TL;DR:
- Over 96% of infrastructure project data remains untapped, hindering progress.
- Incompatible tools and systems lead to data loss during handoff between project phases.
- 90% of data generated during engineering and construction is unstructured, impeding translation and migration.
- Unstructured data causes a lack of visibility, communication, and collaboration among stakeholders.
- Leveraging AI and machine learning can unlock actionable insights for sustainable outcomes.
- Open platforms, interoperability, and standardized data requirements are essential for rich datasets.
- Digital twins offer consolidated and dynamic datasets, but require an open foundation and standardized infrastructure schemas.
- Rich datasets drive informed decision-making and operational excellence.
- By embracing data and advanced tools, stakeholders can achieve better project outcomes and sustainability goals.
Main AI News:
Our modern world operates on data. The convergence of Building Information Modeling (BIM) and the widespread adoption of IoT-enabled sensors has ushered in an unprecedented data deluge. However, despite this abundance, we find ourselves at a crossroads – drowning in data, yet failing to effectively utilize its immense potential.
According to a recent report by FMI Corporation, a prominent consultancy firm, a staggering 96% of infrastructure project data goes unused. While it is true that certain data is inherently project-specific and not reusable, it is inconceivable that such a substantial majority is rendered obsolete.
A major obstacle preventing us from harnessing the power of infrastructure project data lies in the handoff process from one entity or phase to another. Each transition, be it from project design to construction or construction to commissioning, results in data loss. It is evident that the tools and systems employed in different phases often lack compatibility, rendering the data generated incompatible with subsequent phases.
Regrettably, a significant portion of the hard work invested in one phase is sacrificed, forcing subsequent phases to start anew with diminished fidelity and detail. The aforementioned FMI study further reveals that a staggering 90% of data produced during engineering and construction is unstructured. Unstructured data poses a daunting challenge, as its translation and migration between tools can prove arduous, if not impossible. This disconnection hampers visibility, communication, collaboration, and decision-making, ultimately contributing to data incompleteness and obsolescence.
The Solution Lies in AI and Machine Learning: Unleashing Sustainable Outcomes
To fully leverage the potential of tools such as artificial intelligence and machine learning in the infrastructure sector, it is imperative to establish robust datasets encompassing both structured and unstructured data. These datasets serve as a wellspring of valuable patterns and insights, fostering communication and collaboration among architectural, engineering, and construction (AEC) firms, as well as asset operators.
A comprehensive and cohesive dataset also enables accurate measurement of an asset’s complete carbon footprint throughout its entire lifecycle, encompassing project delivery, construction, operations, and potential decommissioning. This holistic approach empowers the sector to drive sustainable outcomes, aligning with the pressing need to combat climate change.
Moreover, the availability of robust and accurate data, harnessed by cutting-edge machine learning and artificial intelligence tools, ensures accuracy in design intent and enables operational efficiencies across the asset lifecycle. These technologies uncover invaluable insights that would otherwise remain concealed.
Paving the Way: How Can the Infrastructure Sector Achieve Data-Driven Transformation?
From a technological standpoint, the creation and maintenance of comprehensive datasets necessitate open platforms capable of accommodating multiple tools. Seamless interoperability among these tools, coupled with secure and appropriate data sharing throughout the supply chain, are fundamental prerequisites for success. Furthermore, data requirements should be standardized across the entire lifecycle, transcending the current reliance on Piping and Instrumentation Diagrams (P&IDs) to enable a more proactive approach.
By shifting the focus of data definitions earlier in the lifecycle and specifying the necessary data for each phase, we can ensure that the right information is captured as projects progress. Furthermore, harnessing the capabilities of digital twins allows infrastructure stakeholders to consolidate data from disparate sources, transforming isolated data silos into a unified, geospatially referenced repository of crucial information. Whether it pertains to carbon footprints, emissions, as-designed and as-built data, maintenance records, or asset performance insights, digital twins offer a holistic view when implemented correctly.
To truly unlock the potential of digital twins, it is paramount to establish an open foundation built upon infrastructure schemas with standardized data translation protocols across applications. Only then can a digital twin flourish as a dynamic, comprehensive, and ever-evolving dataset. By illuminating the dark corners where valuable data currently goes to waste throughout the infrastructure and asset lifecycle, we can extract genuine insights that drive tangible results.
The Value of Rich Datasets: Enabling Informed Decision-Making and Operational Excellence
The rewards for embracing rich datasets are manifold. Actionable insights derived from these datasets empower project decision-makers and operational teams to optimize performance continually. These insights unveil previously undetected trends, enabling proactive course correction and driving superior outcomes for all stakeholders, including design firms, engineering, procurement, and construction (EPC) contractors, asset owners and operators, and the wider public.
Unlocking the true potential of data paves the way for leveraging machine learning, artificial intelligence, and other cutting-edge tools that thrive on rich datasets. These tools enable stakeholders to extract invaluable insights for carbon calculations, predictive maintenance recommendations, and, ultimately, the realization of better, more sustainable outcomes.
It All Boils Down to Data: Embracing the 96% Opportunity
Let us not be content with a mere 4% reuse of data. The vast potential for smarter infrastructure outcomes lies within the untapped 96%. By utilizing infrastructure project data as a foundation for collaboration, leverage, and reuse, we can enhance our efficiency and gain profound insights.
Considering the escalating demand for infrastructure projects, mounting backlogs, resource scarcity, and the urgent need to combat climate change, we cannot afford to overlook the fundamentals. The time has come to embark on a paradigm shift, revolutionizing how we approach and harness the power of data. Only then can we truly navigate the intricate web of infrastructure challenges, bringing about a brighter, more sustainable future for all.
Conclusion:
The untapped potential of infrastructure project data poses both challenges and opportunities for the market. Addressing data loss, incompatibility issues, and unstructured data through AI and machine learning can lead to valuable insights and improved outcomes. The need for open platforms, interoperability, and standardized data requirements is crucial. Embracing rich datasets, including digital twins, enables stakeholders to make informed decisions, optimize performance, and work towards sustainable goals. Harnessing the power of data and advanced tools is essential for driving market competitiveness and delivering successful infrastructure projects in a rapidly evolving landscape.
