It was intended as a sly swipe at the ballooning hype around artificial intelligence, the bulletin board At a construction site in Antwerp, Belgiumin June I read “Hey ChatGPT, complete this build.”
Artificial intelligence, the technology that powers chatbots like ChatGPT, won’t be assembling apartments or erecting stadiums anytime soon, but in construction — an industry known stereotypically for clipboards and Excel spreadsheets — the technology’s rapid adoption may change how quickly projects finish.
Drones, cameras, mobile apps and even some robots are increasingly mapping real-time progress on sprawling job sites, giving builders and contractors the ability to track and improve project performance.
Forget about the skyscraper-building robots, said James Swanston, CEO of Voyage Control, which makes project management software for construction sites. “It’s key, getting the data you need and then putting it to better use.”
The construction industry has it It has long been considered a digital lag, but architects regularly use digital tools to design projects and create blueprints. It’s common to see tablets and drones on the same job sites as hard hats and safety vests.
Now helmet-mounted cameras take footage from a coordination site as new crews or materials arrive, and microsensors can detect if the new window is a few millimeters off the project outline and needs adjustment. And artificial intelligence has begun to be used in buying and selling real estate: JLL, a global broker, was introduced recently his chatbot To provide insights to its clients.
This expanded analysis of data lays the foundation for what many hope will be significant improvements in accuracy, speed, and efficiency by reducing bloated schedules and the waste that has made construction increasingly costly.
said David Jason Gerber, a University of Southern California professor whose research focuses on high technology. in construction.
But the industry’s embrace of AI technology faces challenges, including concerns about accuracy and hallucinations, in which the system provides an incorrect or illogical answer.
And gathering more data has been a complex problem, due in large part to the nature of massive construction projects: no two developments are the same, with wildly varying terrain and local regulations, and new teams of contractors and subcontractors brought together for each project. It’s like starting a multi-million dollar business for every major project.
Ballet’s complex coordination of supplies, labor, and schedules remains a daunting task. But startups and investors see an opportunity, especially as machine learning models, which ingest vast amounts of data to discern patterns and predict how similar situations will progress, are being used to improve project performance.
Sarah Liu, a partner at Fifth Wall, a venture capital firm focused on real estate investments, said the pandemic has already prompted builders to adopt more digital tools to allow them to work on site during lockdowns, accelerating the development of new technology.
“The best companies don’t promote themselves as AI companies,” she said. “They promote themselves as problem-solving companies.”
Construction consultancy nPlan, led by Dave Amratia, who helped craft Britain’s national AI strategy, uses complex algorithms to chart the progress of massive infrastructure projects and avoid errors or supply gaps. Its machine learning system is trained on a database of more than 740,000 projects.
The company’s largest project to date, an $11 billion overhaul of railway infrastructure in the north of England, will use lessons learned from studying that wide range of projects to create detailed project maps in real time for builders, which are expected to account for up to 5 percent of Total cost.
Buildots, an Israeli startup that provides project management guidance via wearable cameras that analyze construction progress, has signed off on its first project in the US, Mixed-use development in Manhattan. a company Commissioned study Of 64 international construction sites, only 46 percent of the average worksite was found to be in use at any one time, evidence of poor organization and scheduling.
“On the best construction site we studied, progress varied by 30 percent each week,” said Aviv Lipovici, the company’s chief production officer and one of its founders. “I think there are huge shortcomings in this industry.”
Construction companies have also made significant investments in their in-house technology. Avison Young’s project management services division claims that its proprietary software and management can, on average, cut development time by 20 percent.
A subsidiary of Suffolk, a large Boston-based construction company, has invested $110 million to fund startup construction projects. Suffolk has a team of 30 data analysts who collect and sift through information from job sites. At a construction site for Boston’s South Terminal Tower, a 51-story development by Hines, cranes contain cameras that document and label the steel used in the building’s framework, creating a data set expected to be used in other projects in the future. Additional software is used to track progress and even predict crashes.
“We don’t have an unemployment rate in the industry; technology will help existing workers do more,” said John Fish, Chairman and CEO of Suffolk. “Artificial intelligence will replace companies that don’t use AI.”
There is a fear of AI, and the issues being accurately reported, that it is being used in an industry where safety is very important. Software like ChatGPT has an unfortunate tendency to sometimes generate answers based on incorrect predictions, said Julien Moutte, chief technology officer of Bentley Systems, a construction software company.
“In infrastructure, this is something we cannot afford,” he said. “We can’t make AI hallucinate about the design of the bridge.”
But the claimed ability to operate faster and cheaper has proven attractive. Dusty Robotics, a technology company in Mountain View, California, is developing autonomous devices that track blueprints on construction sites, a task typically performed manually. While researching the industry, the company’s CEO, Tessa Lau, noticed workers measuring plans with chalk and tape; Some workers have even tried sticking pens to Roombas.
Ms. Lau worried about the backlash it would have for workers to encroach on their workplaces by robots and artificial intelligence. But in an industry desperate to attract young workers, offering potential trainees the ability to use drones and robots could help with recruitment and retention.
Tony Hernandez, a union woodworking instructor in Northern California who teaches apprentices to use drones and dust bots, sees these technologies as “just another tool.” He would prefer the robot to follow lines rather than having to crouch and track itself, which means less wear on its knees.
“This is a great retention tool,” he said. “I’ve brought kids who grew up on Xbox and can discover these tools in a five-hour class.”
Dusty has 120 units in locations across the US, but this is just the beginning. Ms. Lau calls the modules, which can collect gigabytes of data, “Trojans for training the AI of the future.”
Risk reduction may finally be where this technology makes its mark. Depending on the location and nature of the business, insurance can make up to 10 percent of the cost of a single project, which can easily run into the hundreds of millions of dollars. Now, with AI providing better ways to get the job done, there are fewer risks and cheaper insurance options.
Shepherd, a startup insurance company, uses construction data to provide contractors with cheaper premiums. Wint, an Israeli startup that uses sensors and proprietary algorithms to eliminate water damage, which leads to nearly a third of damage claims on construction sites, has been used on about 2,500 projects. A study by Munich Re found that Wint can cut the loss rate by 90 percent.
“Insurance costs can be the difference between whether or not projects can be sustainably funded,” said Justin Levine, co-founder and CEO of Shepherd.
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