Selection of UHPC cementitious materials by Artificial Intelligence and Machine Learning

The Av-AI-lable project aims to improve the procurement process, design and performance of UHPC mixtures using Artificial Intelligence (AI) and Machine Learning (ML).

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About av-AI-lable

Specific objectives and technological applications

The current approach to developing a UHPC or cementitious material consists of three steps.

The raw materials are mixed in a specific ratio with water, followed by curing of the material (28 days) and measurement of physical and chemical performance tests.

The main objective of the AI / ML tool to be developed in the av-AI-lable project is to improve the existing method in TESELA by decreasing the time, shortening the selection through a better matching of raw materials and predicting the final properties of the final mixture.

Main benefits of the project

The UHPC creation process will benefit from the use of the tool as it will improve and reduce human dependency and avoid time-consuming experimental testing to create new UHPC blend materials. In addition, the tool will facilitate TESELA’s choice of suitable materials for UHPC materials based on their abundance in a geographical area, logistics and their sustainable character. The project will serve to:

(a) Increasing digitisation and digital skills .
(b) Promote the creation of data repositories.
(c) Improve the efficiency and productivity of private services.
(d) Increasing public-private R&D collaboration.

At the same time, the appropriate selection by AI and ML algorithms of the rates of materials to be incorporated in the UHPC materials will have four main environmental impacts:

(1) Processes with lower energy consumption due to the possible incorporation of industrial by-products and recovery of waste from other industries.
(2) Incorporation of materials with a lower carbon footprint than those commonly used.
(3) Reduction of greenhouse gases (GHG).
(4) Reduction of water consumption.

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