The MaCAD is a unique online programme training a new generation of architects, engineers and designers ready to develop skills into the latest softwares, computational tools, BIM technologies and AI towards innovation for the Architecture, Engineering and Construction (AEC) industry.

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THERMAL COMFORT_A predictive model for PMV index

Thermal Comfort Index Prediction // Definition OBJECTIVE: predicting thermal comfort in air-conditioned residential buildings using machine learning algorithms. What is indoor thermal comfort? Thermal comfort is “That condition of mind that expresses satisfaction with the thermal environment” (ISO 7730) People may feel that their surroundings are warm, cold or simply comfortable depending on the thermal state … Read more

EUI Predictor

Predicting Building Energy Performance: A Machine Learning Approach to Energy Use Intensity (EUI) In the world of sustainable architecture and building design, understanding energy consumption is crucial for creating efficient, environmentally responsible structures. Energy Use Intensity (EUI) serves as a fundamental metric that tells us how much energy a building consumes per square meter per … Read more

From Excel Guesswork to Data-Driven Decisions

Building a Production-Ready Property Valuation Model In the landscape of urban development, real estate investment, and computational design, accurate property valuation is essential. Yet, the datasets we rely on are rarely clean, tidy, or immediately ready for predictive use. This blog post unpacks how our team developed a robust, interpretable machine learning model for property … Read more

From Raw Acoustics to Predictive Insights: Modeling Comfort in Architectural Spaces

Introduction In this blog, we walk through the complete data science process applied to a unique challenge: predicting the acoustic comfort index of apartment units based on architectural and environmental features. This includes data preprocessing, feature engineering, model training, interpretation, and tuning—with tools like XGBoost, Neural Networks, and SHAP for explainability. The workflow is split … Read more

CarbonAI

Bridging Carbon estimations in early design stages The built environment accounts for nearly 40% of global carbon emissions, the urgency to integrate sustainability into architectural and engineering processes has never been greater. Yet, decisions made in the earliest stages of building design often lock in the majority of a structure’s lifetime carbon footprint, long before … Read more

Fire Risk Prediction

Fire Spread Prediction in Building Layouts Project Goal The goal is to predict fire safety risk at the unit level in a building. We are conducting a fire spread analysis for building layouts by examining spatial, structural, and material features. The objective is to model and understand how fire propagates through different parts of a … Read more