How AI is Used in Construction

AI is being used in the construction industry to develop more environmentally friendly and cost-effective projects by helping to avoid safety concerns, labour shortages, or mistakes during scheduling or ordering. AI can analyse vast amounts of data, at a rate and accuracy far beyond the scope of humans, and offer enhanced data visibility and insights. With a huge range of AI tools being introduced into the market, let's look at how AI can lead to quicker, more accurate, and precise decisions within the construction industry!

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1. Understanding AI, Machine Learning and Deep Learning
2. AI in project planning and management
3. AI in design, maintenance and quality control
4. AI in safety 
5. AI in supply chain optimisation 
6. AI in robotics and automation
7. AI in making construction more environmentally friendly
8. Concerns with using AI in construction

1. Understanding AI, Machine Learning and Deep Learning

Artificial Intelligence covers any effort to enable computers to match or even surpass human intelligence. This includes recognising patterns, learning from data or pre-set algorithms, and 'understanding' language and images. Machine Learning, a subset of AI, allows programs to learn and uncover insights from data. Another faction of ML, ‘Deep Learning’ is used to figure out common elements between data sets using 'neural networks'- the ability for algorithms to make connections between data in a similar way to the human brain. 

• AI: Any effort by a computer program or algorithm to mimic human intelligence. 
• ML: The ability of a program to use massive data sets to independently come up with responses and 'learn' from data.
• DL: A more specialised sub-sect of ML with the ability for.a program to find patterns within data; including speech and images.

In construction, algorithms can be trained on ‘Big Data’, (decades worth of project data) to recognise patterns in budgets, ideal scenarios, and potential pitfalls, helping the program to 'learn' and provide detailed recommendations. These technologies are becoming increasingly useful for improving efficiency, analysing images, identifying design errors, predicting project needs, and managing large data sets. 

2. AI in project planning and management

AI systems analyse vast data from multiple sources to help managers in making informed decisions, particularly decisions concerning budget. By analysing huge amounts of past project data, AI can forecast how long each project phase will last, plus aid in scheduling and ordering supplies. AI predicts cost by considering factors like project size, contract type, and even project manager competence.

AI can help streamline key aspects of project planning including:

• Creating faster and easier communication channels.
• Monitoring the performance of each team.
• Assessing required resources and allocation.
• Figuring out the order of tasks and spotting any possible problems.
• Budget planning, including risk areas and cost management strategies.
• Generating a detailed construction timeline with milestones and review dates.
• Creating a contingency plan for unforeseen events.

AI empowers general contractors to analyse real-time changes quickly, exploring multiple potential routes in the construction timeline. For example, AI can calculate the advantages and costs of different variables, like adding more labour or equipment, leading to improved scheduling and resource management, without sacrificing the quality of labour or materials.

3. AI in design, maintenance, and quality control

AI’s ability to ‘read’ architectural designs and plans allows for increasingly accurate assistance in noting potential safety shortfalls in the designs, enhancing logistics, and generating alternatives. They can suggest design changes to enhance energy efficiency, material usage, and more efficient and sustainable designs. Different AI programs can even model what-if scenarios and compare different versions of a design, all while assessing the project's estimated costs. 

4. AI in safety monitoring

AI-powered surveillance systems on construction sites can identify safety hazards and alert workers to potential risks. These AI-capable video systems can detect when workers are not wearing proper safety gear or are in unsafe areas. AI also streamlines incident reporting through sensors, cameras, and IoT (Internet of Things) devices, gathering real-time data and logging any incidents. The ability to log this data also provides better training and reporting, to avoid future incidents. 

5. AI in supply chain optimisation

At the time of writing, AI solutions can automate about 40% of the sales process workload by predicting material needs and automatically ordering supplies, reducing waste and delays. With the ability to track everything from the amount of supplies needed to the best routes for supplies to travel, AI is helping cut down the frustration and delays associated with transporting the supplies for large projects.

6. AI in robotics and automation

While there is a fair way to go until we see robots pulling on a high vis and getting on the tools, better network connections and AI are enhancing real-time analysis and adaptability to unforeseen site conditions. This means we're not too far off robots that can respond to unexpected changes and challenges on construction sites. Currently, robots powered by AI are being put to work for tasks that are either repetitive or pose a safety risk, like laying bricks, welding, or 3D printing building parts and can be done while stationary. 

7. AI in making construction more environmentally friendly 

Sustainability is a growing concern within the construction industry, with some reports claiming the industry is responsible for up to 50% of climate change. As such, introducing systems to reduce waste, increase efficiency and allocate resources is a key part of the mission to reduce harmful consequences.  

• Energy optimisation: AI algorithms can plan for ways to introduce more natural light or increase energy efficiency within a building's design. 
• Waste reduction: AI can predict the materials needed, reducing over-ordering and minimising waste. It can also suggest ways to use recycled materials.
• Resource management: AI can forecast the need for resources, reducing excess consumption..
• Efficient logistics and supply chain management: AI can plan better transportation routes and schedules for material delivery, reducing fuel consumption and emissions.
• Automated compliance monitoring: AI can monitor and ensure compliance with environmental regulations and sustainability standards throughout the construction process.
• Carbon footprint analysis: AI tools can calculate the carbon footprint of construction projects, helping companies to make more environmentally friendly choices
• Lifecycle assessment: AI can assist in performing lifecycle assessments of buildings, considering all stages from construction to demolition, to ensure the most sustainable practices throughout a building's life.
• Predictive maintenance: AI can predict when building components will need maintenance or replacement, thereby preventing resource-intensive emergency repairs.

8. Concerns with using AI in construction

However, AI usage within the construction industry is not without its challenges. One of the biggest challenges in using AI in construction today is that AI is only as good as the information you give it. The variability and quality of data used to train AI systems can result in biased or incomplete answers, although this is changing as more uniform reporting is introduced. However, human oversight is still crucial, particularly as AI systems can replicate existing biases and might not fully grasp the complexity of real-world scenarios.

• Data quality: The effectiveness of AI is constrained by the quality and consistency of the data it learns from.
• Subjective nature of project assessments: Some projections for milestones or a job well done are subjective and may not accurately reflect a global standard.
• Biases: Because they're based on past standards and average expectations, AI algorithms can unintentionally perpetuate societal biases.
• Need for human expertise and oversight: AI should augment, not replace, skilled labour and still requires human supervision.

Construction has traditionally relied on manual processes, complex scheduling, and intensive labour, often encountering challenges such as delays, cost overruns, and safety concerns. AI's integration into construction is a game-changer, offering unparalleled efficiencies and insights. While its challenges are noteworthy, its potential to revolutionise the industry is undeniable.

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