The area of artificial intelligence- AI gives out a unique opportunity to transform the process of constructing the built environment.
The implementation of AI solutions is not just an issue of gaining a competitive advantage, but it also plays a major role in the construction industry’s efforts to address other significant challenges that happen to be transforming the way projects are scheduled, constructed, as well as managed. These challenges include decarbonisation as well as meeting the demand for net-zero emissions.
AI has in it to be applied in various ways during the design and construction stages of a project’s asset lifecycle. Apparently, the construction industry should take advantage of these chances.
Generative design
Generative design, which happens to be powered by AI, offers a variety of appropriate solutions so as to address specific engineering challenges. These solutions can be further personalised by the user to meet that specific requirement.
Instead of struggling to consider constraints as well as parameters while designing, designers come equipped with the option to inform the software about the specifications and limitations. This can include elements that range from strength and agility to cost and performance. The generative design process will then analyse these inputs and thereby generate potential combinations that meet the specified wants.
AI’s advanced algorithms along with machine learning capabilities enable designers and architects to come up with innovative solutions, leverage energy efficiency, elevate structural integrity and at the same time\ make the design process seamless.Â
Generative design has been shown to offer numerous benefits. According to Autodesk, which happens to be one of the software companies, the utilisation of generative design can lead to a prominent reduction when it comes to material costs by 30% and also offer a decrease in construction time by 40%. The execution of AI-assisted structural analysis has the potential to decrease design time by 50% while also maintaining structural performance. Additionally, the US Green Building Council suggests that incorporating the AI-driven sustainable design practices can lead to a reduction of energy consumption when it comes to buildings by up to 30% and a reduction when it comes to carbon emissions.
BIM-based project estimates
Building Information Modelling refers to a collection of 3D design and also modelling software tools that are widely used within the construction industry. These tools are specifically designed so as to assist in visualising construction designs from numerous perspectives. Integrating AI capabilities into the BIM can bring about various advantages, such as the ability to perform quantity take-offs, that go on to involve extracting essential information regarding the necessary materials.
BIM-based quantity take-offs provide more precise as well as reliable results and also allow contractors to enhance efficiency, reduce costs, and boost the overall quality of design, construction, and operation.
Although the benefits of digital quantity surveying, which relies on a digital model of a project, are clear when it comes to accuracy and time savings, there are also challenges that arise during the switch to digital. One important issue to take into account is the people factor. How can the industry effectively attract the necessary talent, and can it provide incentives and rewards to retain as well as motivate this talent? The required skills extend beyond just digital expertise. In a scenario where the BIM model is incorrect and the individuals involved lack the fundamental quantity surveying capacities to identify the mistake, the question arises as to who bears the ultimate ownership when it comes to precision of the BIM model.
Why is the construction vertical one of the least digitised sectors and also slow to embrace new technology, despite its significant potential? There are typically two reasons.
Many machine learning systems function as ‘black boxes’, meaning they do not provide explanations for their conclusions. Moreover, the algorithms used to make choices are often proprietary and not easily understdable, which could potentially result in construction professionals finding it hard to figure out the reasons behind and the methods used when it comes to making recommendations. Certain academic commentators argue that the potential of AI in the construction sector may be restricted unless an explainable artificial intelligence approach is looked into.
Construction companies often find it very challenging to understand the benefits and implementation of AI-enabled systems because of their high investment costs involved. Making investment decisions can be challenging, especially when there are numerous contractors involved who deliver work by way of multiple subcontractors. This complexity makes it difficult for the industry to come to a consensus on a single, accepted AI-enabled solution. This also raises the possibility that an investment may become obsolete or unnecessary in a short span of time. It is understandable that organisations may be hesitant to invest for the future, considering the monetary challenges experienced throughout the sector.
From a legal standpoint, the utilisation of AI-enabled critical path programmes available in the market could potentially reduce disputes and thereby consequently eliminate the requirement for expert programming witnesses. However, if the conclusions cannot be adequately explained and if the reasoning behind them cannot be tested and questioned, why should those individuals whose commercial interests rely on the outcome be willing to accept it? How does it help if each member of the supply chain is using different programming algorithms, potentially resulting in different outcomes? Governments and policymakers have a clear role to play in this matter.
At this stage, it is possible that one may witness the emergence of a two-speed industry. In this scenario, certain companies are emerging as winners, while the obstacles for others to enter the fast lane are becoming increasingly difficult to get past. Information asymmetry may arise when one party has possession of AI solutions while the other party does not. Consolidation across the supply chain and synchronisation of the supply chain around specific segments may also occur, utilising AI solutions specific to each segment.
