Zvonko Sigmund

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.

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.

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.

The European Union's (EU) main goal, as stated in the EU Green Deal, is to achieve climate neutrality by 2050, and the construction sector plays a significant role in this achievement through efforts to make infrastructure, commercial, and private projects more sustainable. For many years, Cost-Benefit Analysis (CBA) has been used to evaluate and make

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 governance is mostly viewed through the lens of disaster or emergency management departments, agencies, or organizations. Visible in times of crises, risk governance is rarely seen as part of everyday public or private functions such as planning, social welfare, investments, or fiscal responsibilities. This paper emphasizes the importance of disaster risk governance in disaster risk management activities on the example of the post-disaster recovery of Croatia after a series of strong seismic events in mainland Croatia. The analysis is made based on a thorough review of national documents of Croatia and other selected countries overlapped with the national journals reporting on the situation from the affected areas. In accordance with the authors’ opinion, the necessary elements of disaster risk governance are clearly stated through the four Sendai framework priorities, and this statement is supported by the facts from the case study. Without either the political will or the enabling surrounding the disaster, risk management is next to impossible. The Croatian case study emphasizes the importance of disaster risk governance, showcasing the adaptation process for the post-disaster recovery process to start.

Increasing interests of circular economy (CE) principles applied into construction projects has led to the development of assessment methods for their justification. The use of smaller quantities of construction materials, materials of higher quality and durability, and recycling of construction waste are all requirements of today's aspiration for a circular economy, but it is necessary to assess their environmental and economic sustainability. This paper presents a review of the current assessment tools of circular economy projects applied to the construction industry, such as LCA, LCC and CBA. The main objectives of this study were to provide a categorization of CE concepts applied in the construction industry (CI) and to review assessment methods used for evaluating CE projects in CI. This paper selected and reviewed 96 published papers and classified them into one of five aspects of CE highlighted in this study: waste management, reducing the impact on the environment, material & product design, building design, and other. The results showed that the use of assessment methods in CE projects has increased in the recent years as well that an LCA was by far the most used assessment method and waste management the most common aspect of CE in CI.