IntroductionRecent studies emphasize the critical role of documentation quality management within the entire life cycle of a construction project. While prior studies addressed how quality of the documentation and change management affects the project delivery, a stakeholder-specific view on the effect of documentation quality on the project success has not been adequately explained. To address this gap, this paper aims to examine how technical specification of the tender documentation, specifically the quality of drawings and list of work, supplies, and services, affects the success of the project from the iron triangle perspective and designers’ viewpoint.MethodsQuantitative data was collected via a questionnaire survey administered to construction professionals in the Czech Republic. The data was analysed using partial least squares structural equation modelling approach. The analysed model consisted of four reflective constructs. Both direct relationships and indirect relationships through mediation were examined.ResultsThe findings prove that experience of design documentation suppliers and knowledge in drawing documentation as well as the list works, supplies and services have statistically significant positive effect on the production of high-quality documentation. Furthermore, these factors are positively associated with project success in terms of costs, timely completion, and quality outcomes. The analysed model confirms both direct and indirect effects on the project success, with documentation quality serving as a significant mediating variable.DiscussionThe findings underscore the importance of documentation quality management at the early stages of the construction project and the need to consider a stakeholder-specific perspective on its provision. Several managerial implications for project-based organizations are discussed with emphasis on careful selection of design supplier by clients.

Digital transformation is increasingly shaping the planning, construction, and operation of infrastructure projects. Building Information Modeling (BIM) plays a central role in this process by integrating geometric, technical, and organizational information into a consistent digital model, thereby enabling improved collaboration, transparency, and efficiency across the entire project lifecycle. Beyond its technological dimension, BIM represents a fundamental paradigm shift toward data-driven and more sustainable planning processes in the construction and infrastructure sectors. This paper examines the current state and potential of BIM in infrastructure planning in Germany based on a structured literature review and selected practical examples, with particular consideration given to relevant legal and regulatory aspects. Key challenges in BIM implementation are identified, particularly with regard to organizational change, data management, interoperability, and the need for standardized processes. At the same time, essential success factors and best practices derived from infrastructure projects are highlighted. Furthermore, the potential role of Artificial Intelligence (AI) in the context of BIM-based workflows is explored. Although AI has not yet been widely adopted in planning practice, it offers significant potential for applications such as automated model checking, construction schedule forecasting, risk analysis, and the optimization of planning and execution processes. The paper concludes by outlining future perspectives for the integration of BIM and AI as complementary drivers of digital transformation in infrastructure planning.

Although urban morphology has a long tradition, only recently has its interrelation with the risk and resilience of urbanised communities gained greater prominence. The aim of this paper is to present the framework, current state of the art, and approaches to these topics by addressing the following research questions: Which research areas address urban morphology and risk resilience? What topics are related to urban morphology and risk resilience, and what general overlaps or gaps exist? What are the approaches to urban morphology and to risk resilience? Do these topics correlate with the UN SDGs and the Sendai FDDR? The research finds that this is a topic of growing interest, with many interconnected and overlapping topics and related to various scientific fields. It identifies two distinct groups of papers based on their methodological approaches and tools. It identifies several core contradictions: sectoral approaches versus the complexity of reality; narrative versus analytical approaches; frictions arising from differing needs related to various morphologies; and overlaps and gaps in topics and connections to the UN SDGs and Sendai FDDR. The research highlights the need to shift toward high-tech analytical and generative tools based on improved spatial data, adopt multirisk rather than single-sector scenarios, move from narrative to analytical and actionable social resilience research, and prioritize adaptive redundancy over minimal functionality, thereby creating spaces with adaptive morphologies. It also demonstrates how urban form affects the risk and resilience of urbanised areas and different populations providing a basis for policy-related research and implementation.

