Emmanuel Boateng
A personal site

Projects, risk, and the people inside them

2015–present

I study the peopleinside complex projects,and build toolsthat take their side.

murmuration · follows your cursor
§ 00 · Statement

For a decade, across five institutions in Australia and Ghana, I have worked on the people side of complex, high-pressure projects: what they ask of the people delivering them, and what holds those people up. What I am working on now: mental health and psychosocial risk in project-based work.

Dr Emmanuel B. Boateng
Emmanuel B. Boateng · researcher & builder
01 · Listen

Deep learning, neural networks, and text mining, pointed at material that rarely reaches a formal dataset: what people say on site, on social media, in the open text of a survey. I review this work for nine journals, and was the Int’l Journal of Construction Management’s Best Reviewer in 2022.

Read the evidence →
02 · Test

Construction and the built environment are the field, but the questions travel: project delivery and cost, public health, energy, and higher education all appear in the record.

Follow the inquiry →
03 · Build

Research that stops at the paper isn't finished. The evidence gets published, then it gets built: PsycheGuard for the people on site, feedforward agents for students, a cost model now used in industry.

See the builds →
19
Journal articles
1,270
Citations
h-index 16
A$141k
Grants as PI / Co-I
12
International collaborators
§ 01 · Lines of inquiry

Work

Five threads
01

Safety and wellbeing on site

How work stays safe and workers stay well: safety culture, behaviour and interventions on construction and high-risk sites, measured with validated indices and instruments. Home of PsycheGuard, my app for construction workers' psychosocial wellbeing.

  • 2025 Analyses social media conversations to find out how the construction industry talked about and responded to COVID-19 prevention measures.
  • 2021 Reviews the literature to establish how resilience engineering has been conceptualised and measured for organisational safety.
  • 2021 Systematically reviews studies to see how high reliability organisations have been defined and measured in health care safety research.
11 papers · 2016–2025
02

Delivering projects, cost and value

Tender pricing, value management and procurement, and the competencies that decide whether public-private partnerships and international joint ventures hold together. Mostly in developing-country contexts.

  • 2024 Builds and statistically tests a model of the competencies public and private partners each need to reach genuinely win-win infrastructure deals.
  • 2023 Trains deep neural networks on 198 Hong Kong green building projects to predict final cost and duration from early project data, then ships them as a web app.
  • 2022 Reviews prior research to propose the first model linking how joint ventures are controlled to how they perform.
11 papers · 2015–2024
03

Public voice and the classroom

Text mining and sentiment analysis of what people say in public, unprompted, on energy, higher education and history. Alongside it, evaluation of the tools now arriving in the classroom.

  • 2025 Mines public social media posts to map what Africans are saying about decolonising their universities, using topic modelling and sentiment analysis.
  • 2024 Applies sentiment analysis to social media data to gauge public opinion on retrofitting existing buildings toward net-zero energy performance.
  • 2024 Studies YouTube videos and viewer comments about Ghana's slave castles to see how the public online recalls and interprets the Transatlantic Slave Trade.
5 papers · 2022–2025
04

Sustainability and the built environment

Carbon intensity, building emissions, energy demand and the uptake of sustainable construction. You cannot lower a footprint you cannot yet measure, so the modelling comes first.

  • 2021 Benchmarks several machine learning models against each other for predicting carbon emissions from buildings.
  • 2020 Compares four machine learning algorithms for forecasting Australia's carbon emissions and ranks them on accuracy and efficiency.
  • 2020 Builds and deploys neural network models to forecast energy demand for Australia, China, France, India and the USA using 1980-2015 quarterly data.
5 papers · 2017–2021
05

Foresight for health and environment

Forecasting risks before they arrive: projecting the global burden of hypertension, diabetes and obesity, and predicting how persistent contaminants move through soils, so planners can act early.

  • 2023 Uses time-series forecasting and clustering on WHO data to project hypertension prevalence to 2040 by sex and country.
  • 2021 Measures how the pollutant PFOS binds to 114 Australian and Fijian soils and, for the first time, trains neural networks to predict that binding from soil properties.
  • 2020 Forecasts diabetes and obesity prevalence to 2030 across 183 countries and clusters countries by their combined risk pattern.
3 papers · 2020–2023
§ 02 · From the record

Recent papers

5 of 35 outputs · full record & networks →
→ All 35 outputs · topic map · collaboration network