Steel reinforcement inspections in the construction sector are traditionally conducted manually, making them prone to human error and inconsistencies. These processes are time-consuming, expensive, and heavily reliant on the experience of individual inspectors. While laser scanning offers a more sophisticated alternative, it is costly, technically demanding, and primarily addresses quality (e.g. positioning and shape), rather than quantity. Additionally, the high learning curve associated with laser scanning tools poses a barrier for widespread adoption. The lack of automation in progress monitoring and steel evaluation has led to project delays, cost overruns, increased CO₂ emissions due to rework, and compromised structural safety, especially in high-rise construction.

SREM introduces a fully automated, image-driven solution to monitor and assess steel reinforcement in construction. This integrated system captures visual data to evaluate both the quality and quantity of rebar in structural elements. Requiring only images as input, the model removes the need for expensive equipment or specialised operators. It empowers contractors, consultants, and clients to perform progress monitoring remotely and in real-time. The solution reduces rework, mitigates construction risks, and accelerates timelines, all while lowering CO₂ emissions and ensuring compliance with engineering specifications. The flexibility of the algorithm also allows for customisation based on specific project needs or domains, such as pipeline inspections or dimensional checks.

At the heart of SREM lies its unique image-processing algorithm, which is the first of its kind designed specifically for the automated evaluation of steel reinforcement. The technology combines photogrammetry principles with IoT integration, enabling efficient data acquisition and processing with minimal input. By eliminating the need for laser scanning or extensive manual measurement, SREM streamlines a process that was previously slow and error-prone. Its scalable marker technique can be adapted to a wide array of applications, broadening its potential impact across multiple domains, including augmented reality and construction analytics. The innovation also supports remote access and monitoring, providing enhanced transparency and control across stakeholders.