{"id":3283,"date":"2023-11-02T08:37:39","date_gmt":"2023-11-02T07:37:39","guid":{"rendered":"https:\/\/teselainnova.com\/proyecto\/tesela-european-project-av-ai-lable\/"},"modified":"2024-03-25T20:28:38","modified_gmt":"2024-03-25T19:28:38","slug":"tesela-european-project-av-ai-lable","status":"publish","type":"proyecto","link":"https:\/\/teselainnova.com\/en\/project\/tesela-european-project-av-ai-lable\/","title":{"rendered":"EUROPEAN PROJECT. av-AI-lable"},"content":{"rendered":"\n

Selection of UHPC cementitious materials by Artificial Intelligence and Machine Learning <\/h4>\n\n
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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).<\/p>\n<\/blockquote>\n\n

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About av-AI-lable<\/h3>\n\n\n\n

Specific objectives and technological applications<\/h5>\n\n\n\n

The current approach to developing a UHPC or cementitious material consists of three steps. <\/p>\n\n\n\n

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. <\/p>\n\n\n\n

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.<\/p>\n<\/div>\n<\/div>\n\n

Main benefits of the project<\/h5>\n\n
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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:<\/p>\n

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

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:<\/p>\n

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

Download the av-AI-lable information leaflet in your language<\/h2>\n\n