How the s-X-AIPI toolkit optimizes the bituminous mix field value chain
In the framework of the s-X-AIPI project, the asphalt use-case has made a great deal of progress towards the development of specific solutions that will improve the bituminous mix field value chain. The approach that we have adopted in the project takes advantage of advanced analytics and AI models to optimize every stage of the production and construction processes while continually evaluating the models themselves.
Our approach: the data sources
AI-based models are fed by heterogeneous data provided by several sources, such as the Atalaya asphalt production plant's monitoring system, which employs ad-hoc smart sensing technologies tailored for the project. These sensors capture critical metrics in real time, allowing for immediate adjustments and ensuring optimal performance.
Vital data on the properties of the asphalt mix are derived from Quality Control (QC) laboratory tests. This information is crucial for understanding how different materials behave under various conditions, ultimately leading to better formulations that improve durability and performance. The integration of laboratory results with production data creates a robust feedback loop, allowing for continuous improvement in the asphalt mix design.
Paving process and its logistics are also key components of the data ecosystem. Data from paving equipment—such as temperature, speed, and weather conditions—are monitored to identify inefficiencies and optimize operations. By analyzing this data and comparing with the production ones, the AI models can provide actionable insights that help in streamlining the entire paving process, reducing the environmental impact.
Moreover, our approach supports predictive maintenance of the plants’ machinery by analyzing historical performance data. This proactive approach minimizes unexpected downtimes, resulting in higher productivity. By anticipating equipment failures before they occur, project managers can schedule maintenance during non-critical times, further reducing downtime.
New framework
In summary, the s-X-AIPI framework is setting a new standard for a traditional sector like the bituminous mix field by harnessing diverse data sources to create a comprehensive AI-based solution. This transformative approach is not only enhancing the efficiency and quality of asphalt production and paving processes but also delivering significant improvements throughout the entire value chain. As we continue to refine and expand this framework, the potential for innovation in the construction industry becomes increasingly promising.
Bottom Line
During these months, in s-X-AIPI we have been working hard manufacturing and deploying the smart sensors in the paving machineries, testing their functionalities in the specific dashboards designed for the project, fine-tuning and validating our solutions. The final validation results will become available soon.
