Architecture Engineers Award

In response to accelerating global challenges such as resource depletion, climate risk, and urban health inequality, architectural design is undergoing a fundamental shift from experience-based approaches to intelligence-driven practices. Artificial Intelligence for Sustainable Architectural Design (AI4SAD) has emerged as a critical catalyst in this transformation. This study provides the first comprehensive mapping of AI4SAD research, examining how AI is being applied across architectural design stages, sustainability objectives, and algorithmic domains. Scope and Methodology of the Systematic Review The research is grounded in a systematic review of 408 scholarly studies, offering a robust spatiotemporal overview of global AI4SAD development. By analyzing patterns across regions, time periods, design phases, and sustainability targets, the study establishes a structured evidence base that reveals dominant research trajectories as well as underexplored areas within AI-driven sustai...

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...

  Architectural heritage is characterized by complex structural systems, long-term material aging, and evolving usage patterns, which result in diverse and often unpredictable damage mechanisms. These challenges are further compounded by external environmental factors that are difficult to control, increasing uncertainty in conservation efforts. This study positions artificial intelligence (AI) as a transformative tool capable of addressing these complexities by enabling data-driven, adaptive, and efficient heritage protection strategies. Characteristics and Challenges of Architectural Heritage Protection The paper highlights how architectural heritage differs fundamentally from contemporary buildings in terms of materials, construction techniques, and vulnerability to environmental stressors. Damage patterns in heritage structures often develop slowly and nonlinearly, making early detection and intervention difficult. These characteristics necessitate advanced analytical tools ...

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...

This study investigates the application of six machine learning regression models to predict building performance in a residential unit located in Sari, Iran. Using a calibrated EnergyPlus model and three years of utility data, the research evaluates primary energy consumption, emissions, indoor air quality, thermal comfort, and visual discomfort. The aim is to enhance sustainable design decision-making by comparing the efficiency and accuracy of different models. Machine Learning Models in Building Performance The research evaluates Random Forest, K-Nearest Neighbors, Support Vector Regression, Artificial Neural Network, Extreme Gradient Boosting, and Linear Regression. Each model is tested against real-world performance indicators, highlighting their predictive strength and weaknesses in handling multidimensional building datasets. Dataset and Methodology A synthetic dataset of 1,826 configurations with 25 input variables was developed using EnergyPlus. The dataset was split in...