Learn how AI is changing the construction industry with advanced technology, making equipment smarter, safer, more efficient, and better for the environment.
Normally, when we think of construction equipment and heavy machinery, we picture humans manually operating these powerful machines. However, with the rise of AI, many construction vehicles are now automated to reduce risks, improve safety, and boost efficiency. Autonomous and remote-controlled vehicles are becoming more common in the construction industry.
For instance, Volvo recently developed the TA15, a fully autonomous dumper designed specifically for transporting heavy materials such as sand, gravel, and debris to and from construction sites. According to the US Department of Labor, the construction industry has the third-highest rate of fatal injuries. By integrating AI into construction, we can significantly reduce these fatalities and improve safety measures. In this article, we’ll explore how AI is enhancing construction equipment by making it smarter, safer, and more efficient while driving innovation across the construction industry.
Construction equipment and vehicles can encounter accidents due to unpredictable work environments and human error. However, AI-enabled systems can help management effectively address workplace hazards and reduce these errors. AI can also be used in construction equipment to optimize equipment operation, monitor machine performance, and automate maintenance schedules.
Here’s a closer look at the key technologies that enable these innovations:
Computer vision is changing how heavy machinery operates on construction sites, offering new and innovative solutions. Let’s walk through a few interesting applications that showcase the potential of image and video analytics in construction equipment.
A weighbridge is a scale used to measure the weight of heavy-duty vehicles. This is crucial in construction to make sure that vehicles comply with safety weight limits during transportation. Traditionally, this process relies on a booth operator to manually log details like vehicle entry and exit times, registration numbers, and load weights. However, this manual approach can be slow, prone to human error, and lack transparency.
Unmanned weighbridges can help improve accuracy, reduce human error, speed up the process, and provide real-time monitoring and transparency for safer and more efficient operations. They use AI-integrated devices such as sensors, cameras, LED screens, and automated voice guidance to streamline the entire process. As the truck approaches the entry point, cameras equipped with Automatic Number Plate Recognition (ANPR) technology detect the vehicle’s license plate and verify its registration. If the registration is valid, the system grants access to the weighing scale.
IoT-enabled weigh-in-motion sensors then measure the truck's weight as it moves and, if necessary, alert the driver to stop in the correct position for accurate weighing. The weight data is analyzed and compared against predefined limits, and if the truck is within those limits, the driver is directed to the exit gate. At the exit, the ANPR system re-verifies the vehicle to ensure it matches the one that entered, while computer vision-enabled cameras monitor the process for any irregularities. The system alerts supervisors and takes appropriate corrective actions in case of issues such as overweight loads or driver anomalies.
A survey from the U.S. Department of Transportation shows that trucks are the most common mode for transporting goods. Truck drivers often drive long distances, including overnight trips. This is also true in the construction industry, where trucks are essential for moving heavy machinery and materials between sites, sometimes across great distances. Driving overnight can lead to fatigue and an increased risk of crashes. Studies show that 21% of fatal crashes are caused by driver drowsiness.
To address this issue, truck manufacturers are using computer vision to monitor driver drowsiness. Computer vision techniques like facial recognition, pose estimation, and object detection can be used to monitor a driver’s eye movement, head position, and facial expressions. For example, if a driver’s eyelids close beyond a specified range, the system can detect it and sound the alarm to alert the driver. Driver drowsiness detection systems are widely used in Tata trucks and other automotive companies.
Working conditions on construction sites can be tough, especially in extreme temperatures. For example, at excavation sites, workers often face intense heat, which affects their ability to work efficiently and requires frequent breaks for hydration and rest. To help reduce downtime in these conditions, researchers are developing autonomous construction vehicles like bulldozers and cranes.
These autonomous machines are equipped with high-resolution cameras and computer vision technology that analyze the terrain and assess factors like slope, soft ground, and uneven areas. They use object detection to recognize people and equipment, enhancing safety by automatically stopping when an obstacle is detected. Researchers from Huazhong University of Science and Technology (HUST), in collaboration with Shantui, have recently developed an autonomous bulldozer that can operate in extreme temperatures as low as -10°C.
Fuel optimization is vital for construction companies, but implementing fuel efficiency practices can be challenging. With fluctuating fuel prices and multiple drivers operating construction vehicles, managing fuel consumption manually becomes complex. AI-driven fuel management systems can be used to enhance the process and reduce fuel consumption.
These AI fuel management systems are trained with large datasets to optimize fuel usage by generating multiple route options and recommending the most fuel-efficient route. Also, they can be integrated with the vehicle’s engine control unit (ECU) to provide real-time gear-shifting recommendations. By following these AI-driven recommendations, the driving patterns of different drivers can be optimized, resulting in improved fuel efficiency.
AI-integrated construction equipment offers a range of advantages, from data-driven decision-making to real-time monitoring. Here are some key benefits:
However, despite the growing adoption of AI in construction, there are still some challenges to consider:
The construction industry is rapidly embracing AI, with companies like Caterpillar and Daimler leading the way in developing self-driving trucks. In 2019, Daimler introduced a working prototype of their autonomous truck, which is expected to hit the market by 2027. Caterpillar’s autonomous haul truck, the 797F, is already making mining operations more efficient. Major companies like BHP Group, Rio Tinto, and Barrick Gold are using the 797F around the clock, reporting zero workplace injuries. Similarly, TuSimple, a Chinese autonomous trucking company, claims that its trucks are 11% more fuel-efficient than those driven manually. In June 2023, TuSimple successfully completed a 39-mile driverless run on an open public road in China.
As autonomous trucks continue to positively impact the construction industry, the market is expected to grow at a compound annual growth rate (CAGR) of 10%. With AI-driven construction equipment improving safety and fuel efficiency, companies are moving toward safer, more sustainable work environments.
AI is a game-changer in the construction industry and is making heavy machinery smarter, safer, and more efficient. From self-driving vehicles to AI systems that optimize fuel use and monitor construction sites in real-time, these technologies are helping reduce mistakes and save money. While there are challenges, like the cost of implementing AI and training workers, the benefits are substantial. With AI driving innovation, the future of construction is set to be more productive, sustainable, and innovative than ever before.
Curious about AI? Check out our GitHub repository, and join our community to connect with other tech enthusiasts. Learn more about AI applications in sectors like self-driving and manufacturing.
Commence ton voyage avec le futur de l'apprentissage automatique.
