Keywords: heavy equipment, residual value, machine learning, construction, regression
Astract: The residual value of heavy equipment is essential for financial and economic considerations in the construction industry. In practice, empirical methods are frequently used to determine the residual value of a given piece of equipment. Here, various regression methods are compared based on a real-world dataset of used heavy equipment sales from a construction company. The results show that the prediction performance of traditional methods is clearly worse when compared to machine learning models not yet employed for this purpose. For the latter, preprocessing and parameter tuning are essential, and the article guides through these steps. Further, the article demonstrates how a variable importance value comparable across all methods can be obtained. These findings may also be useful in other applications.

Keywords: Construction Management, Tunnelling, Process mining, Documentation, Data analysis
Astract: Conventional tunnel construction often relies on manual methods of construction process analysis, using tools such as paper-based cycle diagrams or spreadsheets, which lack immediate updates and capabilities, limiting performance evaluation, communication, and decision-making. As a result, moving to a fully digital process incorporating business intelligence capabilities can deliver benefits by improving data-driven decision-making, operational efficiency and resource allocation. This paper presents a case study using construction documentation to evaluate the applicability of data and process analytics in conventional tunnelling. We also present a novel approach to visualising and analysing construction sequence deviations. The study demonstrates how data and process analysis can be utilised to evaluate the activity sequences, the duration of single activities, advance rates, and general project performance. By adhering to established industry standards, this research examines the practical implementation of data analysis methods in operational tunnelling environments, contributing to the development of integrated digital workflows.

Keywords: Conventional tunneling, Drill and blast, BIM, IFC, Construction phase, Digitalization
Astract: Building Information Modeling (BIM) has become the key technology for the digital transformation of the architecture, engineering, and construction (AEC) sector. The Industry Foundation Classes (IFC), as an open and vendor-neutral data model, play an essential role in this transformation. Despite all the recent advances of BIM, tunneling needs to catch up with other AEC sectors adopting digital technologies. Currently, IFC is mainly used to capture information directly connected to the structural elements of the final tunnel but rarely as a means to document the tunnel excavation and support. This article proposes an IFC-driven process for the execution phase of conventional tunneling projects by extending the usage of IFC to information about the process, labor, equipment, and employed material. The proposed process is evaluated by a case study demonstrating how to represent data from a conventional tunneling project by IFC. The results show that IFC provides the necessary concepts to express the data of the execution phase of conventional tunneling projects. Implementing an IFC-driven process in this phase significantly contributes to the digital transformation of tunneling projects.

Keywords: BIM (Building Information Modeling), IFC (Industry Foundation Classes)

Keywords: BIM, Construction phase, Digitalization, Equipment, IFC, Labor, Material, Process, Tunneling
Astract: The documentation process of conventional tunneling projects is time-consuming and costly. Building Information Modeling (BIM) has enabled substantial productivity gains in the Architecture, Engineering, & Construction (AEC) sector. However, BIM has only been marginally adopted in the execution phase of conventional tunneling projects. For this purpose, we propose a BIM model that facilitates fully digital and automated data exchange between project stakeholders. We use the Industry Foundation Classes (IFC) as a basis and identify concepts potentially useful to represent data from the execution phase of construction projects. We demonstrate how IFC concepts are utilized to represent a shift report of a conventional tunneling project. Thereby, we deliver a reference model as an implementation guide for software developers in this domain. This may serve as a blueprint for handling construction management data in a machine-readable format, laying the foundations for Big Open BIM in the execution phase of construction projects.