Tha'er Abdalla

School of Architecture

PhD Research Student

Tha'er Abdalla

Full contact details

Tha'er Abdalla
School of Architecture
Arts Tower
Western Bank
S10 2TN

Tha'er is an interdisciplinary doctoral researcher at the School of Architecture of the University of Sheffield. His current research interest spans across multiple disciplines including Architectural Design, Building Physics, Computational Modelling and Artificial Intelligence. Prior to his PhD., Tha'er gained a combined experience in both the architecture industry as a project architect for 6 years and a researcher during his Master's Degree in Architectural Engineering.

Tha'er has a very diverse academic background with broad knowledge in building design, building systems, building performance modelling, and building operation. Additionally, he is conversant in the use of machine learning algorithms and probabilistic distribution functions for developing stochastic predictive models for various building performance measures.

Research Interest:

  1. Predictive Urban/Building Energy and Indoor Air Quality Models
  2. Data-Driven Modelling of Indoor Air Quality
  3. Uncertainty Quantification of High-rise Buildings Infiltration Rates

Educational Qualifications:

M.Sc. Architectural Engineering - Jordan University of Science and Technology, Jordan (2017)

Research interests

Project title: 
Development of a Higher Education Institution Indoor Air Quality Model to Inform Campus Master-planning

Project outline: 
Due to rising levels of air pollution in cities around the world, urban pollution has received much attention in recent years because of its impact on human health. Air pollution is estimated in contributing to about 40,000 early deaths a year in the UK. The epidemiological effects resulting from exposure to air pollution have a high cost to people who suffer from illness and premature death, to the health services and to business. In the UK, these costs add up to more than £20 billion every year (Royal College of Physicians, 2016). Most universities are located in high-density urban areas, and air pollution from urban traffic is known as the most significant contributor to deteriorated indoor air quality. Since people spend long hours indoors working in higher education institutions ‘HEI’, there is a concern of chronic exposure to indoor air pollutants such as PM2.5, PM10, NOx, VOCs etc. This research project seeks answers to three questions: (1) What are the key parameters that determine the indoor are quality of university buildings? (2) Given a university’s existing building stock in its current urban context, how can we model the university’s indoor air quality (IAQ) at the institution stock level? (3) How might university building stock IAQ modelling inform campus master planning? In this research, “university buildings” are buildings designed and constructed for a higher education institution for academic research, education and training. The challenge addressed here is the high level of heterogeneity often observed in a university’s building stock. A methodology is proposed to learn a function that can generate IAQ estimates aggregable to a HEI building stock level in its urban context. In particular, a ‘co-simulation’ technique based on coupling the multi-zone airflow model (CONTAM) with the building energy model (EnergyPlus) will be adapted to perform dynamic IAQ modelling. In addition, Space Syntax analysis via ‘depthMapX’ is for building spatial configuration modelling. Indoor air quality field measurements will be acquired to validate the co-simulation outputs. It is expected that IAQ assessments for different master planning scenarios at an institution stock level can be achieved by providing combined spatial configuration and IAQ modelling data. Finally, the significance of the research will be demonstrated by how the stock IAQ modelling can be applied to assess and inform university campus master planning.

Primary supervisor: 
Dr Chengzhi Peng

Dr Wen-Shao Chang

Date started: 10/01/2018

Research group

People, Environments and Performance