Building information modelling (BIM) is now routine in how construction firms design and document projects; the open question is what artificial intelligence (AI) can add on top of that foundation. Little is known about what moves a firm from BIM to AI, particularly in smaller Central European markets and in small and medium-sized enterprises. The paper asks three questions: how widely BIM is used across project stages, whether that use shows in cost, waste, and sustainability figures, and what stands in the way of AI adoption. Evidence comes from two surveys grounded in official records: one covering firms in Slovakia, Croatia, and Slovenia, the other on Slovak firms’ AI experience and obstacles. Responses were analyzed with descriptive statistics, Kruskal-Wallis tests, and Spearman correlations, with the BIM-sustainability link cross-checked against a convergent model. BIM maturity differs clearly between the three countries, from a mean of 1.33 in Slovakia to 2.49 in Croatia and 4.33 in Slovenia on a five-point scale (H = 86.77, p < 0.001), and firms using BIM more report better cost, waste, and sustainability outcomes (ρ up to 0.93 for material cost, 0.92 for emission reduction). AI use remains rare. What distinguishes AI-ready firms is digital maturity (ρ = 0.49 for digital-tools use, 0.37 for digitalization) rather than size (ρ = 0.01, not significant), and the barriers cited most concern people and skills rather than technology. With the regional sample in mind, the findings point to a solid digital and BIM base as AI’s realistic starting point.

The rapid development of digital technologies presents both a challenge and an opportunity for strengthening sustainability in construction project management. Within the broader digitalization agenda, Building Information Modelling (BIM) and Artificial Intelligence (AI) have emerged as key tools for improving environmental and economic performance through resource optimization. While traditional methods for optimizing resources, costs, and time remain relevant, the integration of BIM and AI introduces innovative capabilities that support decision-making, process automation, and data-driven sustainability strategies. The aim of this research is to analyze the extent to which BIM and AI are used for sustainable resource optimization in construction and to quantify their potential impact on the optimization of costs, resources, and time in the sector. A cross-sectional survey was conducted among construction companies operating in three European markets, Slovakia, Slovenia, and Croatia. The collected data were analyzed using descriptive statistics, correlation and regression analysis, and statistical hypothesis testing to assess the significance of relationships between technology adoption and sustainability outcomes. The results confirm that BIM adoption is positively correlated with improved sustainability management and optimization practices, with usage levels varying by company size and project scale. In contrast, AI adoption remains at a low level, indicating untapped potential for broader application. These findings contribute to understanding the role of digital tools in driving sustainable transformation in the construction sector and highlight areas for further research and practical deployment. BIM demonstrates particularly strong correlations with cost planning (r = 0.983), resource planning (r = 0.964), and schedule planning (r = 0.867), while AI shows robust associations with cost planning (r = 0.925), schedule planning (r = 0.865), and resource planning (r = 0.809). The findings indicate that maximum effectiveness is achieved when BIM and AI are deployed in a complementary manner under skilled human oversight.

Life cycle cost (LCC) analysis has become a key tool for evaluating the long-term economic and environmental performance of built assets, yet its application in marinas and marine infrastructure remains underdeveloped. This review provides the first structured attempt to apply LCC to marina infrastructure, addressing the lack of sector-specific models for pontoons, mooring systems, and marina operations. It also synthesizes research on LCC methodologies, challenges, and emerging trends relevant to coastal facilities, with a particular focus on pontoons, mooring systems, and marina management practices. Studies reveal persistent barriers to effective implementation, including fragmented data systems, inconsistent regulations, and limited sector-specific tools. Existing models, largely adapted from other construction contexts, often overlook the unique technical, environmental, and operational demands of marine assets. The review critically examines international standards, procurement frameworks, and methodological approaches, highlighting opportunities to integrate sustainability considerations and address gaps in cost forecasting. It also identifies the need for standardized data collection practices and risk-based maintenance strategies tailored to harsh marine environments. By mapping current knowledge and methodological limitations, this work provides a foundation for developing more accurate, sector-specific LCC models and guidance. This literature review contributes to the advancement of sustainable coastal infrastructure planning by consolidating scattered research, emphasizing knowledge gaps, and outlining priorities for future studies, supporting policymakers, practitioners, and researchers seeking to optimize investment decisions in marinas and related facilities.

