## 1 Introduction and Background  
Construction processes are characterized by complex interactions among highly interdependent components that are subject to complex activity startup conditions. Discrete-event simulation (DES) tools can express the logic of these complex interactions effectively [1]. Even though DES has been widely researched and practiced over the past couple of decades, the development of simulation models is still expensive and time-consuming. DES has been used to analyze construction processes by simulating various \"what if\" scenarios and redesigning alternatives regarding key performance indicators. Despite this capability, the real-life applications of DES are limited due to two major reasons: (1) it is time-consuming to build a reliable simulation model of construction operations; and (2) it is difficult to represent the complexity of the real-world operations in a simulation model. Moreover, as simulation models are typically static, they do not adapt to the changing conditions of the real world when the initial assumptions and inputs of the model are no longer valid. Typically, the inputs for simulation modeling are statistical data from past projectsand the subjective experiences of decision-makers. However, as the project progresses, an additional layer of uncertainties in the form of changing site conditions, equipment breakdowns, weather delays, etc. are introduced into the project which makes the initial models unreliable for continued decision-making. This lack of complete information at the start of operations can lead to non-optimal decision-making relating to an operational variable such as fleet-sizing and resource allocation, resulting in lost productivity and time and cost overruns. One method of overcoming this inherent limitation of a-priori estimation is to constantly update the simulation model in real-time using data that is available from real-world operations. Process mining is a recently developed method wherein operational processes are studied and analyzed based on the event data that is generated during their course [2]. Process mining techniques can be used to discover the underlying behavior of any operation that can be described by events. Moreover, replaying event data on the process model can enable compliance checking and performance analysis of the process. Even though process mining can be used to diagnose bottlenecks and/or non-compliance issues and predict the future direction taken by process instances, it cannot be used to answer \"what-if\" scenarios and explore comparative redesigns.  
Given the capabilities of these two separate technologies of DES and process mining, their integration can create a whole that overcomes their limitations. Figure 1 demonstrates the classical use of simulation modeling and analysis wherein a simulation model is created from a textual description of the process. The model can be further used to generate event logs recording the simulated behavior of the process.  
Figure 2 shows how can process mining and simulation modeling can be integrated to diagnose the performance of the process. In this case, event data generated by some process can be used to discover the descriptive process model. Further, the event logs can be replayed over the model to analyze bottlenecks and add the temporal and stochastic behavior of the model.  
Figure 1: The classical use of simulation.  
Figure 2: Integrating process mining and simulation for performance analysis.  
This paper presents a framework that generates DES models from event logs using process mining techniques and then updates the DES model with real-time data captured from the operation using Bayesian updating. Thus, the productivity estimation of the construction operation can be measured dynamically which will enable stakeholders to make timely, data-driven decisions regarding their operations.