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The value of automation for infrastructure decision making


Project Description

This PhD project will quantify the value of automating infrastructure health monitoring. The framework developed will help asset owners decide whether new techniques in robotic sensor deployment and machine learning provide enough of a benefit to tilt the balance in favour of committing to the wider deployment of continuous monitoring systems.

To quantify the value of automating all or part of an asset monitoring process, we need a framework to understand the impacts automation has on decision making. This project will be framed in the context of assessing the resilience of transport infrastructure (tunnels and bridges) during multiple-hazards.

The PhD project is a collaborative effort between two departments: Civil & Environmental Engineering, and Management Science. The project will link with our recent £70k investments in vibration testing equipment and robotics, and will synergise with our growing £150k portfolio of projects linked to the Lower Thames Crossing project (in collaboration with Highways England and COWI UK Ltd).

Motivation: Across engineering sectors, current practices for asset integrity assessment typically rely on human inspection, coupled with a few manually fitted sensors. The widespread implementation of practical or data automation faces reluctance, partly due to costs (which will change), but partly due to a “decision problem” — a need for transparent, defensible decision making (this will not change).

Automated monitoring systems, installed with robotics and/or interpreted with black box machine learning techniques often result in data that differs from conventional manual inspections. Regardless of which path is chosen, the purpose of the data is to answer a question: do we take action (e.g. conduct maintenance) or do we wait? In reality, this is a multi-criteria assessment of the trade-offs between costs, safety and the defensibility of the decision making process. For manual inspection, this decision making process is familiar and transparent, but it is also expensive and prone to human errors that are difficult to trace.

Outcomes: The main outcome of this project will be a framework that helps asset owners decide on their level of commitment to automated monitoring. The inputs into the framework will be:

1. models / knowledge that describe the state / degradation of the asset over time;
2. an assessment of the relative efficacy of automated and manual inspection methods in diagnosing system state / integrity;
3. a process to elicit from key stakeholders the trade-offs between cost, safety and defensibility.

The project’s objectives are to:

• instrument structural components in the lab with strain sensors, using both manual methods and robotics, and compare variability in sensor performance (placement location, strain transfer, precision, accuracy and reliability);
• test the instrumented components under dynamic loading, and use the obtained data with an artificial neural network to update the parameters of a finite element (FE) model of the system;
• use the updated FE to estimate the component’s fragility and infer resilience: where possible, connect this with a risk of downtime and a revenue loss.;
• using this framework, explore the knock-on effects that automation of sensor placement and data analysis have on the cost and value of information provided by monitoring;
• use expert interviews to explore how the defensibility of decision making is affected by automating the monitoring as proposed.


Funding Notes

Fees (home rates), stipend and project expenses are provided. Applicants should have or expect a distinction pass at Master’s level, or a first class/ 2:1 BEng/BSc Honours degree in an Engineering, Physical Sciences or Management Sciences subject. Candidates with strong analytical and interpersonal skills are particularly welcome to apply. Experience with risk analysis, structural computational modelling, or programming is advantageous but not essential.

How good is research at University of Strathclyde in Civil and Construction Engineering?

FTE Category A staff submitted: 20.20

Research output data provided by the Research Excellence Framework (REF)

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