Commercial construction involves a dynamic set of activities and contributes to a high volume of information flows. Artificial Intelligence of Things (AIoT) uses connected devices, sensors, and analytics to make sense of that information.
This guide focuses on practical recommendations for creating a system with AIoT capabilities that would support the work of subcontractors, equipment, materials, and other needs in commercial construction.
- A layered architecture for construction AIoT
Construction AIoT solutions in commercial construction entail a set of considerations. An architecture that addresses this domain can be organized into layers.
Device and sensing layer
The construction site has a variety of objects and activities that require tracking. Potential examples of this layer include
• RFID readers and tags,
• Bluetooth Low Energy (BLE) beacons and sensors,
• Ultra-Wideband (UWB) location sensors for higher accuracy,
• GPS for outdoor equipment,
• and other environmental and equipment sensors.
Depending on the application, the technology should be evaluated based on power, precision, and other requirements.
Connectivity and edge layer
Commercial construction sites are changing environments. The connectivity layer should consider potential variations in the layout and support the required devices. Possible options are
• Wi-Fi,
• cellular,
• LoRaWAN,
• and Bluetooth.
The edge layer can be used to filter and enrich data before it is sent to the data integration layer. This approach is particularly useful if the system operates in limited connectivity conditions. For example, a gateway could aggregate and validate sensor data, eliminating duplicates and adding timestamps.
Data integration layer
Tracking systems store and analyze data generated by different technologies and formats. When building an AIoT solution for commercial construction, this layer should be able to associate equipment events with objects in the register, project timelines, materials inventory, and work zone information.
The integration layer should standardize event formats, timestamps, and event types. It should also include rules for de-duplication, permissions, and quality checks. In addition, applications and systems used in commercial construction often utilize data from the construction site. This layer should provide normalization and enrichment rules for ERP, inventory, Building Information Modeling (BIM), and other systems.
Analytics and application layer
This layer provides the business with information and insights based on the data. At the construction site, the first applications to consider are those that provide visibility into the equipment, materials inventory, and project progress.
At the start, rule-based alerts tend to be more valuable than predictive models, particularly if there is insufficient data to train an AI model. Applications should be built with operational knowledge in mind to ensure that AI insights support – rather than replace – workers’ understanding of the worksite.
- Choosing the right tracking technology
The choice of technology depends on the application. An overview of potential technologies and considerations is provided below.
Technology Applicable scenarios
RFID Asset identification and checkpoints
BLE Proximity and location use cases
UWB Higher-accuracy location applications
GPS Outdoor equipment location
LoRaWAN Low-bandwidth sensor telemetry
There is no single best technology for commercial construction. In some cases, a combination of different solutions is preferable. Additionally, the solution should consider the accuracy, cost, and power requirements of the construction site. Before full-scale deployment, it is advisable to test and evaluate the proposed stack.
- Example workflow: equipment visibility
One common application for construction AIoT is equipment visibility. It typically involves the creation of an event-driven workflow. First, RFID or location checkpoints identify the asset. These sensors then relay information to the next layer, where the event is enriched with context and stored in the database. An application then uses this information to display the equipment’s location to the user. Rule-based alerts can be implemented at this stage to flag exceptions, such as when an asset enters a restricted area or exceeds an open period.
The system should clearly differentiate between confirmed, last known, or estimated location and events. Assets can disappear due to environmental conditions or connectivity issues. The application must make it clear that an event was not captured, as it could affect the interpretation of data.
This capability is important for construction site analytics because it establishes expectations about what the AIoT system delivers.
- Designing reliable event processing
An AIoT system is subject to fluctuations in connectivity. Devices can reconnect and send the same data, or events can be out of order. Some rules to consider include
• Idempotency: the ability to process the same event without generating duplicates,
• timestamps: the ability to track when an event actually happened and when it was received by the system,
• validation and schema enforcement: the ability to reject invalid events,
• observability: the ability to observe and debug the ingestion layer,
• retention policies: data storage and deletion rules, and
• access control: data security and privacy rules.
These rules ensure that an increasing number of devices and projects do not compromise the integrity of the system.
- Where AI can add value
AI can be applied in areas where there is a specific decision-making task. At the construction site, AI can identify patterns in the utilization of equipment, materials, or labor, estimate progress and costs, detect anomalies in operations, and prioritize exceptions for human review. However, before deployment, it is advisable to establish baseline expectations, select performance metrics, and test the model on actual data. Additionally, potential false positives and changing conditions should be taken into account.
In many cases, the ability to capture and analyze events provides more value than a fully developed AI model. Nevertheless, as the system accumulates sufficient data, an AI-driven approach can be considered.
- Security and privacy considerations
The construction site generates data that can contain personally identifiable information. A secure construction AIoT system should follow best practices in privacy and security, including
• device authentication,
• encrypted data in transit and at rest,
• least privilege data access,
• secure over-the-air firmware updates,
• network segmentation, and
• data retention policies.
In the case of workforce tracking, workers should understand what data is being collected, why it is being collected, and who has access to it. Security and privacy should be considered in the design and implementation of the system.
- A practical implementation roadmap
A practical roadmap for construction AIoT involves iterative development and testing. First, identify a specific use case and collect baseline metrics. Then, build a small-scale prototype, test it, and analyze its strengths and weaknesses.
The construction site has limited resources and should adopt a progressive enhancement approach. Following are the steps to consider when implementing AIoT in commercial construction.
Pick a specific use case and scope (e.g., equipment search time).
Establish a baseline (current state of equipment search time).
Set up a limited-scale prototype (a few sensors, rules, and processes).
Test and evaluate data quality (coverage, duplication, and location accuracy).
Establish a new baseline (post-prototype equipment search time).
Use the insights and feedback from the prototype to expand the system if it delivers demonstrable value.
For a demonstration of AIoT in commercial construction, refer to
"CommCon AI".
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