Sensor-driven durable low carbon concrete structure

由于道路防腐蚀的维护成本很高，该项目的行业合作伙伴国家公路公司（National Highways）目前正在英国寻找耐用的传感器，用于早期检测低碳混凝土结构的腐蚀。为了设计一种解决方案，剑桥大学的Abir Al-Tabbaa教授领导的研究小组目前专注于提高混凝土公司为低碳混凝土类型和英国的暴露和损坏条件开发的现有实时混凝土耐久性传感器的能力。作为博士研究生的一部分，我建议将整个项目分为三个执行阶段，旨在解决(I)研究，（ii）推进，（iii）在英国低碳混凝土结构中安装实时混凝土耐久性传感器。这需要对道路结构、低碳混凝土类型以及英国高速公路的暴露和损坏情况进行详细研究。进一步，在前期研究的基础上，我建议修改传感器用于检测混凝土损伤的模型，然后补充其他相关传感器，以增加数据的鲁棒性。此外，为了结合不断变化的气候条件并动态更新调整后的模型，我建议使用机器学习算法，这可以进一步帮助早期发现混凝土损伤。因此，本研究计划提出了解决方案，并概述了实现上述目标的方法。

Owing to the high maintenance costs of roads against corrosion, National Highways, the industry partner of this project,is currently looking for durable sensors for the early detection of corrosion in low-carbon concrete structures in UK. Todevise a solution to the same, the research group led by Prof Abir Al-Tabbaa at University of Cambridge is currentlyfocusing on advancing the capabilities of existing real-time concrete durability sensor developed by Concrefy for lowcarbonconcrete types and the exposure and damage conditions of UK. As a part of the PhD studentship, I propose todivide the entire project into three phases of execution which aim to address the (i) study, (ii) advancement, and (iii)installation of the real-time concrete durability sensor in low-carbon concrete structures in UK. This requires a detailedstudy of the road structures, low-carbon concrete types, and exposure and damage conditions of the UK highways.Further, based on the preliminary study, I propose to modify the models used by the sensor to detect concrete damageand then supplement it with other relevant sensors to increase the robustness of the data. Additionally, to incorporate thechanging climate conditions and dynamically update the adjusted models, I propose using machine learning algorithmsthat can further help in the early detection of concrete damage. This research proposal thus proposes the solutions andoutlines the methodology to achieve the above-mentioned objectives.