AI System for Predicting Embodied Carbon (ASPEC) in Infrastructure Projects

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Title
AI System for Predicting Embodied Carbon (ASPEC) in Infrastructure Projects

CoPED ID
045eb06b-ed28-4ec3-94b9-42076ffadf1b

Status
Active


Value
£1,122,992

Start Date
Nov. 1, 2020

End Date
Oct. 31, 2022

Description

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The UK government has set 2050 target for achieving a net zero greenhouse gas emission (Department for Business, Energy & Industrial Strategy, 2019). In the construction and infrastructure sector of the economy, the government has also set a target of a 50% reduction in carbon emissions, through the Construction 2025 strategy to transform the sector (UKGBC, 2017). Following the BREEAM sustainability requirements, construction organisations and clients have continuously worked to achieve the carbon emission reduction target set by the government through the use of various embodied carbon estimation techniques. The construction organisations over the years learnt a lot about embodied carbon performance on projects. These lessons learnt have continue to influence the way organisations address the issue of embodied carbon on infrastructure projects. The current methods for calculating embodied carbon of infrastructure projects is tedious and requires a lot of man-hour (Embley, 2019). In addition, existing methods do not provide design support for driving down the embodied carbon and carbon footprint of projects.

This project will develop an AI system for Embodied Carbon Analytics and prediction of infrastructure projects based on BIM designs, materials carbon data and lessons learnt on past projects. The embodied carbon data of past projects will be used to train and develop deep learning models (such as deep neural network, convolutional neural network). Advanced big data analytics techniques will be used to develop the embodied carbon analytics platform.

The proposed system will have the following components:

1\. AI-based Embodied Carbon Calculator(ECC): This subsystem will use historic embodied carbon data from previous construction projects to develop machine learning models to predict the embodied carbon based on construction project design. Parametric data from the construction project design will be used to train and develop the machine learning models.

2\. Embodied Carbon Analytics and Simulation Platform(ECAS): The ECAS will provide a platform where what-if analysis of embodied carbon of projects will be carried out with the aim of identifying alternative design specifications that reduce embodied carbon of the project. The tool will use advanced big data analytics method that include predictive analytics, prescriptive analytics and visualisation.

Tim Reeve PM_PER
Morgan Hambling PM_PER

Subjects by relevance
  1. Emissions
  2. Infrastructures
  3. Construction
  4. Carbon
  5. Projects
  6. Climate changes
  7. Machine learning
  8. Computer programmes
  9. Big data
  10. Construction projects
  11. Greenhouse gases
  12. Enterprises

Extracted key phrases
  1. Carbon emission reduction target
  2. Construction project design
  3. AI System
  4. Carbon analytic platform
  5. Material carbon datum
  6. Previous construction project
  7. Infrastructure project
  8. Carbon estimation technique
  9. Carbon footprint
  10. Carbon performance
  11. Advanced big datum analytic method
  12. Past project
  13. Embodied Carbon Analytics
  14. Carbon Calculator(ECC
  15. Construction organisation

Related Pages

UKRI project entry

UK Project Locations