Architecture Engineers Award

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...

Climate change has intensified the challenges associated with maintaining indoor–outdoor thermal comfort, prompting the need for advanced, data-driven design approaches. This study introduces an integrated framework combining Multiobjective optimization (MOO) and explainable machine learning (ML) to analyze how spatial morphology affects indoor–outdoor thermal comfort (IOTC). By merging global optimization capability with transparent interpretability, the framework supports climate-responsive architectural decision-making and delivers insights that improve both design quality and environmental performance. Multiobjective Optimization for Thermal Comfort The framework employs a genetic algorithm (GA)–based MOO model to optimize nine morphological parameters related to building and courtyard forms. These parameters serve as decision variables for the simultaneous optimization of predicted mean vote (PMV) and the universal thermal climate index (UTCI) during contrasting seasonal conditi...

  Educational buildings breathe life into growing minds, yet they often struggle to balance two essential needs: conserving energy and maintaining healthy indoor air quality. The study behind this work steps into that tension, exploring how traditional HVAC control methods fall short when faced with shifting indoor conditions and rising sustainability pressures. By pairing experimental testing with advanced machine learning models, the research aims to create smarter, adaptive systems that reduce energy consumption while protecting occupant health. This introduction frames the need for innovation, outlining why educational spaces demand data-driven solutions capable of reacting in real time. Machine Learning Integration for HVAC Optimization The research explores how models such as RNN, LSTM, GRU, and CNN can interpret vast streams of environmental data and turn them into actionable predictions. With over 35,000 real-world records, the study demonstrates how these models learn ...

  In the global effort toward achieving carbon neutrality, the building sector stands as one of the largest contributors to energy consumption and emissions. Enhancing the energy efficiency of buildings, particularly during their operational phase, has therefore become a central focus of modern architecture and sustainability research. The integration of Artificial Intelligence (AI) and Digital Twin technologies presents a promising path forward, enabling data-driven insights, real-time control, and predictive energy management. This research investigates the role of AI-driven Digital Twins in optimizing building performance, aligning technological innovation with sustainable design principles. Research Motivation and Objectives Buildings account for a significant portion of global energy demand, and inefficiencies in operation often stem from the lack of real-time monitoring and predictive control systems. Traditional energy management approaches fall short in dynamically adapt...

  Generative design has rapidly become a cornerstone of modern architectural and engineering innovation, enabling automated exploration of spatial layouts through computational intelligence. However, while these models efficiently generate diverse design options, they often lack integrated systems for evaluating performance metrics such as energy use, carbon impact, and spatial efficiency. This study bridges that gap by introducing a fully automated, image-based performance evaluation framework that accelerates the transition from concept to simulation-ready layouts. The approach redefines the workflow of early-stage design, providing instant sustainability insights that support low-carbon and high-performance outcomes. Image-Based Performance Evaluation Framework Traditional performance evaluation methods rely heavily on manual modeling, which is both time-consuming and prone to human error. To overcome this, the research introduces an automated image-to-simulation (Image2Sim)...

  Effective daylight management and visual comfort in office spaces remain crucial for occupant well-being and productivity. Traditional shading systems often fail to adapt to dynamic environmental conditions and individual preferences, leading to discomfort and energy inefficiency. This research explores AI-driven adaptive façades as a solution, integrating real-time control algorithms and predictive modeling to enhance indoor lighting quality and optimize energy usage. Problem Statement Current shading solutions in office environments are typically limited to static or manually adjustable systems. These fixed geometries cannot respond dynamically to variations in sunlight, glare, or occupant requirements. The lack of adaptability restricts optimal daylight utilization and visual comfort, highlighting the need for intelligent, real-time façade control systems that can respond to changing conditions. Methodology The study implements a real-time shading control algorithm that ...

  Ensuring both energy efficiency and indoor air quality (IAQ) in educational buildings has become a vital concern in sustainable design. As schools and universities grow increasingly tech-integrated, maintaining a balance between energy consumption and healthy indoor environments is challenging. This study explores how Machine Learning (ML) can bridge that gap — providing intelligent HVAC control, data-driven insights, and environmentally conscious solutions that make classrooms greener and healthier. The Challenge of Balancing Energy and Air Quality Traditional energy management systems in educational institutions often prioritize cost-saving over air quality, leading to poor ventilation and occupant discomfort. Conversely, over-ventilation wastes energy and increases operational costs. This research addresses this dual challenge, emphasizing the necessity for integrated ML solutions capable of optimizing both aspects simultaneously through real-time monitoring and adaptive c...