Lifecycle-Long Environmental Impacts of Supplementary Cementitious Materials (SCMs) in Sustainable Land Regeneration: A Machine-Learning Based Approac

可持续土地再生中补充胶凝材料 (SCM) 的全生命周期环境影响：基于机器学习的方法

• 批准号: 2606439
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2606439
• 关键词: Lifecycle; Long; Environmental; Impacts; Supplementary

This PhD work aims to establish a machine-learning (ML) based prediction model for assessing lifecycle-long environmental impacts of current SCMs available in land regeneration, with a special focus on stabilisation/solidification (S/S) technology. The work will include a state-of-art review on lifecycle assessments (LCAs) of current SCMs, then build up a database as well as develop a novel framework for performing LCAs for S/S project. The database and framework will then formulate foundations of the ML based prediction model to better understand the environmental impacts of SCMs systematically, in order to further promote sustainability of S/S technique and project added values of land remediation project.

这项博士工作旨在建立一个基于机器学习（ML）的预测模型，用于评估土地再生中现有SCM的生命周期环境影响，特别关注稳定/固化（S/S）技术。工作将包括对现有SCM生命周期评估（LCA）的最新进展进行回顾，然后建立一个数据库，并为S/S项目开发一个新的生命周期评估框架。该数据库和框架将为基于ML的预测模型奠定基础，以更好地系统地了解SCM的环境影响，从而进一步促进S/S技术的可持续性和土地修复项目的项目附加值。