Predictive Analytics in Preventing Construction Disasters

Seeing the future with absolute certainty is impossible, as disaster strikes in the most unexpected ways. However, predicting the future with incredible accuracy is becoming more possible due to the rise of machine learning and data analytics. This expanding area of computer science and artificial intelligence is allowing organizations to anticipate situations by analyzing a massive database of past behaviors and their associated outcomes. 

This practice – known as predictive analysis – is becoming an important component of strategic project management across banking, healthcare, HR, marketing, and countless other industries. Organizations can more confidently predict everything from customer loyalty and stock prices to disease outbreaks and equipment failures. And emerging at the forefront of this analytical revolution is the construction industry, which is looking to its history of innovation to build a new era of efficiency, growth, and modernization. 

Defining Predictive Analysis 

Predictive analysis is a branch of advanced analytics that makes predictions about future outcome and trends based on previous scenarios. Closely associated with big data and data science, predictive models use historical data combined with statistical modeling, data mining techniques, and machine learning. Many models categorize data based on historical relationships or similarities between data points, while others use inputs at specific time intervals to assess seasonality, trends, and cyclical behavior to make predictions. 

Predictive modeling is a powerful tool to help organizations find patterns in data and identify risks and opportunities, but it also carries significant pitfalls. Models trained on biased or incomplete data will output similarly biased or unreliable results. Predictive models that aren’t thoroughly trained and validated can lead to poor generalizations about future events. And an overreliance on predictive outputs that does not also consider domain knowledge can impact accuracy and reliability. 

Predictive Analysis in Construction 

In construction, predictive analytics can offer valuable insights and help project managers anticipate challenges, optimize resources, and mitigate risks effectively, ultimately improving decision-making and solving business problems. With a variety of moving and interrelated parts – subcontractors, material availability, change orders, weather – predictive analytics can potentially provide a level of predictability in an often-unpredictable environment, as is seen in tragedies like the Francis Scott Key bridge collapse. In fact, experts state that “this disaster reveals how exposed America’s critical infrastructure is to sudden and devastating accidents as well as intentional destruction,” sparking a new call to action for more timely repairs to our nation’s aging buildings, bridges, and other important structures – and predictive analytics may be one viable solution. 

Here are a few ways predictive analytics can be applied to construction projects: 

• Cost Estimation: Predictive analytics enable construction teams to create more accurate, and realistic budgets by accounting for most, if not all, possible factors that could impact project costs. This includes regional labor costs, material expenses, and other variables, ensuring that budgets are comprehensive and accurate from the outset. 

• Procurement Optimization: By analyzing historical data on supplier performance, construction companies can identify suppliers with the highest performance, positioning them to negotiate better terms. 

• Scheduling: Using data from similar projects in specific areas allows predictive analytics to better estimate and optimize project timelines. Project managers can then allocate resources effectively and plan for contingencies, ultimately ensuring projects finish on time and within budget. 

• Risk Identification: A construction company using predictive analytics can better identify potential risks and delays in complex projects. Analysis of weather patterns, labor productivity, and material availability data can inform companies of issues before they escalate, thus minimizing disruptions and maximizing efficiency. 

• Worksite Optimization: Predictive models can help optimize worksite layouts and resource allocation. By predicting resource needs and potential bottlenecks, construction companies can streamline operations and improve efficiency. 

• Improving Outcomes: Despite project variability, predictive analytics can help project teams assess market conditions, portfolio composition, and individual project performance to strengthen their business proposition. 

Predictive analytics is becoming increasingly indispensable to construction companies looking to enhance project outcomes, manage logistics, and mitigate risks. Predictive analytics tools are essential to improving the quality, productivity, and financial outlook by enhancing strategic decision-making, driving business growth, and improving competitiveness. 

Building Your Degree in Construction Management 

Capitol Technology offers several degree programs in Construction and Critical Infrastructure management, including our Online Bachelor of Science (BS) in Construction Information Technology and Cybersecurity. As a student, you have access to resources tailored for your success such as our Center of Excellence in Construction Management (CECM) that prepare you to be a part of the predictive analytics solution and its critical applications in the industry. For more information, visit our website or contact our Admissions team.