Architecture Engineers Award

  High-rise office buildings frequently experience airflow stagnation zones on windward façades, particularly at mid-height levels where wind streams divide upward and downward. These stagnation effects limit natural ventilation potential and increase reliance on mechanical cooling during warm seasons. This study investigates how parametric façade aperture design can strategically enhance airflow distribution and reduce cooling loads in high-rise office buildings. Focus on Stagnation-Level Floor and Design Hypothesis The research concentrates on the floor intersecting the façade stagnation point, where airflow dynamics are most constrained. It is hypothesized that optimized aperture geometry and spatial distribution can redirect pressure differentials to improve indoor ventilation performance and thermal comfort, thereby reducing cooling energy demand without mechanical intervention. Multi-Stage Methodological Framework A multi-stage methodology was implemented integrating com...

The accelerating global transition toward carbon neutrality demands rapid and reliable energy prediction tools for innovative building systems such as Building-Integrated Photovoltaic (BIPV) modular buildings. Conventional physics-based simulation methods, while accurate, are computationally intensive and unsuitable for real-time design optimization. This study proposes a novel machine learning-based rapid energy prediction methodology tailored specifically to the thermal and geometric characteristics of modular BIPV-integrated buildings. Feature Engineering for Modular Building Representation A comprehensive feature engineering framework was developed to capture the distinctive attributes of modular construction. The approach incorporates six-surface thermal property encoding, geometric parameters, and detailed solar irradiance calculations to represent envelope exposure and inter-module interactions. This structured encoding ensures that key thermal behaviors and photovoltaic infl...

Material selection is a central challenge in sustainable building design, particularly for high-rise structures where structural safety and environmental performance often conflict. Conventional Building Information Modeling (BIM) workflows typically separate structural analysis from environmental assessment, making it difficult to evaluate trade-offs efficiently during early design stages. This study addresses this gap by introducing an integrated methodology that simultaneously evaluates structural stability and embodied carbon, enabling informed, performance-driven material decisions. Limitations of Conventional BIM-Based Design Approaches Traditional BIM workflows treat structural performance and environmental impact as parallel but disconnected processes. This separation restricts designers’ ability to iteratively explore material combinations and geometric variations, especially when assessing low-carbon alternatives. As a result, design teams may overlook optimal hybrid solut...

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...