Discover Functionality of the Machine Learning Decision Support System
Time & Location
In the past few years, ASPARi plays a pivotal role in validating and implementing the PQi measurements through collaboration with contractors, resulting in significant progress in making the operational strategies explicit in road construction. However, while PQi have been widely applied as a baseline approach for assessing the variability occurring in the asphalt paving process, much less is known about how the assurance in the process quality can ultimately affect the quality of the product, i.e., the product quality of the as-built asphalt pavement. At present, the correlation between process and product quality of the asphalt pavement is still treated implicitly and intuitively. Given that the essential object of monitoring and assessing the process quality is to enhance the quality of asphalt pavement, it is thus crucial to obtain a thorough and explicit understanding of the missing linkage between the process quality and product quality.
Great efforts have been put in the gradual establishment of a data-driven environment in the practice of ASPARi over the past few years. An important gain during this process is that a wealth of data has been collected and stored from the data acquisition. Theoretically, these data contain the knowledge and actionable insights regarding what happened during the construction process. Furthermore, during or after the construction process, external organizations such as contractors and clients may own the data concerned with the product quality, by taking the in-situ measurements, laboratory tests, or regular inspections when the pavement is put into use. Potentially, by combining the PQi data regarding the process quality with data acquired and owned by external organizations about product quality, these data can be utilized to extract the hidden information from the complexity using the concept of machine learning (ML) after being pre-processed into a well-organized structure, thus enabling the decision makers to make better and fast decisions regarding construction strategies or preventive maintenance.
On these premises, one of the PDEng candidates from ASPARi group, Qinshuo Shen, is developing a decision support system using the aforementioned data-driven techniques, to couple the Process Quality during the asphalt construction process, with the Product Quality of the asphalt pavement. In order to gain insights regarding the input-output structure of the proposed model and elicit the needs from potential User Groups of the research outcome, thus gathering and defining the functionality of the research outcome, we are, therefore, pleased to invite you to attend a workshop on Friday 26th November 2021 at the University of Twente. The workshop will start at 10:30 and end at 15:00. Lunch will be provided. Should you not be able to attend physically, we are prepared to set up an online environment so that you may still participate.
Brainstorming session – Identifying the input/output structure of the “black box”