Artificial intelligence in the construction industry

[edit] Introduction

Humans and animals display varying levels of natural intelligence. Thanks to integrated software programmes, machines can be enabled to perform some tasks intelligently, thinking and working in a similar way to natural intelligence. This ability of machines to act in this way is called artificial intelligence (AI) or machine intelligence.

A branch of computer science, AI is a machine's ability to imitate intelligent behaviour and learn from its operational history or experience. Typical examples of AI include speech and facial recognition, learning, predicting and problem solving. Artificial intelligence will also provide the analytical power behind self-driving vehicles navigating to their destination with human passengers while avoiding accidents.

The UK Government’s 2023 policy paper on ‘A pro-innovation approach to AI regulation’ defined AI, AI systems or AI technologies as “products and services that are ‘adaptable’ and ‘autonomous’.” The adaptability of AI refers to AI systems, after being trained, often developing the ability to perform new ways of finding patterns and connections in data that are not directly envisioned by their human programmers. The autonomy of AI refers to some AI systems that can make decisions without the intent or ongoing control of a human. (This definition also appears in the UK Government 'Artificial intelligence (AI) glossary' update, January 2024.

[edit] AI related terms

The proliferation of AI in the last decade has lead to a wider variety of terms that relate to and expand the definition, detail and uses of AI, some of these are outlined here, with definitions as published in the UK government's 'Artificial intelligence (AI) glossary' updated January 2024:

• Deep learning. A subset of machine learning that uses artificial neural networks to recognise patterns in data and provide a suitable output, for example, a prediction (UK Post Brief 57 'Artificial intelligence: An explainer'). Deep learning is suitable for complex learning tasks, and has improved AI capabilities in tasks such as voice and image recognition, object detection and autonomous driving (UK Post Note 633 Interpretable Machine Learning).
• Frontier AI. Defined by the Government Office for Science as ‘highly capable general-purpose AI models that can perform a wide variety of tasks and match or exceed the capabilities present in today’s most advanced models’. Currently, this primarily encompasses a few large language models including: ChatGPT (OpenAI), Claude (Anthropic), and Bard (Google).
• General AI. Often referred to as Artificial general intelligence, strong AI or broad AI, this often refers to a theoretical form of AI that can achieve human-level or higher performance across most cognitive tasks. See also Superintelligence below.
• General-purpose AI.Often refers to AI models that can be adapted to a wide range of applications (such as Foundation Models). See also narrow AI below..
• Generative AI. An AI model that generates text, images, audio, video or other media in response to user prompts. It uses machine learning techniques to create new data that has similar characteristics to the data it was trained on. Generative AI applications include chatbots, photo and video filters, and virtual assistants.
• Machine learning. AI that allows a system to learn and improve from examples without all its instructions being explicitly programmed (UK Post Note 633 Interpretable Machine Learning). Machine learning systems learn by finding patterns in training datasets. They then create a model (with algorithms) encompassing their findings. This model is then typically applied to new data to make predictions or provide other useful outputs, such as translating text. Training machine learning systems for specific applications can involve different forms of learning, such as supervised, unsupervised, semi-supervised and reinforcement learning.
• Narrow AI. Sometimes known as weak AI, these AI models are designed to perform a specific task (such as speech recognition) and cannot be adapted to other tasks. See also general-purpose AI above.
• Responsible AI. Often refers to the practice of designing, developing, and deploying AI with certain values, such as being trustworthy, ethical, transparent, explainable, fair, robust and upholding privacy rights.
• Superintelligence. A theoretical form of AI that has intelligence greater than humans and exceeds their cognitive performance in most domains. See also artificial general intelligence above.

[edit] Learning with AI

As a key element of AI, machines can learn, draw conclusions and act independently of humans if they have sufficient information about what is going on in their particular area of activity. The spaceship computer HAL in Stanley Kubrick's '2001 A Space Odyssey' displayed a particularly malevolent form of AI, thinking and acting against the interests of its human programmers.

AI allows a machine to learn and identify patterns and trends from gigantic quantities of real-world data (big data) without human supervision. Computers are now better than humans at playing chess - and can teach themselves how to play - and can also, for example, spot the danger signs that humans might miss on cancer scans.

