Introduction to Civil Construction Optimization Processes based on Data Science
In general, Data Science can be used for many purposes, but mainly to drive business decisions, reduce costs and increase productivity. A company managed by a Data-Driven Culture focuses on interpreting data by showing the best way to ensure better results.
From logistics companies to apparel companies to the more recent phenomenon of music and movie streaming, Data Driven Culture is becoming the main way companies organize. The civil engineering market is going down the same path, it is a trend of great importance for the construction sector, due to the fact that companies that adopt these solutions considerably improve their results, guaranteeing a high rate of assertiveness in their projects.
Civil Construction is one of the main economic activities in Brazil, but it could have better results if waste were reduced and productivity increased. Common problems in the sector, such as waste of materials, delays in works and lack of budget control, can be solved with the use of technology. Data Science can help managers find the roots of problems and make more assertive decisions. If this process were done manually, the analyzes could be time-consuming and even unproductive, but with the use of Data Analysis software and tools, it is possible to use them intelligently.
Within Data Science, the term Big Data has been gaining strength in recent years. This concept refers to the centralization of the use and analysis of a large volume of data, which has been helping to transform the construction sector. In order to take advantage of this type of technology, it is initially necessary to start gathering data from different construction sources. With the data stored, then the mining, cleaning, transformation can be done, and finally presentation phase begin. During this process, different types of methodologies can be used, such as Artificial Intelligence and Machine Learning. The focus is on transforming all data into relevant Insights so that managers can be more clear when making decisions.
Currently, there are several software and analysis methods that allow the application of this type of intelligence in various construction sectors. Technologies that use the assumptions of Data Science to develop modeling and computer simulations based on historical data, so that changes in works are made with greater assertiveness and awareness of the possible results.
As previously mentioned, the union between Data Science and Civil Engineering can bring several benefits, among them, information control. With the proper management of engineering project information, budgets and schedules in a single location, it is possible to help organize and plan current and future works. Understanding how the items and sub-items of each budget interact with each other can help create models that detect patterns, increasing the assertiveness rate of unit values, as well as the deadline for each step in the schedules.
Based on access to historical data, it is also possible to detect and analyze indicators of projects in all disciplines, with the aim of assessing divergences. At the same time, historical data from the company’s previous projects, together with publicly available data, allow forecasts to be made and the optimal use of resources to mitigate risks before they affect project margins. Data Science can also be used to predict temporal conditions, producing a high positive impact on the construction schedule, preventing, for example, that on days scheduled to develop the concreting of a given structural element, there are no problems or delays in the process due to rain. With the help of this technology and based on historical data, it is also possible to predict the costs of a construction, allowing greater assertiveness of the budget and in each constructive stage.
Sequentially, the centralization of information regarding the storage of work materials can help control the entry and exit of materials, in order to understand the needs of the work regarding each of the inputs. Based on the assumptions of Machine Learning, it is possible to develop predictive models to know when it will be necessary to purchase a certain material, to know the period of useful life of that same material during the course of the work and to understand its performance in different types of use, thus generating a reduction in the waste of materials. Based on the same assumptions and with the use of equipment with IOT (Internet of Things) networks, it becomes possible to identify when a certain piece of equipment on the job will need maintenance or some type of intervention, before it even happens, contributing to the flow positive aspect of construction planning.
Data Science can also contribute in a way related to Civil Construction professionals. The centralization of information regarding the operational team can help control the productivity and performance indicators of each of the professionals working on the job. Based on the assumptions of Machine Learning, it is possible to detect employee patterns and, with access to historical data, develop a visualization model, in which the productivity of each employee is presented, referring to each of the services provided, helping in the control of decision by the manager.
With models based on the premises of Artificial Intelligence and work safety, it is possible to associate image tagging and analysis, identify and analyze safety risks and send alerts when some personal protective equipment is not being used properly in the workplace. This technology can even be used to identify who is violating safety standards and tag supervisors to resolve the issue. In the long term, it is possible to store this information so that in future works it will be possible to categorize risks and prioritize safety issues throughout the construction life cycle.
The combination of Data Science and other construction technologies makes it possible to even improve the relationship between construction companies and their customers. Having relevant information on hand to present and develop strategies for the project can positively impact the relationship with the client. With the storage of consistent data, in addition to keeping the client informed about the progress of the work, it can bring greater visibility on how financial resources are being used, creating total transparency.
Finally, several studies point to the civil construction industry as one of the least digitized sectors and most resistant to the adoption of new technologies. However, as previously mentioned, several technological tools are already responsible for major changes in the sector, bringing more security, efficiency and transparency to the processes of the entire construction production chain. Companies that insist on not adopting technological solutions like these will soon no longer have a competitive advantage over their competitors, which could jeopardize the future of the business.
