## 2 Related Work  
### DTC and Data-Driven DES Modeling  
DTs are platforms that offer promising opportunities for monitoring and prediction in the construction industry [2]. While no universally agreed-upon definition exists of what a DT is [6], in general, it is about the data flow between the real and virtual worlds [16].  
To digitally represent construction equipment operations, data-driven DES is widely reported in the literature, e.g., for tunneling [17], earthmoving [7], or pile drilling [18]. DES models simplify construction systems by discretizing activities so that they are bounded by start and end events. This enables investigations of the system's behavior under different parameters. To consider all the dynamics and uncertainties, a DES needs to be updated with as-built data to support reliable decisions based on the results. [PERSON] and [PERSON] [19] identified three pieces of knowledge that can be extracted from the equipment: (1) state, (2) operational logic, and (3) layout arrangement. The dynamic updating of DES requires the pre-processing of data from the construction site, for which different statistical probability distribution functions may be used (e.g., Exponential, Gamma, Lognormal, Normal, and Weibull) and evaluated by goodness-of-fit tests (chi-Square test; Kolmogorov-Smirnov; Anderson-Darling) [20].  
Finding a suitable (fit for purpose) level of modeling detail is challenging. For example, [PERSON] et al. [21] presented a data-driven Monte Carlo simulation framework to predict equipment life-cycle costs. They used K-means clustering and expectation-maximization to distinguish between the equipment within a fleet. [PERSON] and [PERSON] [22] replicated and modeled the discovered process as a Petri net in the DES model software 'Strobe (a STROBOSCOPE [23] development). [PERSON] et al. [24] also presented an earthmoving analysis but with the help of image recognition and using a process modeled in the DES software WebCYLONE (a CYCLONE [25; 26] development) to optimize resource allocation. [PERSON] et al. [20] used the intralogistics DES software plant simulation [27] to model a Petri net. They considered three stages of construction progress (25%, 50%, and 75% complete) to show the impact of data-driven modeling. Besides Petri nets, [PERSON] et al. conducted data-driven DES on agent-based modeling (macro-simulation) to schedule project interdependencies [28] and process modeling (micro-simulation) to investigate the influence of material flow [29]. They introduced a DTC using both DES models for adaptive planning to serve as a decision-making tool (Figure 1).  
[PERSON] and [PERSON] [7] conducted a study to show data integration into a DES model. Based on the activities recognized in their previous work [9], they calculated the activities' duration, pre-processed it into distribution functions, and fit it using the chi-square goodness-of-fit test.  
Several studies show the importance of data-driven DES. A variety of different modeling levels have been considered depending on the modeling purpose. In combination with data processing, frameworks exist to connect the DES model with the real equipment as needed to create a true DTC.