Architecture Engineers Award

Over the past few decades, architecture has been profoundly reshaped by unprecedented global transformations, including climate change, pandemics, natural disasters, escalating carbon emissions, and the rapid rise of artificial intelligence. These forces have challenged traditional architectural thinking and raised critical questions about the preparedness of architects to design resilient buildings and cities. This study positions architecture within this evolving context and argues that a lack of comprehensive theoretical grounding limits the discipline’s ability to respond effectively to contemporary challenges. Conceptual Foundations of Architectural Theorization The paper begins by establishing a clear conceptual framework through an in-depth literature review. It defines and distinguishes between the notions of architectural trend , contemporary architectural trend , and theorization . By clarifying these foundational terms, the research sets the intellectual groundwork necess...

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...

  Architectural plan generation using pix2pix-series algorithms presents significant challenges, particularly regarding the absence of domain-specific evaluation standards and limited understanding of how training configurations jointly influence performance. Current architectural AI workflows focus heavily on visual output, often overlooking principle adherence, vectorization quality, and the structural logic of predicted plans. Addressing this gap, the proposed framework introduces a systematic experimental design and a dual-aspect evaluation approach specifically tailored for architectural-like plans, establishing a rigorous foundation for AI-enabled generative design in architecture. Limitations of Existing pix2pix Adaptations in Architectural Design Although pix2pix architectures are widely adopted for map translation and image-to-image tasks, their adaptation to architectural plan generation remains underdeveloped due to the lack of domain-centered evaluation benchmarks. A...

The rapid evolution of artificial intelligence has opened new possibilities for integrating deep learning into architectural workflows, particularly in the transformation of conceptual sketches into structured 3D models. This study introduces an intelligent modeling framework that merges architectural domain knowledge with advanced computer vision systems to overcome the long-standing challenge of the 2D-to-3D domain gap. By embedding the phased, selective, and iterative characteristics of architectural design directly into the workflow, the proposed model aligns AI-driven generation with the precision and logic expected in traditional design practices. This integration forms the foundation for a more adaptive and intelligent design-to-model pipeline within architecture. Deep Learning Integration in Architectural Design The research demonstrates how deep learning tools such as Stable Diffusion, CycleGAN, and Pixel2Mesh can be strategically aligned with architectural reasoning. These mo...