Architecture Engineers Award

Building Information Modeling (BIM) has become a central workflow in the architecture, engineering, and construction (AEC) industry, yet 2-dimensional (2D) architectural drawings remain indispensable for documentation, communication, and construction execution. Despite BIM’s advantages, generating 2D drawings from BIM models still requires substantial manual effort, with current practices demanding significant additional time for conversion. This research addresses this inefficiency by proposing an automated, deep learning–based framework capable of transforming BIM models into accurate and stylistically diverse 2D architectural drawings. Persistent Role of 2D Drawings in BIM-Based Workflows Although BIM offers rich parametric and information-embedded models, 2D drawings continue to dominate regulatory submissions, on-site coordination, and professional communication. The reliance on manual or semi-automated conversion processes introduces redundancy and inefficiency into BIM workfl...

The increasing demand for old building renovation presents complex spatial, functional, and technical challenges that exceed the capacity of conventional design workflows. In response, this paper investigates the application of deep reinforcement learning (DRL) to enable automated architectural space composition under predefined built environment constraints. By integrating artificial intelligence into architectural design processes, the study positions computational intelligence as a strategic tool to improve efficiency, adaptability, and decision-making in renovation-oriented architectural practice. Reinforcement Learning Framework for Architectural Design The research establishes a dedicated reinforcement learning platform centered on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. This framework enables multiple agents to collaboratively explore spatial configurations while responding to shared environmental constraints. By framing architectural space compo...