Emission measurement of construction machines through IoT-based activity mapping

Purpose and goal:

The goal is to develop an IoT solution that considers construction machinery CO2 emissions in planning, procurement, and reporting through activity classification and CO2 estimation. The results indicate significant potential for activity classification using both existing machine data and more advanced sensor systems and analysis methods. The challenge is primarily in utilization, as there are currently no procedures for incorporating existing machine emission information into procurement or reporting.

Expected results and effects:

The project has shown that it is technically feasible to develop an IoT and AI-based activity sensor node that can classify construction vehicle activity and correlate it with relevant metrics for monitoring and maintenance. However, achieving the expected reduction in CO2 emissions requires more effort, as the follow-up organization needs to work on long-term changes in work practices, which are also linked to organizational culture.

Approach and implementation:

The project´s structure and execution have predominantly focused on technical possibilities, but it has become evident during the project that the major challenges are primarily related to organization rather than technology. In the construction industry, there is significant potential in activity classification, but it is suggested that climate requirements must originate from customers.

The project has explored eco-conscious procurement, which is a complex area requiring further exploration, particularly regarding culture and practices.