ASSESSMENT OF SUSTAINABILITY OF CONCRETE PRODUCTION USING LIFE CYCLE ASSESSMENT AND ARTIFICIAL NEURAL NETWORK MODELS

Concrete is one of the most widely used construction materials, but its production has significant environmental impacts. The sustainability of concrete production can be assessed using life cycle assessment (LCA) to evaluate the environmental impacts of concrete production from cradle to grave. However, the complexity of LCA analysis and the need for large amounts of data make it challenging to perform, especially for small or medium-sized enterprises. Artificial neural network (ANN) models offer a datadriven approach for assessing the sustainability of concrete production by analysing large datasets and identifying patterns in the data. This research paper aims to discuss the development and application of ANN models for assessing the sustainability of concrete production using LCA, its advantages, limitations, and potential applications. Concrete is a widely used construction material, but its production has significant environmental impacts. To address these concerns and promote sustainability. This paper presents an assessment of the sustainability of concrete production using a combination of life cycle assessment (LCA) and artificial neural network (ANN) models. The LCA methodology provides a comprehensive approach to evaluate effect with concrete production throughout its entire process. It considers factors such as raw material extraction, energy consumption, greenhouse gas emissions, and waste generation. On the other hand, ANN models, inspired by the human brain's neural networks, offer predictive capabilities based on historical data and input parameters. The objective of this study is to integrate LCA and ANN models to assess the sustainability of concrete production. By combining these methodologies resource consumption. This assessment will enable the identification of key areas for improvement and optimization in concrete production practice The findings from the LCA analysis highlight the significant environmental impacts associated with concrete production, emphasizing the need for sustainable practices. The ANN models showcase their accuracy in predicting sustainability indicators, enabling informed decision-making. By incorporating these approaches, concrete producers can enhance their understanding of the environmental implications and optimize their production processes. The results of this assessment contribute to the existing knowledge on sustainable concrete production practices. The combination of LCA and ANN models offers a robust framework for assessing and promoting sustainability in the concrete industry. This research provides valuable insights for researchers, policymakers, and industry stakeholders, emphasizing the importance of adopting holistic approaches and advanced computational techniques to drive sustainable concrete production.