用于混凝土预测的多尺度图神经网络与微观力学机制

本研究项目旨在开发一种分层图神经网络，通过整合微观力学理论来预测混凝土的力学性能。该项目涉及连接各组成材料的模型，并分析它们之间的相互作用对整体材料行为的影响，利用实验数据与数值模拟结果来验证预测结果的准确性。

About the project Concrete properties depend on interactions between multiscale structures and their interfaces. This project will develop a hierarchical GNN by integrating micromechanics theory. Combining material data, numerical simulations and AI, the research will connect mechanical behaviour across scales and evaluate predictions of concrete mechanical properties. Concrete may appear uniform, but its strength depends on how cement paste, aggregates, and their interfaces interact. Fine aggregate combines with paste to form mortar; coarse aggregate introduces another level of interaction. Thin regions around aggregate particles influence load transfer and where cracks begin to form. Predicting how these connected scales govern strength remains challenging, particularly when measurements of individual constituents are incomplete. This project will complete and extend an emerging hierarchical graph neural network for concrete. You will develop linked models of paste, mortar and concrete to investigate how constituent properties and interfaces influence concrete mechanical properties. The central research challenge is to integrate micromechanics: the theory linking the behaviour of a composite to its constituents. You will investigate homogenisation to estimate overall stiffness from constituent properties, and model stress transfer and interface damage during loading. These relationships will guide how the network combines information between scales. Experimental data and numerical simulations will support development and validation, including checks on intermediate material properties where measurements are available. Comparisons with conventional machine learning and mechanics models will assess whether the combined approach improves prediction and physical consistency. The model will help integrating civil engineering, material science and AI technologies. You will: develop expertise in machine learning and computational mechanics through a concrete materials problem learn to connect material observations, physical theory and numerical models, and critically assess AI predictions build transferable skills for research careers in materials modelling, engineering software and AI-assisted design You'll receive training in Python, machine learning and computational mechanics. Technical training will cover micromechanical models, numerical simulation and the transfer of material properties between scales. You'll learn how to combine experimental and simulated data, test the accuracy of model predictions, and assess whether they remain consistent with physical behaviour. Professional development will support scientific writing, research presentations and reproducible software development. You'll gain experience communicating across materials science, mechanics and AI, and explaining the strengths and limitations of the models you develop. The School of Engineering is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.

Entry requirements You must have a UK 2:1 honours degree, or its international equivalent , in one of the following: civil or mechanical engineering materials science computer science mathematics a related discipline Essential skills: an interest in connecting AI with concrete mechanics, and a willingness to develop skills across both areas ability to work independently, communicate clearly and collaborate effectively Desirable skills: programming experience in Python or a comparable language knowledge of concrete materials, solid mechanics or machine learning experience with machine learning, finite element modelling or experimental data analysis research experience and clear scientific writing Applicants whose first language is not English must meet the University's English language requirements .

资助：竞争性资助 学费与资助 我们为英国本土学生和国际学生提供多种资助机会。符合条件的学生可通过“地平线欧洲”学费减免，自动免除国际学生学费与英国本土学生学费之间的差额。 大学通过竞争性遴选授予的校长助学金，为遴选排名靠前的申请人支付国际学生学费与英国本土学生学费之间的差额。 本校各学院通过竞争性遴选提供的博士奖助金，通常为遴选排名靠前的申请人支付按英国本土学生标准收取的学费，并提供生活津贴。 资助采用滚动授予方式，因此请尽早申请，以尽可能增加进入资助评选的机会。 如需了解更多信息，请访问我们的研究生研究资助页面。

如何申请 您需： 选择课程类型（研究型）、2027/28学年、工程与物理科学学院 选择全日制或非全日制 搜索工程与环境博士项目（7175） 在申请表第2部分填写导师姓名 申请材料应包括： 说明您的动机与该项目适配性的求职信 研究提案（1或2页） 您的CV（个人简历） 2份学术推荐信 截至目前的学位课程成绩单和证书 英语语言资格证明（如适用）