Google's DeepMind research project into AI aims to develop programmes; "...that can learn to solve any complex problem without needing to be taught how". An unbeatable chess-playing computer is a typical example. The firm claimed that if the project is successful, it will be of a huge benefit, tackling some of the world's most pressing challenges, from climate change to improved healthcare.

[edit] Examples of AI in the construction industry

Using AI, the effects of billion-pound infrastructure will be predictable before planning and design, making infrastructure delivery more effective and economical. AI-empowered autonomous equipment can navigate within its surroundings without human assistance. This type of equipment can survey a proposed construction site, and create 3D maps and plans from the information it has gathered.

AI can advise engineers on the best methods to use on projects. For a super-tall building, AI systems could indicate the most optimal and economic structure.

One of the most groundbreaking applications to have come into the building industry in the past few years is Building Information Modelling (BIM). This allows the creation of a virtual model holding information that may encompass all aspects of the construction project across the building's lifecycle. Architects, engineers and others who have access to the BIM model can amend, update and consider the effect of various changes to the project under a wide range of parameters, including cost, programme, performance, buildability or clash detection. When AI processes are merged into BIM, the result will be the creation of an all-encompassing tool that gives full and intelligent control and prediction of planning, design and construction.

See also the article the long expanding list of AI tools for building planning, design, construction and management for many examples of AI relevant construction industry tools.

[edit] ChatGPT

In 2023, ChatGPT, created by OpenAI, was asked to write an article about the impacts of AI on the construction industry, it wrote this:

Artificial Intelligence (AI) has revolutionized the construction industry, with its impact felt in various areas of the field. From automating tasks to improving project management and reducing the cost of construction, AI has proven to be a game-changer for the industry.

One of the primary ways AI has impacted the construction industry is through automation. Tasks such as material handling, earth-moving operations, and other manual labor can now be carried out more efficiently with the help of AI-powered machines and robots. This not only speeds up the construction process but also increases safety, as machines can perform dangerous tasks that were once performed by human workers.

Another major impact of AI in the construction industry is in project management. AI can help manage construction projects by providing real-time data and insights into the progress of a project, helping to identify any potential issues before they become major problems. This can significantly improve the accuracy of project schedules and budgets, resulting in more efficient and effective construction processes.

In addition to automation and project management, AI has also proven to be useful in reducing the cost of construction. By optimising resource allocation, reducing waste, and streamlining construction processes, AI has the potential to save construction companies significant amounts of money. This is particularly true for large-scale projects where even small cost savings can have a major impact on the overall budget.

One of the most promising areas of AI in the construction industry is in the use of BIM (Building Information Modeling). BIM is a digital representation of a building's physical and functional characteristics, and it can be used to simulate various aspects of construction, from design to construction and maintenance. With AI, BIM can be used to automate many construction processes, such as material selection, project scheduling, and cost estimating, saving time and money while improving accuracy and efficiency.

In conclusion, AI has had a profound impact on the construction industry, improving productivity, reducing costs, and increasing safety. As technology continues to evolve, it is likely that the impact of AI will only continue to grow, and the construction industry will continue to reap the benefits. Companies that adopt AI technology will be well positioned to stay ahead of the competition, and those that do not risk being left behind.

[edit] Related articles on Designing for Buildings

• Artificial intelligence and civil engineering.
• Artificial Intelligence and its impact on the project profession.
• Artificial intelligence and surveying.
• Artificial intelligence for smarter, safer buildings.
• Artificial intelligence in buildings.
• BSRIA publishes Artificial Intelligence in Buildings white paper.
• Building automation and control systems.
• Building information modelling.
• Computer aided design CAD.
• Computers in building design.
• Generative design.
• Global building automation.
• Internet of things.
• Key AI related terms to be aware of, with UK government explanations.
• Parametric design.
• Predictive analytics.
• The impact of digital on civil engineering.
• Will AI ever be able to design buildings?

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It seems like AI is one of those watershed moments, like the creation of the internet, that will make some functions and business models redundant. However, AI will never innovate, it will only re-hash existing knowledge.