration, and self-learning paradigms zooming in on the design-development processes instead of only on their results. Leveraging the objectivity of assessments and streamlining workflows, artificial intelligence promises to unleash true architectural creativity and leverage the productivity and efficiency of the design, planning, and operation processes.