## 1 Introduction  
In Germany, the introduction of digitization in the manufacturing industry called Industry 4.0 initiated the fourth industrial revolution [1]. Industry 4.0 includes the implementation of sensors and embedded systems on equipment to make equipment \"smart\". This digitization, also known as the Internet of Things (IoT), helps to interconnect decentralized systems to form a \"system of systems\" (aka. a Cyber Physical System (CPS)) and to link the real world to its digital representation (aka. a Digital Twin (DT)) [2].  
Like the manufacturing industry, the construction industry can benefit from the application of Industry 4.0 and its technologies, which is known as Construction 4.0 [3, 4, 5]. DTs in Construction (DTC) are often equated with Building Information Modeling (BIM) but missing feedback loops to the construction site [6]. In heavy civil engineering, research is being conducted on DTCs using construction equipment for updating and optimizing construction operations. Besides supporting data transformation, these DTCs use two key technologies [7]: (1) data-driven discrete-event simulation (DES) to digitally represent construction equipment operations and (2) artificial intelligence (AI) to analyze the DES input data coming from the equipment.  
Different modeling approaches have shown how supervised machine learning (ML) can help to analyze this input equipment data. An objective has been to automatically recognize activities for equipment such as front-end loaders [8; 9], hydraulic excavators [7; 9; 10; 11; 12], compactors [12], and drill rigs [13; 14]. Researchers have used different methods to collect data, such as vision- or motion-based methods, and to analyze them, e.g., using logistic regression, k-nearest neighbor, decision trees, support vector machines, or artificial neural networks (ANN). As part of deep learning (DL), ANN fulfills the requirements for minimal pre-processing as well as robustness to outliers and sensor noise and recognizes many activities [9; 12; 14].  
In all cases, classifying activities has been challenging, because no level of detail or granularity is a priori defined. Literature is lacking in studies that focus on activity modeling. The existing studies on activity recognition have aimed to improve DL techniques instead of improving data acquisition. This paper aims to address activity modeling using a real-world use case as an example to highlight the current challenges and then provide a solution in the form of a productivity model. Our research questions (RQ) are as follows:  
_RQ1: What impact does production model granularity have on activity recognition?_  
_RQ2: How does production model granularity affect the application of DTC?_  
_RQ3: What is needed to adopt production models for DTCs in heavy civil engineering?_  
To answer these questions, we start by giving an overview of the current DTC approaches including the modeling part of data-driven DES and activity recognition. Our overview's focus is on equipment-intensive construction operations; however, we recognized that there is much research into recognizing human activities [15]. Then, we present a case study in special foundation engineering, namely, the machine production of bored piles. This study underlines the challenges arising with activity recognition as well as the possibilities and limitations of data-driven DES in heavy civil engineering. It discusses the impact of process descriptions based on real-time data. As a basis for the adoption of DTC applications, a flow-based production model is proposed.