7 citations · 26 across the 8 of their papers we have counts for
6 papers · 1 filter
Studying Buildings Outlines to Assess and Predict Energy Performance in Buildings: A Probabilistic Approach
Zohreh Shaghaghian, Fatemeh Shahsavari, Elham Delzendeh
Building performance is commonly calculated during the last phases of design, where most design specifications get fixed and are unlikely to be majorly modified based on design pro…
A Machine-learning Framework for Acoustic Design Assessment in Early Design Stages
Reyhane Abarghooie, Zahra Sadat Zomorodian, Mohammad Tahsildoost +1
In time-cost scale model studies, predicting acoustic performance by using simulation methods is a commonly used method that is preferred. In this field, building acoustic simulati…
A machine-learning framework for daylight and visual comfort assessment in early design stages
Hanieh Nourkojouri, Zahra Sadat Zomorodian, Mohammad Tahsildoost +1
This research is mainly focused on the assessment of machine learning algorithms in the prediction of daylight and visual comfort metrics in the early design stages. A dataset was…
Learning Geometric Transformations for Parametric Design: An Augmented Reality (AR)-Powered Approach
Zohreh Shaghaghian, Heather Burte, Dezhen Song +1
Despite the remarkable development of parametric modeling methods for architectural design, a significant problem still exists, which is the lack of knowledge and skill regarding t…
Application of Classification and Feature Selection in Building Energy Simulations
Fatemeh Shahsavari, Zohreh Shaghaghian
Building energy performance is one of the key features in performance-based building design decision making. Building envelope materials can play a key role in improving building e…
Towards Learning Geometric Transformations through Play: An AR-powered approach
Zohreh Shaghaghian, Wei Yan, Dezhen Song
Despite the excessive developments of architectural parametric platforms, parametric design is often interpreted as an architectural style rather than a computational method. Also,…