It is clear that the construction industry has become more open to the adoption of new technology and methods. The question is – what is next? Richard Fletcher, Regional Business Director at Trimble Buildings, explores the future potential of artificial intelligence (AI) within the world of BIM and construction.
As is the case with all technology, BIM is an area of constant development. We are all continually looking for ways in which we can further push the efficiency and productivity benefits that technology can offer to our detailing, engineering, fabrication and construction workflows. Parametric design, or data driven design as it is also known, is perhaps one of the most recent developments, with an increasing number of detailers and engineers adopting this way of working.
Used in conjunction with modelling software, parametric design tools allow designers to input their required rules, parameters and design algorithm and have the computer then generate the design output. Perhaps a natural progression of this is the idea of computer-driven design. Here, you can push technology further. By inputting the required parameters and allowing the computer to automatically generate various different design iterations, the technology can be used to help determine and identify the most optimum and efficient design solution.
With an increasing number of people now adopting parametric design within their BIM workflows, allowing the software and technology to have more power while still remaining in control of the inputs and outputs, the question is: what’s next?
While not necessarily new, cloudbased software, such as Trimble Connect, continues to be a great and effective way of enabling a connected workflow, facilitating collaboration and communication between project teams. Essentially acting as a huge data storage facility, a project’s BIM model, and all its associated drawings, schedules and documentation, can be stored in the cloud, ready for people to access, review and individually work on. However, what happens to this mass amount of valuable data once a project has been completed? Often, the majority will remain in the cloud, un-used and un-utilised by its owner. Yet, this could all be changed by the rise of Machine Learning and Artificial Intelligence (AI).
Put simply, AI is a form of machine learning, whereby existing information and data is used to develop its own intelligence system; to learn and to think in a similar way to humans and provide its own solutions. Typically, the more data a machine is exposed to, the better it will become at detecting and internalising patterns in said data and understanding and providing insights.
Within the BIM and construction industry, AI has the potential to successfully harness and utilise the significant amount of past project data currently unused, in turn helping
to further improve and enhance our productivity and efficiency levels.
While every building and structure is itself unique, detailing and modelling tasks can often be repetitive by way of nature and design. For example, different concrete panels, steel beams and columns and their various connection solutions can all be commonly found within a design project. It is these similarities in data where the potential for automation arises; enabling a company to utilise its experience and known good design choices from past projects to help automate, design and optimise the new. For example, consider the task of detailing a complex steel connection.
Through the use of AI and machine learning, it is possible that BIM software (in the future) may be able to detect patterns and similarities between a user’s new model and their previously completed designs, automatically suggesting and recommending design solutions based on these past projects. In this case, the optimum design could feature fewer welds, fewer bolts or even less steel, saving money and materials, as well as being quicker and easier to fabricate offsite and assemble on site.
It is clear that such automated technology could deliver very real time-savings, both in terms of the initial detailing work and also improved accuracy, resulting in less required rework. However, it could also contribute towards achieving the most optimum and efficient design. Imagine if AI technology was able to look at completed designs and categorise what worked well and what didn’t; taking this existing data and using it to improve the new. Collaborative platforms could take this even further, potentially feeding fabricator and construction information, including costs and time, into this. The result would be new BIM designs that are driven by, not only design, but fabrication and construction. What was easy to fabricate? What was easy to install? What was most cost-effective? What was most successful?
Ultimately, however, the success of AI in these complex environments, such as BIM, depends greatly on acceptance. There has to be a sense of trust – trust and confidence in the solutions that such automated and machine-learned software suggest – if the industry is to benefit from such technological advancements. Only then can we truly reap the rewards of our technological advancements.
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