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AI in Construction—What Does It Mean for Our Contractors?

Artificial intelligence is revolutionizing the construction industry by enhancing efficiency, safety and decision-making throughout the project lifecycle. AI in construction involves the application of advanced technologies like machine learning, computer vision and data analytics to various construction processes. Through AI, machines can learn and imitate human cognitive functions.

The importance of AI technology in construction should not be underestimated. It can help companies complete projects on time, minimize staffing challenges, save money and address safety concerns. AI learns from the data provided to it. It can adjust project plans based on the information it receives, allowing decision-makers to alter those plans or change them to improve safety or minimize inefficiencies.

It can enhance productivity, reduce costs, improve safety and promote sustainable practices, making it a vital tool for the industry’s future growth and development. The possibilities may sound endless, but as an industry traditionally looking from the outside in at technology, we must first step back to educate ourselves on the basics. This resource is meant to act as a starting point in your journey to understand AI and its potential impact on the construction industry. By reading through definitions, construction use cases and considerations, the reader should walk away with a level of knowledge to ensure they can actively participate in future conversations on AI in construction.



Definitions



Artificial Intelligence

Per The National Artificial Intelligence Initiative Act of 2020: “A machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments.”

Machine Learning

Application of AI that allows a system to automatically learn and improve from experience. In other words, machine learning helps computers do tasks like recognizing colors, finding pictures of cats on the internet or even suggesting what to watch on TV. It’s like teaching the computer to be smart and make decisions by looking at lots of examples and learning from them. One common example of this are the Large Language Models.

Deep Learning

Per IBM: “Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to ‘learn’ from substantial amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy.” Deep learning has achieved remarkable success in various applications, including self-driving cars, medical diagnosis, recommendation systems and more. Its power lies in its ability to automatically learn and adapt to new data, making it a cutting-edge technology in the field of AI and data analysis.

Generative AI

A type of AI that can creates new data or content, such as images, text, music or even videos, by learning patterns and structures from existing examples. It works by understanding and mimicking the patterns and styles it has seen in the data it was trained on. The most publicly recognized tool in the last year is ChatGPT, built by the company OpenAI. ChatGPT is an artificial intelligence chatbot that can process natural human language and generate a response. It has revolutionized how we interact with computer systems and has influenced the evolution of AI. Additional generative AI tools from other major technology companies include: Meta’s Llama 2, Microsoft’s Copilot, Google’s PaLM 2, Amazon’s Bedrock and Dall-E 2, also from OpenAI.

Predictive AI

A type of AI that uses data and machine learning algorithms to forecast future events or trends. It helps businesses and organizations make informed decisions by analyzing historical data, identifying patterns and making predictions based on those patterns.

Project Lifecycle Impacts/Examples



Preconstruction

Predictive Analytics: Analyze historical project data and current conditions to optimize construction schedules, resource allocation and task sequencing.

Alice Technologies—End users can harness the power of artificial intelligence to enhance construction planning and scheduling abilities to keep jobsite labor moving

Optimized Design Development: Allow project stakeholders to identify the best design for a building based on real-world data; Rapidly create and explore a variety of unique design options.

Hypar’s artificial intelligence function lets you describe a building and turn your text into a quantifiable building model

Augmenta—A fully automated building design platform in the cloud, built from the ground up around generative AI. It creates highly cost-, labor-, time- and energy-efficient designs that are fully code compliant, error-free and constructible

Construction Drawings:
Blueprints AI—An advanced artificial intelligence-powered tool designed specifically for the construction industry to automate the takeoff process. This tool significantly reduces the time and effort required for construction estimators to quantify materials and costs from blueprints and plans

Stack—STACK Assist, its Artificial Intelligence functionality automating takeoff tools for contractors, will allow contractors to use measurements specific to the trades they need, and AI will perform takeoff and counts automatically.

Togal.AI-—After uploading construction drawings, state-of-the-art artificial intelligence modeling will complete as much of the takeoff as possible

Supply Chain: Throughout the procurement process for self-performing contractors, artificial intelligence will empower the purchasing team to quickly identify availability and best pricing within a certain region.

SubBase—Effortlessly streamline invoice reconciliation through a centralized inbox, utilizing AI for automated logs, cost code confirmation and a custom-digitized approval workflow

Kojo—simplifies the complex task of material sourcing, saving time, reducing costs, and enhancing project outcomes. Using state-of-the-art technologies like OpenAI's GPT-4 and Hugging Face transformer models, along with comprehensive statistical and machine learning methods, the Kojo Intelligence Layer helps contractors efficiently find project-appropriate materials at optimal prices

Contract Review: Empower legal teams to quickly identify critical risk factors in construction contracts

Construction

Autonomous Equipment:
AIM—Enable existing equipment to run at full utilization every day of the year, in any weather, without an operator and with 360-degree safety technology preventing any accidents

Project Management:
Smartapp—Brena.AI is an AI-powered assistant developed by Smartapp that helps automate various tasks in construction projects. It generates lookahead schedules, automates job reports, updates progress and also enhances safety through PPE recognition and multilingual support while reducing injuries and improving onboarding efficiency.

