Architecture Engineers Award

The construction sector is under increasing pressure to reduce energy consumption and carbon emissions while continuing to support rapid urban development. Green building practices have emerged as a key response, but their effectiveness is often limited by fragmented workflows and insufficient integration of sustainability principles across project stages. This study investigates the role of Building Information Modelling (BIM) as an integrated platform for improving energy performance, resource efficiency, and lifecycle sustainability in green building design. BIM as an Integrated Platform for Sustainable Design Building Information Modelling (BIM) enables the creation of data-rich digital representations of buildings, facilitating collaboration among architects, engineers, and stakeholders. Unlike traditional design methods, BIM allows sustainability considerations to be embedded from early design stages through to operation. By integrating energy analysis, material data, and perfo...

Accurate prediction of indoor operative temperature is essential for improving HVAC system performance, enhancing occupant comfort, and reducing energy consumption in buildings. Operative temperature represents the combined effect of air temperature and the mean radiant temperature of surrounding surfaces as experienced by occupants. In highly controlled environments such as sentry buildings, precise thermal forecasting enables more responsive and energy-efficient climate control strategies. This study proposes a hybrid deep learning framework to improve the accuracy and robustness of indoor operative temperature prediction. Concept of Operative Temperature and Its Role in Thermal Comfort Operative temperature is widely used as a key indicator of indoor thermal comfort because it integrates both air temperature and radiative heat exchange between occupants and surrounding surfaces. Traditional temperature prediction approaches often focus only on air temperature, overlooking the infl...

The construction sector continues to struggle with long-standing challenges related to low productivity, limited innovation, and fragmented organizational structures. While Artificial Intelligence (AI) presents significant opportunities for transformation under the paradigm of Smart Buildings and Construction 4.0 (SBC4.0), its implementation at the organizational level remains insufficiently understood. This study addresses this gap by examining how construction firms adopt AI and the organizational forces shaping these decisions. Theoretical Foundations for AI Implementation The research develops an integrated theoretical framework drawing on institutional theory, the resource-based view, and dynamic capabilities. Institutional theory explains how external pressures influence organizational behavior, while the resource-based view and dynamic capabilities highlight the internal assets and adaptive capacities required for AI adoption. Together, these perspectives provide a comprehens...

With the rapid advancement of digitalization and artificial intelligence , architectural design faces a fundamental limitation: the dominance of geometric representations with insufficient semantic depth. This imbalance restricts the interpretability, reasoning capacity, and transferability of AI-driven design systems across the building lifecycle. Addressing this challenge, the present study explores how architectural geometry models can be semantically enhanced to support AI-based modeling and reasoning, laying the foundation for more intelligent, explainable, and integrated design processes. Limit thinking in Geometry-Centric Architectural Models The study critically examines the limitations of conventional geometric representations across the design–performance–construction chain. Through bibliometric analysis and an extensive literature review, it reveals how geometry-only models fail to capture intent, performance logic, construction rules, and associative knowledge. These shor...

In the global pursuit of carbon neutrality , improving the operational energy efficiency of buildings has become a central challenge for the architecture, engineering, and construction sectors. Advances in Artificial Intelligence (AI) and Digital Twin technologies offer new opportunities to optimize building performance through data-driven decision-making. This research investigates the role of AI-driven Digital Twins in building operations, positioning them as an effective approach for enhancing sustainability, reducing energy consumption, and supporting intelligent control strategies. Design Science Research Framework The study adopts a Design Science Research (DSR) methodology to systematically guide the development, implementation, and evaluation of a digital artifact for energy-efficient building operation. DSR enables the structured creation of a practical solution while ensuring theoretical rigor through iterative problem identification, artifact design, demonstration, and eva...