The potential uses of 3D scan data in the construction industry have been extensively researched in the last 20 years, with many benefits over traditional methods proclaimed by researchers. However, despite their advocated benefits, their implementation in actual construction sites remains low. This research aims to discover the potential uses of 3D scan data for construction management purposes and the limitations and barriers to their implementation and widespread adoption. Previous research into the topic was analysed to discover what technologies were used for generating 3D scan data, for what purpose and what issues were identified. These discoveries were then used to specify the potential uses of 3D scan data for, primarily, progress monitoring and quality control, which were then cross-referenced with all known limitations and barriers from the literature and the researchers’ own experience. Research has shown that, currently, there are numerous issues with both the capabilities of current technical solutions and with the construction industry’s readiness, which hinder mass adoption. Potential for breakthroughs, fortunately, does exist; however, greater impetus from the construction industry is needed to drive forward the demand for better technical solutions, which would resolve current issues and lead to the widespread adoption of 3D scan data for construction management-related uses.

Risk management in large-scale construction projects is a critical yet complex process influenced by financial, safety, environmental, scheduling, and regulatory uncertainties. Effective risk management contributes directly to project optimization by minimizing disruptions, controlling costs, and enhancing decision-making efficiency. Early identification and mitigation of risks allow resources to be allocated where they have the greatest effect, thereby optimizing overall project outcomes. However, conventional methods such as expert judgment and probabilistic modeling often struggle to process extensive datasets and complex interdependencies among risk factors. This study explores the potential of an AI-based framework for risk identification, utilizing artificial intelligence to analyze project documentation and generate a preliminary set of identified risks. The proposed methodology is implemented on the ‘Trg pravde’ judicial infrastructure project in Zagreb, Croatia, applying AI models (GPT-5, Gemini 2.5, Sonnet 4.5) to identify phase-specific risks throughout the project lifecycle. The approach aims to improve the efficiency of risk identification, reduce human bias, and align with established project management methodologies such as PM². Initial findings suggest that the use of AI may broaden the range of identified risks and support more structured risk analysis, indicating its potential value as a complementary tool in risk management processes. However, human expertise remains crucial for prioritization, contextual interpretation, and mitigation. The study demonstrates that AI augments, rather than replaces, traditional risk management practices, enabling more proactive and data-driven decision-making in construction projects.

While Digital Twins offer transformative potential, their efficacy for real-time control is constrained by the slow data acquisition and the high computational intensity required to process raw datasets like point clouds. This paper identifies these critical bottlenecks—specifically the latency between data capture and actionable insight—and proposes a refined theoretical framework for near-real-time automated progress monitoring and quality reporting. Building on the findings of the NORMENG project and informing the subsequent AutoGreenTraC project, this research synthesizes state-of-the-art advancements in reality capture, including LIDAR, SfM-MVS, and 360-degree vision. The study highlights a fundamental divergence in stakeholder requirements: the need for millimeter-level precision in quality control versus the demand for high-velocity documentation for progress monitoring. A key innovation presented is the shift toward neural rendering techniques to bypass the computational delays of traditional photogrammetry and enable immediate on-site visualization. By structuring a tiered processing hierarchy that combines lightweight edge analysis for immediate safety and progress monitoring with asynchronous high-fidelity Digital Twin updates, the framework aims to establish a single source of truth.

This paper represents initial research with the aim to establishes a baseline for subsequent research into AI-based construction monitoring, building upon the NORMENG project in Croatia, which previously integrated photogrammetry, laser scanning, and BIM-based methods. The study tests general purpose AI’s ability to detect materials and estimate quantities, aiming to assess whether a broad, context-aware AI system can match the precision of specialized, domain-specific tools or even human work needed for productivity estimations. While the AI demonstrated potential for basic entity detection and preliminary quantity estimations, it showed significant limitations in delivering fine-grained, temporally accurate breakdowns without targeted adaptation. The findings underscore the need for domain-specific fine-tuning and human-in-the-loop validation to transform AI into a reliable tool for construction management. This initial contribution provides empirical insights and actionable recommendations for advancing automated progress monitoring in the construction sector.

Evacuation shelters are crucial for protecting people in areas affected by disasters, minimizing loss of life and increasing community resilience. Planning and allocating shelters reduce vulnerability and include improving infrastructure, crisis management, education, legislation, early warning systems, and recovery strategies. However, uneven distribution of shelters and differences in population density limit their accessibility. Shelter location selection is based on distance from disaster areas, access to green spaces, and building quality. The article proposes a research framework to analyze the impact of population numbers on shelter location selection, aiming to place shelters outside vulnerable areas to maximize safety. The study offers practical information for future disaster planning, prevention, and preparation.

This paper has a goal to show basic terms regarding parametric modelling, regarding the fact that it has become an unavoidable tool for designers in architecture and civil engineering as well as in other fields of engineering. Basic tools from one of the most widespread modelling application Rhinoceros 3D and its parametric modelling addon Grasshopper will be shown, and also will be shown a development of a conceptual model and the making of variant solutions based on parameter change. References and guidelines for future research of parametric modelling will also be given.