|Funding for:||UK Students, EU Students|
|Placed On:||9th September 2019|
|Closes:||1st October 2019|
Funding amount: EPSRC iCASE studentships are fully-funded (fees and maintenance) for UK students or provide fees only for EU students from outside the UK
The use of computer simulation early in the design process can have a major impact on embodied energy (Life Cycle Analysis studies). Careful choice of the geometry and layout of the structure can reduce internal forces and decrease the amount of energy-intensive structural materials required for support. Accurate service-life prediction of particular long-span members is vital for taking appropriate measures in a time- and cost-effective manner. However, the conventional prediction design models rely on simplified assumptions for typically used members (standard sizes) often leading to inaccurate estimations. Although data driven approaches mainly used today to enhance the performance prediction, they still depend on empirical formulas with many limitations.
This project will engage with Artificial Intelligence (AI) methods recently developed for structural engineering applications, as is proving to be an efficient alternative approach to classic modelling techniques, and attempt to reduce the percentage of uncertainty of the results as well as saving significant human time and effort spent in experiments.
The power of the approach proposed in this research project can be exercised performing a series of studies focusing on long-span structural systems such as roofs for archaeological sites, airport terminals, concert halls, and train/bus stations. Such structural forms pose a special modelling challenge: they often have large open spaces with unusual shapes and few interior columns, so they rely on systems of triangular space trusses and frames working together to support the load of the building.
This study will focus on finding an efficient scheme for the topology optimisation in order to create long-span steel members (beams) with high buckling strength than the one created by just using empirical approaches in conjunction with machine learning for developing the most optimum floor-plate layouts of given geometric and loading characteristics. Buckling optimisation will be studied for first time at this scale using advanced algorithms of Hyperworks software tools. Together with machine (supervised) learning Neural Network algorithms (via regression analyses), the limitations of classical prediction models will be demonstrated.
Parametric nonlinear finite element analyses will be performed using ANSYS software to feed the machine learning algorithm with validated data. In addition, both the initial energy required for making structural materials and components as well as the future operational energy will be quantified and compared for the design of energy-efficient structures.
The knowledge generated by this project can push solutions in interesting and unexpected ways and lead to new building designs and regulations (including low- and high- storey lightweight structures) via the design of long-span lightweight and stiff (support-less) structural members that are high-performance, innovative and architecturally expressive.
This PhD is part of an interdisciplinary research programme which attempts to tackle the global challenge of environmental change and provides solutions for Engineers and Architects especially when design large scale structures. It is anticipated that the proposed methodology will be also adoptable to other construction systems and projects.
SC4 shares a history of close collaboration with the University of Leeds. SC4 has reputation for delivering innovative, integrated solutions to complex steel structures, leadership in sustainability and a commitment to energy-efficient steel solutions.
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