A
Amin Hammad
Researcher at Concordia University Wisconsin
Publications - 177
Citations - 3594
Amin Hammad is an academic researcher from Concordia University Wisconsin. The author has contributed to research in topics: Computer science & Building information modeling. The author has an hindex of 28, co-authored 163 publications receiving 2654 citations. Previous affiliations of Amin Hammad include Concordia University & University of Tokyo.
Papers
More filters
Journal ArticleDOI
Knowledge-assisted BIM-based visual analytics for failure root cause detection in facilities management
TL;DR: This paper investigates a knowledge-assisted BIM-based visual analytics approach for failure root-cause detection in FM and utilizes BIM visualization capabilities to provide FM technicians with visualizations that allow them to utilize their cognitive and perceptual reasoning for problem solving.
Journal ArticleDOI
Automated Code Compliance Checking for Building Envelope Design
TL;DR: A hierarchical object-based representation of simulation results is proposed as an extended building information model (EBIM) to describe the attributes of a building and its subsystems and building codes are seamlessly linked with the compliance checking software.
Journal ArticleDOI
Simulation-Based Multi-Objective Optimization of institutional building renovation considering energy consumption, Life-Cycle Cost and Life-Cycle Assessment
TL;DR: A genetic algorithm (GA) coupled with an energy simulation tool is used for simultaneously minimizing the energy consumption, LCC, and environmental impact of a building while providing an efficient method to deal with the limited renovation budget.
Journal Article
Lifecycle management of facilities components using radio frequency identification and building information model
Ali Motamedi,Amin Hammad +1 more
TL;DR: This research proposes permanently attaching RFID tags to facility components where the memory of the tags is populated with accumulated lifecycle information of the components taken from a standard BIM database.
Journal ArticleDOI
Predicting movements of onsite workers and mobile equipment for enhancing construction site safety
TL;DR: Novel Kalman filters for predicting the movements of the workers and mobile equipment on the construction sites are proposed that take the positions of the equipment and workers estimated from multiple video cameras as input, and output the corresponding predictions on their future positions.