Angus Frost is a senior IT consultant at Burger Consulting Group, a construction technology company based in Chicago. The opinions are the author’s own.
Businesses have taken notice of artificial intelligence and the heavy civil construction industry is no exception. As someone who oversees this industry, I fully agree that the integration of AI in this sector is not just a good idea but really a necessity, driven by the need to improve efficiency, reduce costs and improve safety.
But in an area where mistakes can cost lives, how do you navigate the hype cycle to determine where AI innovation is mature enough to deliver value?
Angus Frost
Authorization granted by Burger Consulting Group
What sets heavy and civil contractors apart from other construction companies is an even greater reliance on labor, large fleets of equipment, manufacturing materials and trucks. All of these areas show enormous potential for improvement through AI, but progress has been uneven. It then takes a look at the maturity of various AI use cases in heavy civil construction and how these technologies are reshaping the industry.
Planning and design
One of the first applications of AI in heavy civil construction is in project planning and design. AI-augmented programming tools offer the ability to perform self-analysis and identify potential project risks faster than could be achieved before.
For example, a large GC working on the widening of 8 miles of highway on the East Coast was able to obtain more than $25 million in completion bonuses and savings in labor and materials through optimization scheduled
AI-powered design tools such as BIM systems leverage machine learning algorithms to create detailed 3D models of construction projects. These models facilitate better visualization, allowing stakeholders to identify potential problems before construction begins.
Conclusion: Although the use of BIM in heavy civil construction in the U.S. is still underutilized, its value has been proven in many countries outside the U.S. Adoption of this type of tool is slow but increasing, with vendors in the space that demonstrate an increasing level of maturity. .
Security management and risk assessment
Safety is a primary concern in the heavy civil construction industry. AI improves security management through real-time monitoring and predictive analytics. For example, AI-powered cameras and sensors can detect unsafe behavior or conditions on site and alert supervisors.
In fact, a large US-based heavy civil contractor recently reduced recorded incidents by 28% and reduced lost time by 35% using this technology.
Trench safety is of particular interest to OSHA and the National Utility Contractors Association. It is an area where the early warning of AI-assisted technology can have a significant impact. Similar to adaptive braking in cars, AI-powered sensors on heavy equipment can detect workers in the path of machinery and alert the operator to stop the equipment.
Additionally, with good cumulative inspection data, AI algorithms can analyze historical data to predict and mitigate potential risks. Factors such as weather, employee turnover, frequency of safety training, and a project backlog have an impact on potential safety incidents.
Conclusion: While the technology itself is advanced, widespread adoption is still growing. The main barriers include the high initial cost of implementation and the need for comprehensive training for the workforce. However, companies that have adopted AI for safety report fewer accidents and improve compliance with safety regulations.
Automated construction equipment
Automation in construction is advancing rapidly, with AI playing a crucial role in the development of autonomous equipment. Drones, robotic arms, and autonomous AI-controlled vehicles are being used for tasks such as site surveys, material transportation, optimized cutting/filling, and even construction tasks such as masonry and welding.
Florida-based Ajax Paving is using autonomous vehicles to address labor shortages. A single operator is able to manage several machines at different workplaces without wasting time driving between each one.
Machine control technologies that have existed for some time are certainly an early example of automation.
Conclusion: Automated teams are an emerging technology with significant potential. Current applications are mostly in pilot stages or used in controlled environments. Although greater adoption exists outside the US As AI technology advances and becomes more cost-effective, adoption is expected to increase.
Optimization of the supply chain
AI is transforming supply chain management in construction by optimizing logistics and inventory management. Machine learning algorithms can predict demand for materials, identify the most efficient supply routes and manage inventory levels in real time.
For example, AGIS, a large infrastructure and mining contractor in Brazil, uses supply chain automation to optimize the delivery of material to job sites.
This reduces delays and ensures that materials are available when needed. This use case is a good example of how important it is to have good data, as the advantage you get goes out the window if your data is bad.
Conclusion: AI-driven supply chain optimization is gaining traction, especially among large construction companies. Although the technology is mature, its implementation requires integration with existing enterprise resource planning systems and collaboration with suppliers.
Quality control and guarantee
Ensuring high quality construction is critical to the success of the project. AI aids quality control by analyzing images and data from construction sites to identify defects or deviations from the project plan and specifications. For example, computer vision systems can detect cracks in concrete or inconsistencies in structural elements. While AI-based solutions exist for vertical construction, the tools for use in heavy civil are still new. One example is Drexel University, which has made great strides pavement imaging technology for crack detection.
Conclusion: AI for quality control is relatively mature and is being adopted in several projects. The main challenge is the integration of AI systems with traditional quality assurance processes and the need for skilled personnel to interpret AI-generated data.
AI-driven decisions
The number of decisions a project team must make in any given day to keep their project on track, on budget, and safe is staggering. These decisions are made with or without good data or decision support.
AI-based decision support systems provide construction managers with information and recommendations based on real-time data analysis that may include external data sources.
These systems can predict project outcomes, suggest resource allocations, and identify potential delays or budget overruns. Industry associations are using generative AI to help provide their members with more detailed and specific information needed to support their activities through virtual assistants.
Conclusion: The availability and effectiveness of these varies greatly, depending heavily on the quality of the data entered and the ability of managers to understand the data presented, trust it and act on the recommendations generated by the AI.
Although the potential of AI in heavy civil construction is significant, several challenges hinder its full adoption. These include the high cost of AI technologies, the need for skilled personnel, data privacy concerns, and resistance to change within the industry. However, the continued evolution of AI in this sector suggests that many areas of AI use will extend beyond the hype cycle.
