Smart Construction: How AI and Machine Learning Will Change the Construction Industry

Artificial Intelligence (AI) is when a computer mimics specific attributes of human cognitive function, while machine learning gives the computer the ability to learn from data, as opposed to being specifically programmed by a human. Here are ten ways that AI and machine learning will transform the construction and engineering industries into what we’ll call “smart construction.”

These days, seemingly everyone is applying Artificial Intelligence (AI) and machine learning. I have written about disruptions in the manufacturing industry, such as Industry 4.0, while illustrating the Hard Trends that indicate where improvements will be made in the future.

The construction industry, which makes up 7% of the global workforce, should already have applied these technologies to improve productivity and revolutionize the industry. However, it has actually progressed quite slowly.

Growth in the construction industry has only been 1% over a few decades while manufacturing is growing at a rate of 3.6%. With the total worker output in construction at a standstill, it is no surprise that the areas where machine learning and AI could improve such statistics were minimal. Yet, those technologies are finally starting to emerge in the industry.

Artificial Intelligence (AI) is when a computer mimics specific attributes of human cognitive function, while machine learning gives the computer the ability to learn from data, as opposed to being specifically programmed by a human. Here are ten ways that AI and machine learning will transform the construction and engineering industries into what we’ll call “smart construction.”

  1. Cost Overrun Prevention and Improvement

Even efficient construction teams are plagued by cost overruns on larger-scale projects. AI can utilize machine learning to better schedule realistic timelines from the start, learning from data such as project or contract type, and implement elements of real-time training in order to enhance skills and improve team leadership.

  1. Generative Design for Better Design

When a building is constructed, the sequence of architectural, engineering, mechanical, electrical, and plumbing tasks must be accounted for in order to prevent these specific teams from stepping out of sequence or clashing. Generative design is accomplished through a process called “building information modeling.” Construction companies can utilize generative design to plot out alternative designs and processes, preventing rework.

  1. Risk Mitigation

The construction process involves risk, including quality and safety risks. AI machine learning programs process large amounts of data, including the size of the project, to identify the size of each risk and help the project team pay closer attention to bigger risk factors.

  1. More Productive Project Planning

A recent startup utilized 3D scanning, AI and neural networks to scan a project site and determine the progress of specific sub-projects in order to prevent late and over-budget work. This approach allowed management to jump in and solve problems before they got out of control. Similarly, “reinforcement learning” (machine learning based on trial and error) can help to collate small issues and improve the preparation phase of project planning.

  1. More Productive Job Sites

Professionals often fear machines will replace them. While intelligent machines will take over first repetitive and eventually more cognitively complex positions, this does not mean a lack of jobs for people. Instead, workers will transition to new, more fulfilling and highly productive roles to save time and stay on budget, and AI will monitor human productivity on job sites to provide real-time guidance on improving each operation.

  1. Safety First

Manual labor not only has the potential to be taxing on the body, but also to be incredibly dangerous. Presently, a general contractor is developing an algorithm that analyzes safety hazards seen in imagery taken from a job site, making it possible to hold safety briefings to eliminate elevated danger and improve overall safety on construction sites.

  1. Addressing Job Shortages

AI and machine learning have the capacity to plot out accurate distribution of labor and machinery across different job sites, again preventing budget overruns. One evaluation might reveal where a construction site has adequate coverage while another reveals where it is short staffed, thereby allowing for an efficient and cost-effective repositioning of workers.

  1. Remote Construction

When structures can be partially assembled off-site and then completed on-site, construction goes faster. The concept of using advanced robots and AI to accomplish this remote assembly is new. Assembly line production of something like a wall can be completed while the human workforce focuses on the finish work.

  1. Construction Sites as Data Sources

The data gathered from construction sites and the digital lessons learned by AI and advanced machines are all tools for improving the productivity of the next project. In this way, each construction site can contribute to a virtual textbook of information helpful to the entire industry.

  1. The Finishing Touches

Structures are always settling and shifting slightly. It would be beneficial to be able to dive back into data collated by a computer to track in real time the changes and potential problems faced by a structure — and AI and machine learning make this possible.

Given the inevitable changes on the horizon, and the potential for costs to drop up to 20% or more with increased productivity, professionals in the construction industry must pay attention to Hard Trends, become more anticipatory, and ultimately learn to turn disruption and change into opportunity and advantage.