Adverse weather events have a negative impact on the productivity of construction site activities. Understanding these effects is essential for developing realistic construction schedules. The influence of weather is shaped by both environmental factors (climate, geography, topography) and construction-related aspects such as technologies, materials, equipment, and site exposure. This paper proposes a model to quantify the influence of adverse weather by estimating monthly intervals of expected days with reduced construction productivity, based on data regarding specific weather events, including precipitation, wind, extreme temperatures, snow cover, fog, and high humidity. Data analysis employs the inclusion–exclusion principle, a combinatorial technique, alongside confidence interval estimation, a standard statistical approach. The model was applied in three Croatian cities to demonstrate its practicality and accuracy. Contractors with extensive on-site experience reviewed the results, providing insights into weather-sensitive activities and organizational practices.

This paper analyzes environmental performance indicators (PIs) in the construction and building industry using bibliometric and content analysis, particularly in the fields of architecture and civil engineering. The paper aims to present a framework for environmental performance in the construction industry, focusing on projects and their impacts. It addresses which research fields are most focused on this area, whether the topic is currently relevant, whether it shows a positive or negative trend, what related topics exist, and what general overlaps or gaps are present. It also examines which PIs are most frequently mentioned and whether the topics and indicators align with the United Nations Sustainable Development Goals (UN SDGs). The results reveal a fragmented research area, with both complex PIs and very narrow PI applications, highlighting the need to bridge these gaps and address the challenge of insufficient data. The research uses QtoQ Target Mapping to map the PIs to the UN SDGs and provide an overview of coverage. The findings indicate that this topic is highly important and researched across various disciplines, and that the PIs and their analysis further contribute to the Sustainable Development Goals.

This research focuses on developing neural network-based models for predicting time and cost overruns in construction projects during the construction phase, incorporating sustainability considerations. Previous studies have identified seven key performance areas that affect the final outcome: productivity, quality, time, cost, safety, team satisfaction, and client satisfaction. Although the interconnections among these performance areas are recognized, their exact relationships and impacts are not fully understood. Hence, the utilization of a neural networks proves to be highly beneficial in predicting the outcome of future construction projects, as it can learn from data and identify patterns, without requiring a complete understanding of these mutual influences. The neural network was trained and tested on the data collected on five completed construction projects, each analyzed at three distinct stages of execution. A total of 182 experiments were conducted to explore different neural network architectures. The most effective configurations for predicting time and cost overruns were identified and evaluated, demonstrating the potential of neural network-based approaches to support more sustainable and proactive project management. The time overrun prediction model demonstrated high accuracy, achieving a MAPE of 10.93%, RMSE of 0.128, and correlation of 0.979. While the cost overrun model showed a lower predictive accuracy, its MAPE (166.76%), RMSE (0.4179), and correlation (0.936) values indicate potential for further refinement. These findings highlight the applicability of neural network-based approaches in construction project management and their potential to support more proactive and informed decision-making.

The selection of an appropriate formwork system represents a critical decision in the planning of reinforced concrete multi-story buildings. While this decision has traditionally been deferred to the construction phase, increasing evidence of time and cost overruns in construction projects has highlighted the necessity of addressing it during earlier stages, particularly in design and planning. Early identification and selection of the optimal formwork system enhances the likelihood of achieving significant improvements in both time efficiency and cost effectiveness. To facilitate this process, a decision-support framework based on the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods has been developed. This framework provides decision-makers with a structured and systematic approach for evaluating alternatives and selecting the most suitable formwork system for a given project. By offering an analytical foundation for the decision-making process, the framework assists designers and engineers in mitigating risks associated with delays and potential standstills during construction. The findings indicate that the proposed decision-support framework ensures both clarity and consistency in decision-making outcomes, irrespective of the analytical method employed. Consequently, it contributes to more robust planning and execution of construction projects.

