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Multi-Objective Optimisation for Sustainable Steel Structures Employing Artificial Intelligence

PGR-P-140

Key facts

Type of research degree
PhD
Application deadline
Thursday 30 April 2020
Project start date
Thursday 1 October 2020
Country eligibility
UK and EU
Funding
Funded
Source of funding
Research council
Supervisors
Dr Konstantinos Tsavdaridis
Schools
School of Civil Engineering
<h2 class="heading hide-accessible">Summary</h2>

The use of computer simulation early in the design process &ndash; when the share of the building in determined &ndash; can have a major impact on embodied energy (Life Cycle Analyses &ndash; LCA 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. More specific, 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.

<h2 class="heading hide-accessible">Full description</h2>

<p>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.</p> <p>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.</p> <h5>Methodology</h5> <p>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 Altair&rsquo;s Hyperworks software tools. Together with machine (supervised) learning Neural Network algorithms (via regression analyses), the limitations of classical prediction models will be demonstrated.</p> <p>Parametric nonlinear finite element (FE) 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.</p> <h5>Impact</h5> <p>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.</p> <p>The proposal project is in line with the wider EPSRC portfolio and more specifically with the following five Themes (in order of relevance): Research Infrastructure, Living with Environmental Change (LWEC), Energy, Manufacturing the Future, and Adventurous Manufacturing Research as well as the following (not limited to) EPSRC Research Areas: Engineering Design, Structural Engineering, Manufacturing Technologies, Materials Engineering &ndash; Metals &amp; Alloys, Infrastructure and Urban Systems, End Use Energy Demand and Software Engineering.</p> <p>This PhD is part of an interdisciplinary research programme which attempts to tackle the global challenge of environmental change and provides solutions for Civil 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.</p> <p>This project focuses on the following EPSRC&rsquo;s Priority Areas:</p> <ul> <li>Lightweight systems: Composites manufacturing; Multiple-material approaches to lightweight systems; Net shape manufacturing.</li> <li>Sustainable built environments: Underpinning LCA/LCC studies for the built environment and a mixture of theoretical and experimental work.</li> <li>Sustainable use of materials: Sustainable materials for manufacture; Resource-efficient manufacturing; More sustainable and environmentally-friendly approaches to manufacturing.</li> </ul> <p>The project is aligned with the following sector hubs:</p> <p>1. Discrete sectors:</p> <ul> <li>Digital Technologies</li> <li>Artificial Intelligence and Machine Learning</li> <li>Cultural &amp; Creative Industries</li> </ul> <p>2. Energy and Resources cluster:</p> <ul> <li>Energy</li> </ul> <p>The supervisors have extensive experience working with long-span and lightweight steel and composite structures and they have co-published papers on optimisation techniques. The main supervisor, is a member of the Scientific Committee of the &lsquo;International Association of Shell and Spatial Structures - IASS&rsquo; and a working group member of WG 8 dealing with Metal Spatial Structures (Buckling and Earthquake Response) and he has co-published a book through IASS titled &lsquo;Guide to Earthquake Response Evaluation of Metal Roof Structures&rsquo; which is relevant to the proposed projects.</p> <h5>Industrial partner</h5> <p>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.</p>

<h2 class="heading">How to apply</h2>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="https://www.leeds.ac.uk/info/130206/applying/91/applying_for_research_degrees">University&#39;s website</a>. Please state clearly in the research information section&nbsp;that the research degree you wish to be considered for is &lsquo;Multi-Objective Optimisation for Sustainable Steel Structures Employing Artificial Intelligence&rsquo; as well as&nbsp;<a href="https://engineering.leeds.ac.uk/staff/597/dr_konstantinos_daniel_tsavdaridis">Dr Konstantinos Tsavdaridis</a>&nbsp;as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p><em>We welcome applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.</em></p>

<h2 class="heading heading--sm">Entry requirements</h2>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline.

<h2 class="heading heading--sm">English language requirements</h2>

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid.

<h2 class="heading">Funding on offer</h2>

<p>UK/EU &ndash; EPSRC CASE studentship with additional support from industrial collaborator SC4 UK Ltd. Funding covers the cost of tuition fees as well as maintenance grant (&pound;18,009 in Session 2019/20). Applicants will be eligible for a full award paying tuition fees and maintenance, for 3.5 years.&nbsp;</p>

<h2 class="heading">Contact details</h2>

<p>For further information please contact Doctoral College Admissions by&nbsp;email:&nbsp;<a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a>, or by telephone: +44 (0)113 343 5057.</p>