The construction sector is rapidly digitalising, however, many projects suffer from one problem – a lot of operational data is available, but it's hard to gain any insights from it.
Modern construction sites produce information via equipment, construction workers, materials, environmental sensors, security systems, scheduling of projects and quality control inspections. In the majority of cases, all the aforementioned systems work separately and therefore require switching among various applications for gathering information about what happens on site.
AIoT provides a new perspective on the issue.
By merging the Internet of Things (IoT) technology and Artificial Intelligence (AI), construction companies may obtain operational intelligence from disconnected data for making better-informed decisions at all stages of project implementation.
Here, in this article, we will look into AIoT in commercial construction, technologies involved, applications, as well as problems that need to be considered before implementation.
AIoT: The Definition
AIoT or Artificial Intelligence of Things is an interaction of Artificial Intelligence and Internet of Things devices.
IoT devices constantly receive data from the real world, while Artificial Intelligence analyses this data for detecting patterns and making predictions.
Instead of receiving sensor data, AIoT is aimed at answering questions, such as:
Where is the important equipment now?
What workspaces become crowded?
Do materials arrive according to the schedule?
What assets require maintenance soon?
Are there any new risks to health and safety?
Thus, the aim is to shift from collecting data to making decisions based on it.
Continuous Generation of Data on Construction Sites
A regular commercial construction site already consists of many connected systems.
For example:
RFID asset tracking system
Fleet management using GPS
Beacons based on Bluetooth
UWB positioning
Environmental sensors
Smart cameras
Safety wearables
Access control systems
All the technologies generate their own valuable data.
The difficult part is to integrate all the technologies into a single picture of the construction operations.
The Importance of Edge Computing
One myth is that all construction applications should upload their data directly into the cloud.
Actually, construction sites may have:
Poor connectivity
Network interruptions
Remote project locations
High bandwidth needs
Latency-sensitive operations
Edge computing helps to overcome these challenges by performing processing locally.
There is no need to upload all the data from sensors to a distant data center – local edge computing devices can analyse data instantly.
Advantages of edge computing include:
Decreased latency
Reduced bandwidth consumption
Increased robustness
Cost savings on cloud processing
Better support of critical operations
For use cases like equipment monitoring or area entry notifications, even a few milliseconds can make a difference.
AIoT Use Cases
Equipment Tracking
Construction equipment moves between zones during its working period.
AIoT platforms offer equipment tracking using GPS, RFID, BLE, or UWB technologies to increase efficiency and minimize idle time.
Workforce Tracking
Connected badges combined with workforce location technologies help site managers to know the whereabouts of employees and how resources are distributed without depending only on reports.
Workforce tracking helps to:
Properly allocate resources
Respond to emergencies
Count attendance
Analyse productivity
Keep in mind that privacy and regulations are crucial when tracking workforce.
Predictive Maintenance
Heavy machinery constantly produces operational data.
By using machine learning to analyse vibrations, temperatures, working hours, and performance trends, possible maintenance needs are identified before problems arise.
Not waiting for scheduled maintenance, condition-based maintenance is implemented.
Environmental Monitoring
The conditions at construction sites are quickly changing.
Using the AIoT approach allows monitoring such conditions as:
Air quality
Sound level
Temperature
Humidity
Dust concentration
Notifications help to address the necessary thresholds in time.
Material Management
Construction projects face delays due to material issues.
Having a connected inventory helps to keep track of materials, their deliveries, storing places, and usage, which helps to avoid material shortages and unnecessary purchases.
Implementation Challenges
Implementing AIoT solutions is not limited to the sensor implementation only.
For the successful implementation of a project, it is usually important to take into account several operational aspects.
Data Integration
Construction companies use different software platforms.
It is essential to integrate ERPs, project management tools, BIM software, asset management systems, and IoT infrastructure.
Cybersecurity
Each connected device increases the number of vulnerabilities.
It is necessary to consider security issues like:
Device authentication
Communication protection
Network segmentation
Software updates
Identity management
Data Quality
AI requires accurate data.
Inaccurate sensor calibration, lack of data, inconsistencies in naming conventions, or non-integration of the processes negatively impacts the quality of AI suggestions.
Accurate operational data is still among the most valuable project assets.
The Future of AIoT in Construction
There are several technologies that together will be creating a smarter environment for construction work.
They are:
Digital twins
Inspection drones autonomous operation
Computer vision
Edge AI
Predictive scheduling
Robotics
Operational assistance using Large Language Models (LLMs)
The combination of these technologies will help construction teams improve their ability to track project progress, plan resource allocation and deal with changes at site.
AI will not replace professional workers, but help them to make faster and better decisions.
Final Thoughts
Commercial construction becomes more connected every year.
But gathering more data alone will not lead to any improvements in project performance.
The true value is in turning operational data into insights that inform the decision-makers.
In AIoT, the ability to achieve this is brought about by integrating connected devices, edge computing, and intelligence analytics to create a cohesive operational environment.
For developers, system architects, and tech leaders, the question has shifted from whether construction sites will be connected but rather how they will design connected and intelligent environments that are secure, scalable, resilient, and actually useful to the managers of projects on the ground.
The following materials available on CommCon AI can help readers learn more about how such solutions are being deployed in commercial construction: [https://commconai.com/]
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