博士分析解释岩土基础设施资产产生的声发射

拉夫堡大学在研究强度（REF2014）方面是英格兰排名前十的大学，并且有资格提交给REF的拉夫堡学术人员中有66％的杰出工作被评为“世界领先”。或“国际一流”，而全国平均水平为43％。<br>在选择拉夫堡进行研究时，您将与该领域的领导者一起工作。您将受益于我们的全面支持和指导，包括量身定制的职业建议，以帮助您在研究和未来职业中取得成功。<br>项目详细信息：颗粒材料变形过程中耗散的能量比例转换为热量和声音。这种声能的高频（10kHz）分量称为声发射（AE）。AE监测提供了潜在的感测颗粒尺度相互作用的能力，
Loughborough University is a top-ten rated university in England for research intensity (REF2014) and an outstanding 66% of the work of Loughborough's academic staff who were eligible to be submitted to the REF was judged as ‘world-leading' or ‘internationally excellent', compared to a national average figure of 43%. In choosing Loughborough for your research, you'll work alongside academics who are leaders in their field. You will benefit from comprehensive support and guidance from our Doctoral College, including tailored careers advice, to help you succeed in your research and future career. Proportions of the energy dissipated during deformation of particulate materials are converted to heat and sound. The high-frequency (10kHz) component of this sound energy is called acoustic emission (AE). AE monitoring offers the potential to sense particle-scale interactions that lead to macro-scale responses of granular materials. AE is widely used in many industries for non-destructive testing and evaluation of materials and systems, however, it is seldom used in geotechnical engineering, despite evidence of the benefits, because AE generated by particulate materials is highly complex and difficult to measure and interpret. The aim of this PhD is to develop analytics for the automated interpretation of AE generated by geotechnical infrastructure assets. The objectives are: (1) to establish feature extraction and/or pattern recognition methodologies to quantify AE parameters, (2) to develop artificial intelligence analytics to convert AE input parameters to asset health statuses, and (3) to assess performance of the analytics using experiments. Extensive datasets of AE measurements have been produced through series of controlled element (e.g. triaxial, direct-shear) and full-scale physical model (e.g. buried pipelines) tests. This PhD will exploit the existing datasets, and supplement them using new experiments. The successful candidate will join the ‘Listening to Infrastructure' research group, which is developing continuous, real-time acoustic emission (AE) monitoring systems that can be distributed across geotechnical infrastructure assets (e.g. buried pipelines, foundations, retaining structures) to sense soil and soil/structure interaction behaviour, and provide early warning that will enable targeted and timely interventions.