Ground Engineering Solutions

Becoming a Data Driven Consulting Firm

  • In an era of ubiquitous technology, are engineers using their data well?

At the time of writing, data, or rather the protection thereof, is very topical. Facebook’s share price has fallen a full 12% in the last few days (around 9.5% year-to-date) on the news of an alleged surreptitious data breach which has both British and EU MPs looking to Mark Zuckerberg directly to provide evidence and explanations. The slump in share price is indicative of the backlash the company has suffered but also demonstrates how valuable data has become.

According to the National Infrastructure Commission (1), data contributes roughly £50 billion to the UK economy annually. With the advent of ever-improving Artificial Intelligence (AI) systems, it estimates that 10.3% could be added to the UK economy by 2030. The same report goes on to examine, at a high level, big themes of Smart Infrastructure, Big Data analytics and Digital Twin, all of which is welcome – it’s aspirational and the NIC should be aspirational. It should be reflective of where we want to get to.

But what about small consulting firms based in the construction sector of the broader economy? At its broadest definition, the construction industry accounts for 15-16% of the total annual economic activity (2) but has been consistently derided as a lethargic industry rife with low productivity and huge inefficiency (3, 4). Many think-tanks have examined this (5, 6) citing BIM as a potential game-changer in the voyage toward productivity gains. Indeed the same NIC report suggests that in 2014, BIM saved the construction sector an estimated £840 million. Despite this, the inefficiency of the sector persists (7) with the cyclicality of the industry and forthcoming skills shortage (widely cited) likely to stifle any productivity gains. What can small consulting firms do?

One suggestion is to mine your data. Not in the high-level “big data” sense, but the volume and quality of data collected by small consulting firms in the normal run of their day-to-day work is surprising. Here’s an example: we do a lot of soil mixing projects. Really good technology for the implementation of quality improvement to soft ground. There’s a chart below gleaned from the rig data showing strength development with mixing time (the time spent in each mixing cell for the required volume of binder addition). We measure strength in order to verify design assumptions and prove spatially we’re getting the coverage and strength development that we need. As long as we present this to the Client to show the specification has been achieved, all is good.

But the mixing time data is there, captured by the rig, so why not use it? –  Some loose correlations are fitted and we can see an increase in strength with increasing mixing time, but consistently a falling away in strength gain after 1.4 minutes per m3. We could glean from this that there’s perhaps an optimum mixing time for a given soil type or binder ratio? Maybe – we’re not sure. We’ve a lot more data to collect and a lot more work to do with the data but we will do this. And if that means our specialist contractor spends 1.4 minutes per m3 of soil instead of 2 minutes (a 30% reduction of mixing time) then productivity is improved. Just by examining data that we already have.

With improved production, maybe we can look to drive margins a little higher, invest a little more in newer technologies and improving technologies as well as training and technical apprenticeships. This is what the construction industry actually needs.


Sources / references

Bloggeotechnical designgeotechnical research#Artificial Intelligence#BIM#construction#Data analytics#Digital Twin#ground engineering#NIC report#Smart Infrastructure#soil mixing