Concrete production, a significant contributor to carbon dioxide emissions, is undergoing a transformative shift to reduce its environmental impact. In Sweden, where cement production accounts for 3-4% of the country's total CO2 emissions, the focus is on developing climate-improved concrete by incorporating alternative materials such as slag or fly ash. This shift poses challenges, altering critical properties of the concrete. To address this, a pioneering project aims to leverage Artificial Intelligence (AI) to enhance the predictability of climate-improved concrete's temperature and strength, paving the way for optimized construction practices and reduced carbon footprint.
The project's core objective is to deploy machine learning (ML)-based methods to develop algorithms predicting the temperature and strength of climate-improved concrete. These algorithms will be integrated into smart tools, providing decision support for concrete manufacturers and contractors. The project aims to achieve this by evaluating various AI methods for strength prediction, developing models for long-term strength forecasting, exploring AI practices for temperature forecasting, and comparing them with existing tools. Additionally, the project seeks to analyze and suggest algorithms for notification/control based on current temperature forecasts and explore alternative methods like Neural Networks for a more sustainable future aligned with the 2030 Agenda.
The primary challenge addressed by the project is the need to reduce CO2 emissions in the cement and concrete industry. By replacing some cement with alternative filler materials, the industry can significantly decrease its carbon footprint. However, changes in critical properties, such as early temperature and strength development, pose obstacles. The project aims to overcome these challenges by utilizing AI techniques to understand and forecast the impact of different parameters on temperature and strength development, thus promoting the use of climate-improved concrete.
Concrete, a widely used building material globally, contributes significantly to carbon dioxide emissions. In Sweden, efforts are underway to produce climate-improved concrete by replacing a substantial portion of cement with filler materials, resulting in a lower carbon footprint. However, challenges arise, including slower strength development and increased sensitivity to cold weather. This hinders the widespread use of climate-improved concrete, especially during colder periods, impacting the industry's emission reduction goals.
Traditional forecasting models based on temperature-strength relationships are insufficient for climate-improved concrete. AI, particularly ML, offers a powerful solution to identify correlations in vast datasets and develop new algorithms for forecasting and decision support. By combining ML with Internet of Things (IoT) systems, the project aims to create smart tools that monitor and forecast concrete properties during curing, enabling optimized and increased use of climate-improved concrete.
Contributions to Agenda 2030
The project aligns with two key goals of the United Nations' Agenda 2030: Goal 9 - Industry, Innovation and Infrastructure, and Goal 13 - Climate Action. Goal 9 focuses on building resilient infrastructure, promoting sustainable industrialization, and fostering innovation. The project contributes by developing innovative AI-based tools for optimizing the use of climate-improved concrete, thus supporting sustainable development and economic growth in the construction industry. Goal 13 emphasizes urgent action to combat climate change. The project addresses this by actively working to reduce carbon dioxide emissions in the cement and concrete industry through the development and increased use of climate-improved concrete.