The construction sector faces persistent challenges in scaling emerging technologies such as 3D Concrete Printing (3DCP), despite their potential to reduce material waste and accelerate build times. This paper addresses a key barrier to adoption: economic uncertainty in the development and deployment of 3DCP production systems. Drawing on a case study of a commercial 3DCP facility, we develop a three-stage stochastic decision-support framework to guide scaling efforts. The first stage quantifies cost uncertainties in hardware, software, and material systems. The second stage evaluates strategic development pathways under multiple future scenarios. The third stage integrates investment costs to support full cost-benefit assessments. Anchored in the Resource-Based View (RBV), our approach identifies how firms can mobilize technological, financial, and human resources in uncertain environments. Methodologically, we combine Monte Carlo simulations with Simulation Decomposition (SimDec) to enable multivariate cost-benefit analysis. The result is a practical toolkit for managers navigating early-stage innovation in construction production. This research contributes to scholarship on technology adoption, strategic investment under uncertainty, and sustainability transitions in construction.

Technical building equipment (TBE) systems offer significant potential for energy savings that can be realized through digital twins and simulations. Digital twins require a precise virtual representation of the real object. Geometric-semantic models originating from Building Information Modeling (BIM)-based planning can be a valuable basis for this. However, due to unavoidable deviations between planning and construction, a high-quality comparison of as-planned and as-built data is crucial. This study presents a geometric and statistical analysis framework for component-specific validation of TBE systems using as-planned BIM models and as-built point clouds. The framework was evaluated on datasets of increasing complexity, from simulations to real-world projects. Results show that with sufficient data quality, the framework enables the validation of up to 88% of components, significantly reducing manual effort, cost, and time. However, as a geometry-based approach, its performance is affected by data quality issues such as point cloud noise and occlusions.

Augmented Reality Head-mounted Displays (AR-HMD) hold great promise in improving construction workers’ performance and safety through immersive training. However, their adoption in high-risk construction environments remains limited due to insufficient understanding of user acceptance. This study advances the measurement of technology adoption by integrating an extended Technology Acceptance Model (TAM) with Electroencephalogram (EEG) signals, creating a dual assessment framework that combines survey responses with objective neurophysiological indicators. The extended TAM incorporates motivational and experiential factors, while EEG captures mental workload and engagement to support the model’s constructs. Both Partial Lease Square Structural Equation Modeling (PLS-SEM) and Bayesian Structural Equation Modeling (Bayesian SEM) confirmed perceived usefulness is a central predictor of user acceptance, with enjoyment, motivational support, and perceived system quality emerging as key drivers. EEG-derived measures, validated via correlation and multiple regressions, provided converging evidence that higher motivation and stronger adoption intentions were associated with reduced cognitive workload, and that EEG ratios independently predict perceptions of usefulness, ease of use, and enjoyment. These results highlight the value of combining subjective and objective measures to inform the design of cognitively supportive, user-centered AR training systems in safety-critical construction scenarios.

Advancements in physiological monitoring systems (PMSs) and wearable sensing devices (WSDs) have enabled real-time, objective assessments of workers’ mental status in construction. However, existing studies lack a comprehensive synthesis of mental status monitoring and classification approaches used in construction, including data collection, preprocessing, as well as postprocessing techniques. This paper systematically and bibliometrically reviews 223 studies following PRISMA guidelines, providing a structured framework for PMS and WSD applications in construction. The findings identified ten sensor types used to assess four mental status factors: risk perception, mental workload and fatigue, mental stress, and emotional states. For each sensor, the review details data collection procedures, including sensor brands and models, placements, and sampling rates. Additionally, it examines preprocessing techniques (i.e., noise filtering and artifact removal) and postprocessing methods, including feature extraction and metric computation, data interpretation, as well as mental status classification using rule-based and AI-based methods. Identified limitations and future research directions are also discussed. This study serves as a comprehensive guide for researchers and practitioners, promoting the broader adoption of PMSs and WSDs for mental status monitoring in construction.

Accidents in the Egyptian construction industry are rising due to insufficient worker safety knowledge. While traditional safety training methods are widely used, they are often ineffective and uninspiring. This research presents the first immersive, game-based safety training platform specifically tailored to the Egyptian construction industry, integrating augmented 360-degree panoramic images captured from real local job sites. The methodology comprises of three stages: 1) identifying safety practices, 2) platform development, and 3) platform evaluation and statistical analysis. In the first stage, safety experts were interviewed to identify current safety practices and the most frequent hazards in Egyptian construction sites. In the second stage, Platform Development, started with capturing 360-degree images from construction sites, identifying the safety hazards in the captured images, and identifying the associated OSHA standards with safety experts for game development. A game platform was developed using Unity 3D, featuring four interactive scenes: Educational, Assessment, Explanation, and Hazard Controls. The platform integrates Unity’s built-in features and C# scripting to create an interactive and immersive experience. In the final stage, 30 construction practitioners from two construction sites in Egypt tested the platform, evaluated its user interface, and assessed the quality of the 360-degree images. Feedback was collected through a questionnaire and analyzed using statistical analysis. Results showed that 92% of participants positively rated the user interface, and 97% recommended the platform as a safety training tool. This research offers a first-of-its-kind digital solution that addresses the gap in immersive safety training in Egypt, enhancing hazard recognition and knowledge retention through real-world simulation.

