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Technology, Costain

Forensic engineering data for future infrastructure project success

Forensic engineering data collected during the delivery of an infrastructure project is mainly in written form, unreadable and high in volume. Our PhD researcher aims to use modern computer analytic techniques and develop a framework to implement systematic learning.

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University of Edinburgh

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Forensic engineering data

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Construction engineering / data science

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October 2016 to October 2020

Research focus

The initial focus of the research explored current methods of learning from past events within the industry and how the data on projects is used for learning. The research also investigates the specific use of forensic engineering data and will look at using a hybrid natural language processing and knowledge discovery framework to transform everyday forensic engineering data into a valuable resource exploitable for learning.

While notoriety is often gained for the investigations and case studies following large catastrophic failures, smaller scale investigations using these same principles occur almost daily on construction projects in the form of non-compliance, incident and near-miss reports. However, these data are often rendered inaccessible upon project completion, and the learning opportunity to the wider community wasted, only remaining as tacit or experiential knowledge.

Additionally, these data are predominantly captured in written prose, an unformatted style, therefore analysis is hindered by the lack of techniques to deal with data of this type. This results in restricting learning to fewer significant cases to avoid inundating people with constant minor alerts or updates.

In considering how these data may be captured, understood and learning extracted, Henrietta’s research has been divided into two parts.

The first explored, using semi-structured interviews and thematic analysis, the notion of defining failure within construction projects and the existing ways in which forensic engineering data are captured and lessons learnt disseminated. The aim of this part was to contexualise the problem and generate relevant assumptions about the data in order to intelligently overcome barriers to learning from forensic engineering data. While this part of the research is notionally complete, additional insights can always be gleaned from further experience within industry.

The second part of Henrietta’s research will determine how informatics techniques can be leveraged to implement learning from everyday forensic engineering by presenting a framework that uses recent developments in both the data analytics and natural language processing fields. The research will demonstrate a hybrid natural language processing and knowledge discovery framework to transform everyday forensic engineering data into a valuable resource exploitable for learning.

Contact and social

Tim Embley

Knowledge and innovation manager
01628 842444
[email protected]