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PhD Studentship: Structural Health Monitoring and Predictive Maintenance for Ensuring Long-Term Structural Health

5 months ago

Edinburgh, United Kingdom The University of Edinburgh Full time

Summary: This research proposal addresses challenges in the UK's construction industry, focusing on sustainability & safety. It aims to develop machine learning models that combine data from similar infrastructure and integrate information from previous inspections to predict potential problems. The models will also account for the impact of changing climate conditions. Large Language Models (LLMs) will be used to provide actionable insights for maintenance schedules, enhancing the longevity and safety of critical infrastructure. The research will engage with industry stakeholders to ensure practical and scalable models aligned with current and future needs. This project addresses infrastructure challenges and contributes to the UK's construction industry by leveraging LLMs.

Project Background:

The UK's construction industry is facing a major challenge in ensuring that its infrastructure remains safe and lasts longer in the face of the changing climate. Structures like bridges and rail systems often have shorter service lives than expected, which not only increases their maintenance costs, but also poses safety risks. Additionally, the shortage of skilled engineers & inspectors only aggravates these issues. The high demand for these professionals results in costly delays, and manual inspections in hazardous areas only add to the safety risks.

Climate change introduces a higher level of complexity. According to projections, the UK will experience hotter summers with a temperature increase of 3-4°C, and less summer rainfall with a decrease of 11-27% by the 2080s. These changes carry significant implications for infrastructure integrity, as they present a challenge to design codes that are based on past climate data. As climate stressors continue to worsen, it becomes increasingly evident that infrastructure management practices need to be adapted.

Large Language Models (LLMs) present a promising solution in this challenging context. Their ability to understand and generate human-like text enables them to provide insights and recommendations based on accumulated knowledge, bridging the gap between the scarcity of human experts & the increasing need for advanced analysis.

This proposal aims to address these multifaceted challenges by leveraging machine learning, LLMs, and past inspection data. The research will develop a predictive maintenance algorithm that considers the changing climate conditions in the UK, enhancing infrastructure resilience, sustainability, and cost-efficiency.

Achieving these objectives is crucial not only for the safety and well-being of communities but also for reducing the environmental footprint of the construction industry. Adapting infrastructure to the projected climate changes is imperative for the UK's long-term economic and environmental sustainability.

  1. How can a machine learning algorithm blend UK infrastructure data, including previous inspections, to predict potential problems, considering climate change?
  2. Can the algorithm combine different data sources, like images, time series, past inspections, and real-time data, to improve infrastructure management and respond to climate change?
  3. How accurate & reliable is the predictive algorithm in anticipating problems in the UK's changing climate conditions?
  4. What are the environmental benefits of using the algorithm in infrastructure maintenance & inspection, considering climate resilience and historical data for decision-making?

Methodology:

  • Data Collection
  • Data Preprocessing
  • Machine Learning Model Development
  • Model Validation
  • Implementation and Evaluation

Further Information:

The outcomes of this research hold the potential to revolutionize infrastructure management in the UK. By leveraging machine learning, historical inspection data, & accounting for changing climate conditions, this study aims to enhance the sustainability, safety, and economic efficiency of infrastructure, contributing to the broader goal of reducing the environmental impact of the construction industry & achieving climate resilience.

Tuition fees + stipend are available for applicants who qualify as Home applicants (International students can apply, but the funding only covers the Home fee rate)