## 5 Conclusions  
This paper presents a framework that uses process mining techniques to utilize the event data collected from the earthmoving site and discovers the process model. The process model was used to develop a DES model of the earthmoving operations. Furthermore, the Bayesian updating technique was used to update the input parameters of the DES model for an up-to-date and accurate productivity estimation based on field data. A real-world earthmoving operation was chosen as a case study. Field data were collected using IMU attached to the excavators and dump trucks. Previous works explained how deep learning techniques network was used to predict the activities of the equipment. Then the activity profile for all the resources was generated which recorded the events that happened on the site with their corresponding times. This was used as an event log, and the \(\alpha\)-algorithm was used to discover the process model of the operation. A DES model was developed based on the process model. Cycle times calculated from the predicted activities were used as inputs in the DES simulation model. Bayesian techniques were used to update the distribution of the input parameters of the simulation model. This demonstrates a formal approach to combining original estimates with the sample data collected from the site. The updated simulation prediction shows an increase in cycle times compared to the initial predictions as the project progresses. This shows reduced productivity, and the project duration was predicted to extend by 2 days compared to the initial prediction. These results can be used as a guideline to control the project cost and schedule.  
Figure 16: Cycle times and productivities using initial and updated cycle times.  
This research contributes to the body of knowledge by demonstrating the capability of process mining and the DES model to accurately mimic the real-life operation of construction sites. The presented framework can facilitate the decision-making process by simulating the most up-to-date field conditions. Moreover, the study contributes to the practice by presenting an integrated framework that contains an activity identification model for heavy civil equipment, process mining, and a DES model simulating earthmoving operations. This provides a platform to automatically monitor the activities as well as estimate productivity by utilizing the most recent information.  
In this paper, process mining was used only for discovering the process model. Future studies will investigate other capabilities of process mining such as deviation detection, and bottleneck identification for construction operations. Moreover, the case study was performed only using data from the excavator and dump trucks. The future study can expand this work by including other earthmoving equipment (e.g., loader, scraper, grader, etc.) to capture the complex dynamics of typical heavy civil operations.  
Conceptualization, K.M.R. and J.L.; methodology, K.M.R. and J.L.; software, [PERSON] and J.L.; validation, K.M.R.; formal analysis, K.M.R. and J.L.; investigation, K.M.R. and J.L.; resources, J.L.; data curation, K.M.R.; writing--original draft preparation, K.M.R.; writing--review and editing, J.L.; visualization, K.M.R.; supervision, J.L.; project administration, J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality agreement with construction project owners from where data was collected.  
The authors declare no conflict of interest.