Building Smarter with Data Science: The Game-Changing Applications of Data Analytics in Construction

Global development has its heart in infrastructural development and construction is the oxygen that keeps the infrastructural development pumping. The world is in a whole new era, which we can rightly term “the era of data”, driven by fact-based decision-making enabled by data science and further elevated by machine learning and artificial intelligence. From manufacturing to banking, automobile, retail, and construction, the application of machine learning and artificial intelligence has led to the evolution of new practices and disciplines in every industry. From addressing problems in planning, project management, resource management, supply chain management, and financial management to sales and customer satisfaction management, data analytics is the answer to everything that leads to the success of companies across all industries.

In this article, we will see how data science and data analytics impacts the constriction industry, improving the core processes that lead to the success of any project in the engineering and construction sector.

DATA SCIENCE AND CONSTRUCTION INDUSTRY

The construction industry produces and processes vast amounts of data every day, with huge data repositories being filled with information on everything from cost estimates and material usage to building models and blueprints. If these repositories are left unstructured and difficult to access, it would mean a hurdle to the evolution of the engineering and construction industry, an evolution that is quintessential to global infrastructure development.

Data Science acts as a catalyst for evolution

Many companies in today’s time believe that embracing a digital-first business strategy is just about digitizing their conventional process. While this is the first step, companies can achieve far more significant changes and competitive edge by experimenting with data science and AI.

Advancing technologies like the Internet of Things (IoT) and drones have the potential to produce rich, unconventional data streams. When such data is examined through the lens of data science, it allows for uncovering evolutionary and revolutionary efficiencies. It enables project delivery teams to augment their experience and strategic know-how with deep, tactical insights provided by data analytics and artificial intelligence. When such technology is applied to project delivery processes like total cost management, it defines new disciplines in project management such as total cost management analytics or material application analytics.

APPLICATION OF DATA SCIENCE IN THE CONSTRUCTION INDUSTRY

The construction industry has been infected by problems such as poor planning, cost overruns, miscalculated budgeting, low returns on assets, and inadequate material allocation. Data science comes as a rescue to solve these problems and facilitate construction project management at each level. Some of the most efficient and productive use cases of data science in the construction industry are described as follows.

• Planning and Budgeting

Big Data can benefit project teams right from the earliest stages of the construction process. Analytics tools like CoConstruct consolidate information from past and current construction projects to help teams factor in probable delays and map out the project timeline. Big Data tools also allow for more accurate cost estimates on machinery, labor, and materials, thus reducing the risk of going over budget in the later stages of the construction process.

• Design

Data-driven tools like Building Information Modeling (BIM) and Artificial Intelligence (AI) help contractors, architects, and design teams to create digital models and predict any probable design issues before construction starts.

• Construction

Construction Project Management software collects, manages, and analyses data and alerts teams about complications and schedule changes, facilitating collaboration and communication between different teams.

• Operations and Maintenance

Big Data supports and eases operations and maintenance even after the project ends. The collection of building information during construction can serve as a valuable resource for maintenance crews by providing them with access to critical structural details and insights that can assist in future renovation projects. In some cases, permanent sensors are installed to monitor structural conditions in completed buildings. Additionally, in some cases, permanent sensors are installed to monitor the structural conditions of completed buildings, and the data generated from these sensors can be integrated into Building Information Modeling (BIM) models. This integration enables monitoring of energy usage, temperature patterns, long-term maintenance requirements, and other essential metrics.

• Risk Analysis

Risk analysis and risk management are at the core of a successful construction project. This involves planning, identification, classification, response analysis, monitoring, and other processes. It aims at an estimation of future assets, outcomes, and impacts that may create complications in the project execution. This requires consideration of several factors such as technical elements, siting, and complexity. Data science tools and techniques use these and more factors and apply a probabilistic approach to perform risk analysis and enable risk management.

• Tracking Construction Assets and Equipment

There are a large number of data analysis tools that help in tracking the performance of equipment, tools, and assets of all sizes. Construction companies rely on their physical assets such as machinery equipment, vehicles, and tools to track real-time equipment inventory and to manage and allocate costs. This proves to be beneficial in the prevention of theft and equipment loss.?

• Contractor Performance Optimization

Measuring performance during execution allows us to better understand the possibilities of improvement and to make precise calculations. Many tools such as Procore, Oracle Primavera, Build7, and many others have been designed to facilitate contractor performance optimization processes.

• Robotization

The construction industry has experienced a sudden rise in automation with construction robots being used in various processes. Robots like 3D printing robots, construction robots, demolition robots, and automated vehicles act as key sources of labor productivity in the construction process.

FIVE ADVANTAGES OF BIG DATA ANALYTICS IN CONSTRUCTION

Construction companies that have leveraged Big Data have seen improvement in productivity, efficiency, and overall project costs. Some of the advantages of using Big Data are:

• Promotes collaboration and reduces construction time.
• Structural issues can be reported easily.
• Improve worker safety from data collected from wearable sensors.
• Making accurate building predictions to reduce construction waste and CO2 emissions.

DATA SCIENCE TECHNIQUES AND TOOLS TRANSFORMING THE CONSTRUCTION INDUSTRY

• Data analytics – used to process, organize, and manage data from unorganized data repositories.
• Building Information Modeling – uses data to create 3D models of past and current building projects.
• Project Management Tools – used to provide a real-time update on schedule and design changes with the use of data-driven project management software.
• On-Site sensors – used to provide data on site conditions, worker safety, and material tracking using the on-site and wearable sensors.

CONCLUSION

Data science and its application in the construction industry open new avenues to define possible risks, reduce costs, reduce time, and improve project management. With the application of data analytics and AI-powered solutions, construction companies can project future outcomes, minimize risks, take actionable decisions, and control the work of numerous contractors. With years of experience in delivering the process and expertise that underpins digital transformation, PBI Analytics serves as your strategic partner, bridging the skills gap, and providing the vital training that your project management team needs.?