AI in BIM: Driving Innovation in Construction Technology

Introduction

Building information modeling has been one of the most notable advancements in the construction industry over the last decade. It offers a particular approach that is drastically different from most industry processes, resulting in better performance and a smaller amount of rework. The technology behind the concept of BIM has not stopped improving, either, with better workflow management, more straightforward integration, and other beneficial capabilities introduced regularly.

BIM is getting more and more recognition as time goes on, with better software and higher adoption rates. With the relatively recent rise of artificial intelligence, it should not be very surprising to learn that there are plenty of capabilities in BIM that are either AI-assisted to some degree or moving towards that goal. The capabilities of AI in BIM are only going to get better over time, but knowing the basics of these concepts is a necessity to be able to take advantage of the full scope of BIM’s capabilities.

Existing capabilities of BIM

At this point, BIM is already an integral part of the construction industry worldwide simply because of the many advantages it offers compared with the traditional approach to project management. It completely changes the design, planning, and execution phases of the process by providing a centralized collaborative platform that allows for easy and convenient data sharing and cooperation at any point in time.

The process still has plenty of disadvantages, such as limited automation, complex integration processes, and many other motivators to make current implementations of BIM even better. Most improvement processes involve the use of the latest technologies to a certain degree. Artificial intelligence is one such technology that could make BIM even better than it is now (and it already has, to a certain degree).

Most notable advantages of integration of AI with BIM

1. Improved efficiency

AI-driven algorithms are much better and more thorough when analyzing existing information, such as project models, due to the ability to learn from large data sets. That is not to say that fundamental analysis capabilities are not already present in many BIM solutions to a certain degree. However, AI-driven versions of the feature should be significantly better at the same task than their legacy counterparts.

The introduction of AI into the design analysis pipeline reduces the number of errors and the amount of rework, improves the accuracy of simulations, and can even offer a variety of accurate predictions for the structure in the future, significantly enhancing the decision-making process.

There are also multiple examples of BIM solutions providing automatic geometry classification capabilities, which use the power of AI to drastically reduce the time it takes to assign specific categories and parameters to each element of the BIM model. One prominent example of such software is BricsCAD.

2. Enhanced planning and design capabilities

General project optimization efforts can also be assisted with the power of AI, with one notable example being the ability to analyze and suggest more optimal positioning for certain design elements, such as plumbing or HVAC. The analysis capabilities of BIM software such as clash detection are also capable of benefiting greatly with the introduction of AI-based information analysis.

AI-powered BIM software can easily collect and analyze information from various sensors and other hardware and notify the manager if it detects an irregularity in the project model. Of course, both of these capabilities existed in BIM software before the introduction of artificial intelligence, but we mention them here because AI can greatly enhance the performance of these features and processes.

While the ability to perform detailed cost estimation has been a notable element of BIM software for a while now, AI can still assist it in some ways, including better quality control for the overall information gathered from sensors and other hardware (as well as the ability to alert the manager if the system detects anomalies in the sensor data).

That is not to say that only project-centric information can be analyzed with the help of AI, far from it. The correct implementation of AI should also be able to perform a multitude of other tasks, including layout analysis, labor schedule analysis, and more. All of these analytical capabilities also come with the ability to suggest improvements and changes based on the information collected.

3. Better integration with new technologies

AR integration has been one of many technologies that BIM has benefited greatly from, offering an interesting approach to the project review process with an unprecedented level of detail.

The introduction of AI BIM into the mix should significantly decrease the effort it takes to transform regular BIM models into their AR-ready counterparts, enabling all of the advantages of AR from the early design phases to post-construction maintenance or eventual disassembly.

4. Improved sustainability

Information analysis capabilities may also make AI BIM far more efficient in general project optimization, especially from the viewpoint of sustainability. Analyzing existing project designs for improvements to material use and better energy efficiency is just one aspect of what AI should be capable of in the context of BIM tasks.

The same logic applies to material management, since AI BIM should be able to analyze the existing combination of materials and design choices and suggest other materials for better sustainability, higher performance, lower price, and so on.

General limitations of BIM and AI

At the same time, the introduction of AI elements will not solve all issues with BIM. BIM software remains relatively expensive, especially for smaller industries, and many people still think of BIM as nothing other than a detailed 3D project model.

BIM itself has also struggled a lot with standardization. Dozens of proprietary file formats make data sharing in the context of BIM extremely problematic, especially in larger projects. The overall complexity of BIM as a branch of software also makes education and training extraordinarily challenging and expensive in most situations.

That is not to say that artificial intelligence does not have its share of issues, both within and outside the context of BIM. For example, all modern machine-learning models have to “learn” from massive pre-existing data pools, and the quality of this data is directly responsible for the effectiveness and value of machine learning in the future.

Additionally, while BIM may benefit significantly from receiving template-like models made by AI based on client preferences, this type of workflow also tends to drastically limit the overall creativity of the design process. A delicate balance must be kept between a template-oriented workflow and a design-centric orientation for AI to prove useful without offering standardized but uninspired projects to solve every task.

Future prospects of BIM

Building information modeling has been incredibly advantageous for the construction industry in a relatively short time. The technology has also been improving regularly, offering its users new and improved features. BIM will continue improving its integration with both new and existing construction software, and deeper integration with hardware such as Internet-of-Things sensors and drones should raise BIM’s capabilities to an entirely new level.

The introduction of AI into existing BIM software provides a fair share of improvements, and both technologies will only improve as time goes on. Unfortunately, the number of issues with this technology is now also higher due to the introduction of AI into BIM workflows.

The existing issues of BIM in terms of its price, complexity, and standardization are combined with the most significant problems of artificial intelligence, including data quality issues, the necessity of maintaining a balance between standardization and creativity, and more.

That is not to say that these technologies are not worth using because of their disadvantages. On the contrary, the advantages of BIM and AI are too significant to be canceled out by potential shortcomings, and both of these solutions should only get better over time. However, it is essential to know about the limitations of these technologies to use the combination of BIM and AI to its fullest.

Frequently Asked Questions

What is the biggest advantage of implementing AI in BIM?

“Efficiency” is the most cookie-cutter way to describe the advantages of Artificial Intelligence in the context of BIM. AI can enhance the performance of different processes while also reducing the number of errors in features such as simulation, clash detection, resource management, etc.

What are the most significant challenges of AI in BIM?

Despite their overall effectiveness, AI algorithms still struggle with two overarching issues: data quality and the issue of balancing between creativity and template-driven standardized content. Unfortunately, many of these disadvantages are also present in the AI features of BIM solutions.

Is there any way AI can assist with sustainability efforts in the context of BIM software?

One of the most significant ways AI BIM can contribute to sustainability efforts is by performing extensive analysis of the structure of projects to improve energy efficiency and suggest alternative materials for certain elements that could prove more energy-efficient in the long run.