SmartBuild—At the core, Smartbuild’s intuitive and easy system has the necessary process and performance timelines to manage projects for success. In partnering with Microsoft, Smartbuild users gain access to seamless operations and informed decision making through the Azure AI platform

Procore Copilot—Artificial intelligence-powered conversational and predictive experience that will provide customers the ability to automate time-intensive, manual processes across the Procore platform

Autodesk—Construction IQ delivers automated risk analysis of quality and safety data from Autodesk Construction Cloud to help projects advance faster and with less risk. AutoSpecs in ACC is an automated submittal process that helps users generate submittal logs in minutes and leverages construction IQ to suggest potentially missing items

Computer Vision/Intelligent Site Monitoring: Increase safety and security on jobsites. Through machine learning, video footage is trained to detect things like the number of workers entering/exiting the jobsite, workers in proximity of heavy construction machinery and even safety violations, such as the lack of face protection while saw-cutting concrete

Safety:
Dozer—Computer vision for jobsite equipment; With a 360-degree view, the cameras can see the cabin, bucket and everything in between. Proprietary artificial intelligence models calculate a depth map of the surrounding area and constantly monitor various elements of your jobsite

Labor Tracking:
AlwaysAI—Computer vision enables existing cameras to immediately identify and interpret objects in the physical world; Manage direct labor and material costs more efficiently and provide a safer and more secure working environment by leveraging existing camera infrastructures with practical AI for construction

Jobsite Mapping:
DroneDeploy—Mapping application that uses artificial intelligence to process images; DroneDeploy uses machine learning to decode images and find patterns that are invisible to the human eye



Building Maintenance

Energy Management: Analyze energy usage patterns and optimize HVAC systems to reduce energy consumption and overall costs

Predictive Maintenance: Through the expanded use of building automation and control networks, AI can predict when building equipment is likely to fail, allowing for a proactive response

HR Office Considerations



Per insights from Littler professionals, the following are things to consider when drafting inter office AI policies. Please also note that ‘insights’ do not constitute legal advice.

As construction technology continues to be a driving voice in the industry, there are also discrete factors that need to be considered, particularly around the use of artificial intelligence within our work environments.

Some of the examples listed in the prior sections, such as the use of generative AI, may be more commonly embraced quicker than others in the construction industry. HR professionals will need to consider asking questions on the use of AI in the office. Before laying out a blanket policy, clearly define the purpose of the AI usage policy, which may include what AI technologies are covered and how it applies to employees and/or outside stakeholders.

An AI usage policy should include a purpose or mission statement, an AI definition section, an explanation of who the policy applies to and a policy that allows for open use or prohibits or limits AI use. Designate certain point people to oversee AI usage, to troubleshoot problems if they arise and who can approve of AI use. Clarify a policy that instructs employees that programs like ChatGPT still makes a lot of mistakes and that these programs should be used to assist employees and not serve as a substitute.

Training and awareness are key to ensure employees are well-informed about the AI usage policy and how it impacts their roles and responsibilities. Consider training managers on AI use. As is the case for most technologies, human interaction is still an important factor. Consider an overall approach that monitors AI use and encourages innovation, but ensures that AI is only used to augment internal work and with proper data.

Conclusion



Artificial intelligence has been in the background of some of our everyday technologies, but in the last year has come closer to the surface thanks to strategic marketing and perhaps a more consumer-friendly approach. There are still a lot of unknowns on what the impact will be and what the technology could look like in the next few years.

First, we need to consider how AI will increase productivity and eliminate many of the manual entry tasks that bog down our days. We can obtain the ability to augment the search for knowledge and completing tasks.

Secondly, we should continue to push closer and closer towards becoming a digital workforce. Artificial intelligence and the many layers involved in its functions rely heavily on clean and consistent data. This leans into the transformation of our workforce: Who is managing this data? How are we managing the data? How are we using the data in an effective manner?

The existing gap between academia and skilled trades is closing in, which offers the industry immense opportunity to continue to evaluate how we deliver projects on time, within budget and safely. As Director of Product Management at Autodesk Pat Keaney puts it, “AI undoubtedly has wide-reaching implications for the construction industry. In construction, how you build should be as rewarding as what you build, and implementing AI into the construction process will help the industry improve the quality of construction jobs and make workers safer and more productive”.

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