Despite significantly contributing to the world economy, the construction industry still lags behind industries like manufacturing in technology adoption.
The resistance to change adversely affects the sector due to inefficiencies in traditional methods. However, since the early 2000s, the adoption of Building Information Modeling(BIM) software has been a bright spot, continuing to gain widespread acceptance, especially for big projects.
BIM provides a comprehensive way to manage a project’s information. The different kinds of data help create a digital representation of the project, which is helpful throughout the project’s lifespan. It has profoundly impacted the construction landscape, from project design to execution and operations.
It’s possible to plug other software into BIM. This makes it possible to use an AI-based app to leverage the data found in the software. The fusion of BIM and AI promises to revolutionize BIM processes, boosting workflows like cost estimation and designing.
Ways AI and BIM Impact the Construction Industry
AI’s key advantage to BIM is its ability to analyze vast datasets and extract actionable insights. Combined, the two offer unique ways to overcome conventional practices through data-driven and automated frameworks.
Predictive Analytics
BIM provides historical and current project data that can be used to train predictive models. Machine learning(ML) algorithms like decision trees(DT), neural networks, and random forests(RF) can build a model depending on the predictive solution envisioned1.
Here are some ways BIM data can augment predictive analytics in construction projects.
- ML techniques can analyze historical and real-time data to anticipate risks and delays. BIM-AI can forecast potential disruption to schedules through weather or supply chains.
- BIM can collect data from cameras, IoT sensors, and devices embedded in building and construction equipment. An AI solution can leverage this real-time information to forecast scenarios like equipment malfunction or structural component deviations.
- AI-based predictive analytics can help stakeholders estimate overhead expenses such as administrative and equipment expenses.
