Construction teams are employing AI software to reduce waste and improve operational efficiency with features such as automated Materials Take Off (MTO), real-time waste reduction monitoring and reporting, predictive equipment maintenance, and potential safety risk forecasts.
When used strategically, AI can reduce project errors such as overordering or overproduction, defects, poor inventory management practices, and less obvious wastes like materials awaiting workers which lead to construction waste.
Why Is Reducing Construction Waste Important?
Managing construction waste is a core responsibility of construction managers, workers, and contractors. Failure to reduce construction waste could lead to increased project costs which are passed down to the project owner. Additionally, it can cause delays in project timelines and increase environmental and climate footprints.
It should be added that reducing all forms of construction waste proactively lets you complete projects within time and budget while complying with regulatory standards.
10 Ways AI Can Reduce Construction Waste
Here are some ways you can reduce construction waste using AI.
1. Project Forecasting and Design Simulations
AI-enhanced Building Information Modeling (BIM) lets you create multiple versions of a design project based on predefined parameters like structural integrity and building materials. Similarly, BIM can predict and anticipate possible challenges with your building plans. This broadens the scope of options stakeholders can choose from, letting them visualize the project and reduce the waste caused by avoidable rework.
2. AI-Optimized Project Scheduling
Considering factors like resource availability, potential risks, and weather, AI can simulate multiple project scheduling possibilities, maximum and minimum crew sizes for different tasks and scenarios allowing construction teams to choose plans that ensure efficient scheduling and resource allocation.
Using this, teams get visibility into the impact of task schedules and sequences and spot potential scheduling errors or overlaps. Scheduling errors can lead to:
Equipment lying idle on site for months, wasting the warranty period or needing renewed certification when it’s ready to use thus wasting time.
Increasing risks of theft and damage of construction materials left unused.
With AI, you can execute just-in-time delivery of materials, ensuring vendors deliver materials when you need them, which avoids lengthy storage and possible material waste.
3. Real Time Monitoring and Reporting
AI tools can monitor construction waste generation and recycling in real time. This helps your construction teams to measure the efficiency of waste management strategies objectively and modify the efforts to reduce construction waste.
Using video recordings and images, AI can identify types of waste materials like wood and plastic at waste collection points and estimate the waste’s accumulated weight. With historical data, you can use AI to predict waste collection trends of future projects and improve waste reduction strategies.
AI can also improve waste collection schedules and volume tracking, helping you to efficiently manage waste and avoid unnecessary waste collection trips.
4. Quality Assurance
Some AI tools detect deviations from design specifications in real time using AI-powered cameras and building sensors. This helps construction managers and other stakeholders ensure compliance and minimize defects promptly, thereby avoiding sanctions and stopping wasteful reworks. This, in turn, allows for the optimal use of materials and reduces the need for premature renovations, demolitions, or new building projects.
5. Predicts Equipment Maintenance
Using sensors installed in construction machinery, AI can monitor equipment performance, anticipate potential equipment failure, and ensure prompt maintenance. Some machine-learning predictive maintenance models can anticipate 22% of equipment failures with a low false positive failure rate of 2.5% about 10 days before they occur.
Preventive measures powered by predictive maintenance reduce equipment malfunction leading to waste and unforeseen downtime.
6. Improves Safety
On-site accidents can lead to unplanned delays, unpredictable schedules, lost time, and possible damage to ordered inventory. AI can use data from cameras, sensors, and wearables to observe environmental conditions and personnel behavior to spot and forecast potential safety risks.
It can identify workers entering hazardous zones without wearing safety gear and flag wrong equipment usage preventing accidents and possible damage. This can significantly lower the risks of mishaps which can lead to loss of lives, equipment and material damage, and unanticipated project delays.
7. Improved Supply Chain Management
AI can forecast material demand by predicting the types and quantities of materials needed at different phases of construction projects. It can also monitor stock quantities and usage rates prompting teams to reorder or automate orders for materials. This reduces human error and material damage or deterioration.
8. Material Take Off (MTO) Accuracy and Improved Inventory Management
Instead of manually estimating material quantities or drawing polygons to create estimates with estimation software, AI can automate material take-off (MTO) with precision and speed using data from blueprints and architectural designs. Taking the manual approach often leads to overproduction or overordering causing more waste.
Alternatively, using AI estimates lets you predict required materials, accurately measure inventory, and ensure maximum efficiency within the projects’ budget. Another upside to AI is that it can predict possible fluctuations in material and labor costs based on historical data and current market trends.
9. Source Reduction: Preventing Construction Waste
AI can help your team reduce construction waste by preventing waste generation from the source. Source reduction is the best form of waste reduction effort possible. It involves ensuring that the amount and toxicity of waste is minimized in construction design and material production, purchase, or usage.
For example, AI can detect defects in the design of a building and potential risks thereby improving the durability of the building and reducing the need for rework. This lowers the need for new buildings as the useful life of existing structures is extended. It also helps to reduce the volume and mass of waste generated by accurately predicting the quantity of materials needed to complete a building project.
10. Automated Waste Sorting
AI can sort waste making them recyclable and minimize diversions to landfills. This matters as construction waste significantly contributes to landfill waste.
AI-powered cameras identify and categorize construction waste like concrete, wood, and plastic with greater speed and accuracy. This helps stakeholders reduce recycling stream contamination, glean data on the quality and quantity of recycled materials, and improve efforts towards a circular economy.
Reduce Construction Waste in Your Project With AI
While technology plays a huge role in reducing construction waste, getting the best result goes beyond just using tools and tactics.
Managers, construction workers, and stakeholders need to be more conscious of the impact of their projects on the environment. One way to do this is to adopt waste management and LEAN construction principles at a philosophical level. These principles help construction managers set waste reduction goals, learn continuously to refine their strategies, and ensure collaboration to reduce errors.
When managers and stakeholders shift their mindset about construction waste, they can efficiently leverage AI to minimize waste.
Using an ERP system like CMiC lets you and your team adopt AI to reduce wastage in construction projects. CMiC is an ERP software designed to help construction teams like yours improve collaboration, operational efficiency, and optimized workflows.
Sources:
The industry creating a third of the world's waste
Generation of waste by the construction industry in the European Union (EU-27) in 2020, by country
Predict auto failures in advance using connected vehicle data in real time