ECPPM 2022 - eWork and eBusiness in Architecture, Engineering and Construction contains the papers presented at the 14th European Conference on Product & Process Modelling (ECPPM 2022, Trondheim, Norway, 14-16 September 2022), and builds on a long-standing history of excellence in product and process modelling in the construction industry, which is currently known as Building Information Modelling (BIM). The following topics and applications are given special attention: Sustainable and Circular Driven Digitalisation: Data Driven Design and/or Decision Support Assessment and Documentation of Sustainability Information lifecycle Data Management: Collection, Processing and Presentation of Environmental Product Documentation (EPD) and Product Data Templates (PDT) Digital Enabled Collaboration: Integrated and Multi-Disciplinary Processes Virtual Design and Construction (VDC): Production Metrics, Integrated Concurrent Engineering, Lean Construction and Information Integration Automation of Processes: Automation of Design and Engineering Processes, Parametric Modelling and Robotic Process Automation Expert Systems: BIM based model and compliance checking Enabling Technologies: Machine Learning, Big Data, Artificial and Augmented Intelligence, Digital Twins, Semantic Technology Sensors and IoT Production with Autonomous Machinery, Robotics and Combinations of Existing and New Technical Solutions Frameworks for Implementation: International Information Management Series (ISO 19650), and Other International Standards (ISO), European (CEN) and National Standards, Digital Platforms and Ecosystems Human Factors in Digital Application: Digital Innovation, Economy of Digitalisation, Client, Organisational, Team and/or Individual Perspectives Over the past 25 years, the biennial ECPPM conference proceedings series has provided researchers and practitioners with a unique platform to present and discuss the latest developments regarding emerging BIM technologies and complementary issues for their adoption in the AEC/FM industry.

Multi-Models address issues of nD modeling problems in construction information processes. They provide a temporary and loose coupling by aggregating instances of possibly orthogonal domain models and expressing explicit relations between their elements. To provide a base for prototypical and specific multi-model support as well as the development of generic multi-model based tools, the authors propose a data schema for a generic multi-model, consisting of an ID-based link model and two elementary model representations. Specifications of optional meta-data provide structural and semantic description of the data.

The increasing complexity of data management in the Architecture, Engineering, and Construction (AEC) industry, driven by the adoption of distributed digital twins (dDTws) and cloud-based solutions, presents challenges in interoperability, data sovereignty, and scalability. Existing Building Information Modeling (BIM) and Common Data Environment (CDE) frameworks often fall short in addressing these issues due to their reliance on centralized and proprietary systems. This paper introduces a novel framework that transforms the Information Container for Linked Document Delivery (ICDD) into a dynamic, graph-based architecture. Unlike conventional file-based ICDD implementations, this approach enables fine-grained, semantically rich linking and querying across distributed models while maintaining data sovereignty and version control. The framework is designed to enhance real-time collaboration, ensure secure and sovereign data management, and improve interoperability across diverse project stakeholders. The framework leverages graph databases, semantic web technologies, and ISO standards such as ISO 21597 to facilitate seamless data exchange, automated linking, and advanced version control. Key functionalities include federated data storage, compliance with local and international regulations, and support for multidisciplinary workflows in large-scale AEC projects. To demonstrate the feasibility of the proposed framework, a simplified use case scenario is implemented and analyzed. By addressing critical challenges and enabling seamless integration of emerging technologies such as digital twins, this study advances the state of the art in data management for the AEC industry, providing a robust foundation for future innovations.

The recent advance of Building Information Modelling (BIM) and the related needs for BIM-based interoperability of more and more specialized AEC tools in various construction subdomains showed that: (1) a global model for all data in a construction project is neither a realistic nor practical target, and; (2) BIM data typically have to be combined with other kinds of construction-related data to be efficiently applied in real tasks. This paper addresses the issue of BIM extension for such multi-model domain tasks on the basis of work done in the frames of the EU project HESMOS for the development of an energy-enhanced BIM framework (eeBIM) enabling the integration of multiple needed resources (i.e., climate, occupancy, and material data) and the interoperability of various energy analysis and monitoring tools in an Integrated Virtual Energy Lab Platform (IVEL). It explores the pros and cons of three principal approaches - (1) extending the BIM Schema; (2) extending the BIM data; and (3) using a Link Model - before focusing, in particula,r on the latter approach which provides for greatest generality, modularity and application scope as it does not require changes in the BIM schema and guarantees maintenance of each model within its own domain. The generation of the eeBIM model from initial architectural CAD-BIM data and future research plans completes the presentation.

This article proposes a novel approach to segment instances of bulk material heaps in aerial data using deep learning-based computer vision and transfer learning to automate material inventory procedures in the construction-, mining-, and material-handling industry. The proposed method uses information about color, texture, and surface topography as input features for a supervised computer vision algorithm. The approach neither relies on hand-crafted assumptions on the general shape of heaps, nor does it solely rely on surface material type recognition. Therefore, the method is able to (1) segment heaps with “atypical” shapes, (2) segment heaps that stand on a surface made of the same material as the heap itself, (3) segment individual heaps of the same material type that border each other, and (4) differentiate between artificial heaps and other objects of similar shape like natural hills. To utilize well-established segmentation algorithms for raster-grid-based data structures, this study proposes a pre-processing step to remove all overhanging occlusions from a 3D surface scan and convert it into a 2.5D raster format. Preliminary results demonstrate the general feasibility of the approach. The average F1 score computed on the test set was 0.70 regarding object detection and 0.90 regarding the pixelwise segmentation.