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The Industry 4.0 Advantage

This visceral image of “industry” being gritty and exclusively blue-collar is true to some degree, but when “4.0” is added to it, it takes on a whole new meaning, and blue-collar workers end up believing the narrative that robots and artificial intelligence (A.I.) will delete their jobs.

Though common, this fear is unwarranted. Despite the now-proven Hard Trend that A.I., advanced automation and robotics, 3D printing, and other industrial Internet of Things (IoT) advancements often replace mundane tasks in manufacturing, Industry 4.0 transformations allow us to work alongside machines in new, highly productive ways.

Industry 1.0 to 4.0

Manufacturing in every industry has evolved as four distinct industrial revolutions since the 1800s. The first industrial revolution took place between the late 1700s and early 1800s. Manufacturing evolved to optimized labor performed by the use of water- and steam-powered engines with human beings working alongside them.

The second industrial revolution began in the early part of the 20th century, introducing steel and use of electricity in factories. These developments enabled manufacturers to mobilize factory machinery and allowed for capitalizing on manpower in mass production concepts like the assembly line.

A third industrial revolution began in the late 1950s, which brought with it automation technology, computers, and robotics, increasing efficiency and repositioning the human workforce. Near the end of this period, manufacturers began experiencing a shift from legacy technology to an increase in attention to digital technology and automation software.

The current industrial revolution is Industry 4.0, which increases interconnectivity and networked intelligence through the Internet of Things (IoT) and other cyber-physical systems. Industry 4.0 is far more interlinked than revolutions before, allowing for improved company communication and collaboration.

The general definition of Industry 4.0 is the rise of digital industrial technology. To better understand, let’s take a look at nine building blocks of Industry 4.0.

Big Data and Analytics

Industry 4.0 allows for streamlining, collecting and comprehending data from many different sources, including networked sensors, production equipment, and customer-management systems, improving real-time decision making.

Autonomous Robots

The ability for robots to interact with one another while accomplishing rhetorical tasks increases productivity and opens new job opportunities for employees willing to learn new things. These future autonomous robots will cost less while having greater range of capabilities.

Advanced Simulation

Advanced simulations will be used more extensively in plant operations to leverage real-time data, mirroring the physical world in a virtual model. This includes machines, products, and humans and allows operators to test and optimize the machine settings in the virtual world first, accelerating a predict-and-prevent operational strategy for downtime issues.

Horizontal and Vertical System Integration

Universal data-integration networks in Industry 4.0 increase connectivity among departments, suppliers, and partners. This resolves lack of communication or miscommunication within a project crossing departmental boundaries.

Industrial Internet of Things (IIoT)

Decentralizing analytics and decision making while enabling real-time feedback is key in today’s age. IIoT means connected sensors, machines communicating with each other, and more devices having embedded computing enabling Edge Computing, where networked sensors get new data instantly and automated decisions happen faster.

Agile and Anticipatory Cybersecurity

Secure means of communication and identity management is quite important to cybersecurity in Industry 4.0, as increased interconnectivity brings the risk of security issues. Manufacturing companies must pre-solve problems in cybersecurity and implement anticipatory systems by adding a predict-and-prevent layer to A.I.

Advanced Hybrid Cloud and Virtualization

As data increases, local storage will not suffice, which brings us to Cloud Services and Virtualization. Elements of high-speed data analytics coupled with A.I. and machine learning enable real-time knowledge sharing. Advanced Cloud Services also enable anticipatory predict-and-prevent strategies.

Additive Manufacturing (3D Printing)

Advanced additive-manufacturing methods will be integrated into mass production systems, providing a new level of speed and customization along with the ability to solve complex manufacturing problems while also functioning as a standalone system for custom manufacturing.

Augmented Reality

According to my Hard Trend Methodology, this relatively new technology will gain more traction as augmented reality (A.R.) apps for business and industry are developed. For example, in Industry 4.0, AR can help quickly find parts in a warehouse by looking around from one location.

The adaptation of any of the new technologies in Industry 4.0 will face an uphill battle, as blue-collar manufacturing industries are not often open-minded about embracing new technology often seen as a job eliminator. Embracing the ever-changing spectrum of Industry 4.0 technologies allows acceleration of innovation, pre-solving seemingly impossible problems, and developing and implementing digital manufacturing solutions.
Leaders should help their managers and employees anticipate disruption and change to get excited about learning new skills that will keep them employed and ensure development in their careers. Start with my latest book The Anticipatory OrganizationI have a special offer for you!