Sensor-driven durable low carbon concrete structure

传感器驱动的耐用低碳混凝土结构

基本信息

批准号：
2886407
负责人：
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：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2886407
关键词：
Sensor driven durable low carbon

项目摘要

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.

由于该项目的行业合作伙伴国家公路（National Highways）目前正在寻找耐用的传感器国家高速公路国家高速公路（National Highways），以便在英国低碳混凝土结构中发现耐用的传感器。托德维斯（Todevise）是对同一解决方案的解决方案，由剑桥大学的阿比尔·塔巴巴（Abir al-Tabbaa）教授领导的研究小组目前正在促进Concrefy开发的现有实时混凝土耐用性传感器的能力，该传感器针对LowCarboncccccccccccccctrete类型以及英国的暴露和损害条件。作为博士学位学生奖学金的一部分，我建议将整个项目归类为执行的三个阶段，旨在解决（i）研究，（ii）进步和（iii）在英国低碳混凝土结构中安装实时混凝土耐用性传感器。这需要详细研究道路结构，低碳混凝土类型以及英国高速公路的暴露和损害条件。根据初步研究，我建议使用传感器使用的模型来修改该模型来检测混凝土山马力点，然后与其他相关传感器进行补充，以增加数据的鲁棒性。此外，为了合并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可以进一步帮助早期发现混凝土损害。因此，这项研究建议提出了解决方案，并以实现上述目标的方法来完成。