Unveiling the Data-Driven Transformation: Exploring Big Data and Analytics in Deep Renovation

By Samantha Schoenlank and Antonia Egli (Dublin City University)

In our quest to explore the transformative impact of technology on the renovation and construction sectors, we continue with the RINNO blog series focusing on the open-access book ‘Disrupting Buildings: Digitalisation and the Transformation of Deep Renovation.’ This month, we bring you insights from Chapter 5 ‘Big Data and Analytics in the Deep Renovation Life Cycle.’ This chapter delves into the intersection of big data and deep renovation, shedding light on how big data and analytics can revolutionise decision-making in building stock retrofits.

The renovation and construction industries are experiencing a paradigm shift brought about by the proliferation of heterogeneous data. With the accessibility of technologies like sensors and the Internet of Things (IoT), data-driven decision-making is becoming integral, especially in the context of deep renovation. Chapter 5 defines big data and analytics in the deep renovation life cycle and explores the impact of machine learning (ML) and artificial intelligence (AI) on decision-making processes.

Categorising the Power of Big Data in Deep Renovation

The transformative power of big data in deep renovation lies in its ability to leverage vast amounts of data for more informed decision-making. Big data analytics takes centre stage and can be categorised into four distinct types: descriptive, diagnostic, predictive, and prescriptive analytics. Each plays a crucial role in interpreting and utilising data to enhance operational performance and decision-making in renovation and construction.

Descriptive analytics lays the groundwork by unveiling current and past trends, while diagnostic analytics digs deep to explain the causes behind specific outcomes. Predictive analytics, with the use of ML and statistical modelling, estimates outcomes, and prescriptive analytics optimises operational processes using insights gleaned from previous analyses.

Big Data Use Cases and Applications Reshaping Deep Renovation

The construction landscape is witnessing a transformative wave driven by big data engineering, prominently featuring Building Information Modelling (BIM). Horizontal Scaling Platforms (HSPs) and Vertical Scaling Platforms (VSPs) play pivotal roles, contributing to waste management, profitability performance measurement, smart road construction, and more. In parallel, deep learning-based applications, including flood detection and project delay risk prediction, showcase the integration of AI and ML in construction and renovation.

Within the realm of deep renovation, AI and ML find synergy with BIM and Industry Foundation Classes (IFC), propelling advancements in sustainable architecture, energy-efficient building design, and environmental impact mitigation. Deep renovation can thus emerge as a key driver for reducing greenhouse gas (GHG) emissions, with BIM and IFC enhancing both decision-making processes and the energy efficiency of retrofitted buildings.

Building life cycle (credits: Zanni et al.)

Advantages & Benefits vs. Challenges & Barriers of Big Data Analytics

The literature reveals a myriad of opportunities for big data adaptation in deep renovation, spanning generative design, clash detection, performance prediction models, visual analytics, social networking, and personalised services. Beyond these applications, big data analytics in construction and renovation leads to tangible benefits, including improved efficiency and a reduction in environmental impact.

However, challenges and barriers also exist, ranging from human factors to technological integration issues, organisational barriers, and specific data-related challenges like data security and privacy. The potential for future developments is nonetheless immense. The integration of big data promises to benefit construction companies and stakeholders, fostering long-term infrastructure and preventing errors for better construction and renovation outcomes.

Future Developments in Big Data

The transformative power of big data in deep renovation lies in its ability to leverage vast amounts of data for more informed decision-making. While the construction and renovation sectors have made significant strides in adopting big data technologies, the journey towards widespread commercialisation is ongoing. Future developments may focus on the global commercialisation of big data analytics for deep renovation, construction waste simulation tools, linked building data platforms, and big data-driven BIM systems. As the industry continues to evolve, big data is poised to play a central role in shaping the future of construction research.

As we embrace this data-driven revolution, the construction industry stands at the cusp of unprecedented possibilities, where every byte of information contributes to a more efficient, sustainable, and innovative future. If you wish to explore further, we encourage you to delve into Chapter 5 of the book for a comprehensive understanding and additional multidimensional applications.

To learn more about deep renovation technologies in general, you can download the open access book ‘Disrupting Buildings: Digitalisation and the Transformation of Deep Renovation’ for free. In the book, we explore various digital innovations disrupting and transforming the construction sector. To download the full open access book, ‘Disrupting Buildings,’ click here.


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