Big Project ME July 2017

Page 48


Gaining recognition The critical importance of engineering data is gaining more recognition than ever before.

constructors to confidently select the best team for the job, improving stakeholder visibility and confidence as connectivity is achieved through digital means. If your firm or organisation does not adopt a digital strategy, what will be the effect? I suggest it will be lasting damage to project costs, quality and collaboration, as processes will be manual, error-strewn, laborious and not fit for modern demands. The insight a stakeholder can gain into a project now is such that I believe it will soon become mandatory for digital processes to further the potential of BIM, improving infrastructure quality, reducing costs and ensuring consistency across the entire project. How are BIM software developers using data collection and mining to improve their offerings?

BIM Level III focuses on asset performance strategies once

46 July 2017

“BIM advancements enhance project collaboration and improve construction productivity, which contributes toward building a robust construction economy and improving stakeholder input and satisfaction”

projects are in operation. The value of data mining, aggregation and intelligence can be realised through operational analytics. Operational analytics is an emerging, industry-recognised business process that focuses on improving infrastructure asset performance with the power of sophisticated analytics. The critical importance of engineering data is gaining more recognition than ever before, by offering an opportunity for ultimate convergence between IT/OT and now ET (engineering technology). The challenge to this ultimate convergence has been that historically, the engineers who design digital engineering models for planning, design and construction have been less focused on the benefits of these models for operational purposes; and the operators have been less aware that the ‘digital DNA’ produced when designing and building early in

the lifecycle can be used for effective decision support. The potential of connecting technology is starting to be realised for meeting business imperatives and objectives. By connecting the ‘as-operated’ performance of assets to the ‘as-designed’ performance, they can be analysed with burgeoning real-time data leveraged with IoT, big data, historical data and engineering data, to improve operational efficiencies with predictive techniques. The predictive and prescriptive nature of machine learning can be the ultimate decision support tool to identify critical issues in a timely manner, improving safety and reliability, but also optimising such scenarios for future forecasting. In this new environment, BIM models are integrally connected to the physical asset, thus closing the loop between all levels of BIM.