{"id":33949,"date":"2020-01-27T11:23:47","date_gmt":"2020-01-27T10:23:47","guid":{"rendered":"https:\/\/www.ferrovial.com\/blog\/?p=33949"},"modified":"2025-12-12T00:36:38","modified_gmt":"2025-12-11T23:36:38","slug":"attractive-aspects-of-big-data-and-predictive-analysis-and-other-technologies-in-the-field-of-construction","status":"publish","type":"post","link":"https:\/\/www.ferrovial.com\/blog\/en\/2020\/01\/attractive-aspects-of-big-data-and-predictive-analysis-and-other-technologies-in-the-field-of-construction\/","title":{"rendered":"Attractive aspects of \u2018big data\u2019 and predictive analysis (and other technologies) in the field of construction"},"content":{"rendered":"

For several years I have had the opportunity to witness the creation of technological tools that have contributed to modernize safety conditions or the way in which a construction work is carried out, and thus improve the results of the industry. It is amazing to work side by side with engineers and data scientists to innovate in a sector like ours.<\/p>\n

In recent times, the use of big data is opening very attractive opportunities in the sector. These collected data sets can be analysed to obtain ideas that lead to better construction decisions and thus optimize processes. This is where the predictive analysis plays a prominent role, since through this tool you can perform an advanced data analysis to predict future behaviour. Construction is taking advantage of its numerous benefits. In fact, it is considered as a tool to eliminate risks and the industry is increasingly adopting it.<\/p>\n

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Author: Vitaly Vlascov | Source: Pexels<\/p><\/div>\n

The importance of big data for the predictive analysis<\/h2>\n

The power of predictive analysis is such that in the United States some construction and insurance companies are partnering in the Predictive Analytics Strategic Council<\/a> to share their data and improve their experiences. These companies use anonymized own data with more than a decade<\/a> to develop their own predictive models.<\/p>\n

https:\/\/www.youtube.com\/watch?v=fRZI_XQE6Bk<\/a><\/p>\n

Once these models have been created, the association not only wants to benefit itself and its members, but also the rest of the architecture, engineering and construction industry (AEC). Thus, one of the purposes of the Predictive Analytics Strategic Council is<\/a> to create a guide for applying predictive analysis.<\/p>\n

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Author: Burak K | Source: Pexels<\/p><\/div>\n

These sectoral associations are emerging with the aim of using big data through predictive analysis due to the fact that big data help work or company managers to make better decisions. The security team is well aware that it is possible to take advantage of the image capture of employees to know who is on the perimeter or identify risks and quality problems.<\/p>\n

Registering large amounts of data also helps to establish meteorological patterns and provide weather forecast before a project begins as well as to identify locations with bad weather conditions. There are also other technologies that are being used on site and that complement the virtues of big data and predictive analysis, as we will see in further text.<\/p>\n

To see the final result of a building as though it were already built: other ways of applying technology in construction<\/h2>\n

Predictive analysis is one of the most promising technologies of the moment and great growth is expected in the future. However, there are also other tools that are helping the growth of the industry<\/a> and have quality and safer construction day by day.<\/p>\n

In the first place are the technologies of digital twins: the 3D modelling of a building is used to have a real approximation of the result and is useful throughout the work. If virtual reality and augmented reality<\/a> are also applied, the result is more attractive and very visual. For this, iPad applications such as Morpholio AR Sketchwalk<\/a> or Gamma AR<\/a> are recommended, which mix augmented reality with BIM (Building Information Modeling), but there are many more<\/a>.<\/p>\n