Decentralized project delivery in architecture faces challenges related to transparency, accountability, and role definition. This paper explores the application of Soulbound Tokens (SBTs) as a governance and record-keeping mechanism within decentralized autonomous organizations (DAOs). Using a systematic review of practice, the paper identifies five opportunities for SBTs, proposes an operating framework for SBTs with respect to record-keeping (e.g., skills, contributions) and project governance (e.g, voting power, reputation), and describes one case of technical implementation of SBTs. Future research can improve on this technical implementation or develop additional decentralised applications for SBT skills verification and governance mechanisms.
The implementation of BIM in the AEC industry faces challenges due to misalignment between project needs and team capabilities. Current BIM maturity assessments focus on organizational qualifications rather than project-specific needs. This research addresses this gap through an ethnographic case study of two projects, emphasizing strategies for aligning project needs with BIM uses, assigning responsibilities, and assessing maturity. The first case demonstrates how collaborative models like IPD foster team support in implementing BIM. The second case illustrates how teams with lower organizational BIM maturity can still efficiently contribute by leveraging capabilities aligned with project-specific requirements.
Digital technologies play a crucial role in efficiency and integration across industries. BIM has become a key enabler of digital transformation. Despite the increasing use of BIM, traditional practices remain prevalent, and construction lags in digitalization compared to other sectors. This highlights the need for measuring digital maturity. This paper reviews the outcomes of a digital maturity measurement tool applied to a selection of 256 European construction projects across nine offices using Exploratory Data Analysis. It further provides an analysis of the indicators used to calculate the digital maturity score and examines how the results vary across different European countries.
Healthcare facilities require strict safety and efficiency standards, yet secondary building components are often overlooked during handover. Existing methods like laser scanning and redlining are time-consuming and inadequate for inspecting secondary components. This article explores using Augmented Reality (AR) in inspecting secondary components in healthcare construction projects. A case study in a UK project is presented, where secondary components in patient rooms are inspected. The inspection identified 25 deviations from design, highlighting the effectiveness of AR in enhancing inspection accuracy and efficiency. The study recommends integrating AR into existing inspection workflows and further research on scalability, cost-benefit, and standardized protocols.
An AI-driven Digital Twin (DT) framework was developed to integrate real-time sensor data, facility management systems, and AI-driven predictive analytics to enhance building operations. Implemented in a three-story academic building, the DT serves as a dynamic common data environment (CDE) that supports real-time monitoring, asset management, and predictive maintenance. By integrating IoT data streams with an LSTM-based machine learning (ML) module, the framework provides actionable insights for proactive decision-making, helping to streamline facility management (FM) workflows and enhance collaboration among stakeholders. Future work will broaden the scope of ML techniques, expand use cases, and promote wider adoption across similar facilities.
This paper presents a novel framework designed to decentralise data management and enhance collaboration and interoperability in the Architecture, Engineering, and Construction (AEC) sector. By integrating BIM with a representation of building elements as independent actors in an actor-based system, it supports scalable and parallel workflows, allowing to concurrently access and modify distinct parts of the model. Semantic Web Technologies ensure that the data is linked and structured. The framework was tested in a controlled environment, demonstrating its ability to manage element-level actors, propagate changes asynchronously, and maintain data consistency. However, the complexity of maintaining decentralised components remains a challenge.
Semantic Web Technologies enable the integration of heterogeneous building information and the verification of this integrated information against digital regulations. However, integrating geometric information and thus performing geometric compliance checks remains challenging. This paper presents a method for integrating geometric functionalities from IfcOpenShell with the semantic capabilities of Linked Building Data. It presents a Python library that enables performing geometric computations and stores the results of those computations in knowledge graphs. This novel functionality enables performing geometric compliance checks on Linked Building Data. Future work will extend this library with more functions, a user interface, and more robust testing and validation.
To address challenges related to Workplace Management practices, this study leverages System Engineering and Digital Twin Environments principles, proposing a Workplace Asset Management System (WAMS). The methodology spans stakeholder requirement analysis, use case formalization and system architecture design, aiming to validate the structure of a unified environment for multi-domain data integration and stakeholder collaboration, focused on optimizing workspace allocation via data-driven insights. Through the adoption of Semantic Web technologies, the proposed system aims to foster digitized and scalable holistic approaches in Asset Management. While acknowledging limitations, a pilot is being implemented, to demonstrate benefits at organizational and technical level.
This paper proposes the Ray-Based Laser Scanning and Intersection Algorithm (RBLSIA) to generate synthetic point clouds for Mechanical, Electrical, and Plumbing (MEP) systems using BIM models, addressing the lack of MEP datasets for deep learning-based semantic segmentation. Twenty comparative experiments were conducted to assess the performance across different training datasets, synthetic point cloud generation methods.The results show that RBLSIA-generated synthetic point clouds outperform those from uniform sampling by 3.32% in mean Intersection over Union (mIoU). Additionally, increasing the volume of synthetic samples improves overall accuracy (OA) and mIoU, surpassing the performance of models trained with real point clouds.