This paper presents an automated method for converting laser-scanned point cloud data of steel structures into accurate digital 3D models. Point cloud data from laser scanning in such environments typically contains gaps, occlusions, and noise that complicate precise digital reconstruction of complex steel frameworks. Our approach addresses these limitations by combining geometric feature analysis with skeleton-based topology preservation. The method identifies individual steel beam instances within the point cloud, determines precise beam orientations through iterative model-based algorithms, and reconstructs occluded sections using skeletal representations. Cross-sectional profiles are matched to standardized catalogs through multi-objective optimization, generating complete 3D models in IFC format. Validation on industrial point cloud data of I-beam steel structures demonstrates high accuracy, achieving a mean angular precision of 0.1° for beam orientation and a mean geometric deviation of 4.1 mm between source data and reconstructed models. The method maintains robust performance across varying point densities and partial occlusions. This technology addresses critical needs in the construction and manufacturing industries. Potential applications include automated as-built documentation, construction quality control, retrofit planning for existing structures, and generation of digital models for infrastructure management. The automated processing eliminates manual interpretation bottlenecks in point cloud workflows, reducing processing time significantly.

As safety sciences become more prominent, BIM-based simulations are attracting greater interest from various domains, from fire engineering to crowd management. The integration of BIM with advanced simulation tools and technologies has significantly advanced the field of hazard and crowd evacuation simulations. By providing detailed, dynamic, and interactive models, BIM enhances the accuracy and effectiveness of emergency planning and response strategies. This paper targets the development of a dynamic building model that can facilitate BIM-based fire protection and evacuation planning and training. In current practice, the building model is input to simulation tools as a static model. The latter provides comprehensive information about the building, but this information is static and represents the building in an idealized state where no changes are happening over time. We propose an ontological multi-model framework by extending the information container for linked document delivery (ICDD) implementation, ISO 21597, to a dynamic multi-model container aiming to explicitly allocate multiple dynamic values to elements in the building model. This enables consideration of changeable building spaces during the simulation runtime and hence real-time interoperability of the interlinked simulation components and modules.

Construction digitalization faces persistent data management and interoperability challenges. Although ISO 19650 streamlines Building Information Modelling (BIM) processes, the lack of standardized data structures hinders efficient information exchange. Product Data Templates (PDTs)(EN ISO 23387:2020, EN ISO 23386:2020) offer a structured framework for product data definition and exchange, addressing this issue by enhancing digital workflows consistency. This paper investigates the development of an open platform for the Portuguese construction industry to create, manage, and query PDTs in alignment with international standards. Key components include a PDT-compliant database, an interface for reviewing and downloading PDTs in digital formats, and an API gateway for integrating PDTs into BIM environments, and integration into buildingSMART data dictionary. The platform supports industry stakeholders by ensuring data consistency, traceability, and regulatory compliance. By providing machine-readable, interoperable data for seamless retrieval, it enhances data exchange and reduces inefficiencies in digital construction workflows. Developed as an open-source solution, the platform is designed for the Portuguese market but can be adapted for use in other regions, ensuring scalability and broader applicability. Future research could focus on a deeper integration with Common Data Environments, the Level of Information Need, and a Linked Data approach to further optimize structured data exchange and automation in BIM workflows.

Design and construction projects have become increasingly complex throughout recent decades, resulting in a heightened focus on knowledge management. Two specific knowledge types, which have received consideration in the literature are design intent and design rationale, as means for describing what is designed and why a design is the way it is. However, a definition of what design rationale and design intent are and what they consist of, in context of the AEC industry, has not been fully explored. In this study, a literature review, was conducted to summarise the definitions of design rationale and intent presented in the existing body of scientific literature across scientific disciplines. Both qualitative evaluation of the identified literature and Natural Language Processing (NLP) was utilised, identifying the distinctions between design rationale and design intent, and their hidden specificities. The study revealed that a design project must have both explicit design rationale and intent available to be successful, which can guide a designer or project manager in the direction of unexplored design alternatives if something in the design must be changed, instead of exploring already tested and discarded solutions. The study, furthermore, showed that having DR and DI available can support decision makers during design, pre-planning, and construction to ensure that their design solution lives up to the intent of the designer or building owner, through specifying the explicit justification for the solution, and not just what the solution is, should be, or consist of.

The adoption of Building Information Modeling (BIM) and Common Data Environments (CDE) in construction has increased the need for robust security and privacy frameworks, especially as these platforms increasingly leverage cloud-based technologies. This paper examines the mechanisms required to protect sensitive project data in BIM and CDE environments, including financial data, intellectual property and personal data. It emphasises the crucial role of trust — cognition-based, affect-based and system-based — in promoting safe and collaborative workflows and explores the concept of digital trust as a foundation for reliable partnerships in cloud-based construction environments. A critical analysis of ISO 19650-5 identifies significant gaps in addressing the complexity of cyber security and argues for explicit technical guidelines to protect against cyber threats. It also highlights inconsistencies in the transparency of security practises of widely used BIM and CDE providers and emphasises the need for improved disclosure to increase user trust. To provide a structured approach to threat mitigation, we introduce the Process for Attack Simulation and Threat Analysis (PASTA) framework and demonstrate its applicability in aligning business and technical risks within BIM and CDE workflows. In addition, this paper proposes to integrate security requirements into the BIM Execution Plan (BEP) to improve alignment across all project phases. Recommendations are provided for providers and users to establish a trust-based approach to information security that ensures compliance with standards and promotes the resilience of construction workflows. This paper highlights practical pathways to enhance the security of BIM and CDE implementations, focusing on robust technical safeguards and the role of trust in collaborative projects.