The digital transformation of built environments into 3D computer models via photogrammetry for inspection, documentation, or remote mapping has evolved over the last few decades. Today, the appropriateness of different cameras and laser scanners gives even more opportunity for engineers in different situations to capture and create very accurate and high-detailed 3D models. The Structure-from-Motion Multi-View Stereo (SfM-MVS) photogrammetry is a prominent way of reducing capturing time even more by using a 360-degree camera. In situations of buildings in distress, especially those exposed to extreme environmental hazards, such a method for rapid photogrammetric reconstructions seems to be the most suitable one for everyone, especially for first responders. Therefore, the application of SfM-MVS photogrammetry in first response inspection of distressed buildings is presented in a case study from a recent earthquake series that happened during the year 2020 in Croatia. The goal of this paper is to bring insights into the possibilities of applying SfM-MVS photogrammetry in the built environment and to present such digital models in a way that can help first responders make informative decisions for inspecting distressed buildings.

Predstavljeno je istraživanje koje je provodeno u okviru znanstvenog-razvojnog projekta UNIRI INOVA „Inovativni priključak za spajanje konstrukcijskih elemenata od tankostijenih čeličnih C-profila“. Cilj projekta je pružiti bolje razumijevanje o ponašanju komponenti inovativnog priključka za spajanje elemenata od tankostijenih čeličnih C-profila

Construction generally takes place in very variable and dynamic conditions due to the presence of machinery, transport of materials, movement of workers, and the progress of construction. In order to solve the problem of site variable and dynamic conditions, two major research topics may stand out, more precisely, off-site performance planning and on-site performance tracking and monitoring. Earthworks commonly involve extensive utilization of various construction machines, mainly excavators and tipper (or dump) trucks. Tracking and monitoring of earthworks, based on collected quality data from the construction site, are necessary to detect discrepancies between the planned and actual work efficiency of construction machinery. The main topic of this paper is the time study analysis of one of the standard technological processes in earthworks – loading and transporting materials by tipper trucks. On-site data were obtained using the stopwatch method with the purpose of determining the actual work efficiency of the tipper truck. This paper aims to propose a research framework for clear insight into the progress effectiveness of earthworks and the actual work efficiency of the tipper truck. The purpose of applying the research framework is to form a base for tracking and monitoring the work of tipper trucks so the right decisions can be made in a timely manner. Such can enhance earthworks progress by using the appropriate number of tipper trucks and their better utilization.

In the second half of 2020, there was a strong increase in building material prices on world markets, which continued in 2021. Given the unstable geopolitical and epidemiological circumstances, future price fluctuations are unknown. Construction projects are sensitive to such oscillations as contracts and plans are concluded at a time when price changes cannot be predicted with certainty. Given the degree of environmental construction, maintenance projects of existing buildings are becoming increasingly important. Public-funded institution maintenance projects under regional and local self-governments are particularly at risk. Public administrators make maintenance decisions based on existing priority systems and available budgets, which are already limited and pressured due to rising material prices. To mitigate the disturbances, this paper aims to analyze fluctuations in the market of frequently used maintenance materials and provide support to public administrators, i.e., decision-makers, in the form of maintenance management recommendations, in the context of activating the price increases risk. The research consists of two parts, a review of the theoretical background and the processing of material cost data. From the available literature, insight was gained into the current way of planning, budgeting, and way of maintenance performing of public institutions in the Republic of Croatia. The problem of fluctuation in construction material prices and its impact on maintenance was recognized. Often used maintenance materials prices data were collected and analyzed, the resulting changes were graphically and numerically presented, and the problems these changes cause within the maintenance process were identified. Finally, recommendations were made to mitigate the identified disorders that public administrators will be able to apply.

In the field of spatial management, especially from the point of view of spatial economics, many decisions have long-term effects on the environment. Therefore, it is essential that the boundary conditions of the decision-making process are not only transparent, but also aligned with the sustainable development goals. At the same time, road infrastructure represents one of the most complex linear structures in the area, and its comprehensive management should respond to various types of problems, from structured to unstructured, which occur at all levels of decision-making. To solve such problems, management information systems (UIS) can help decision-makers to apply appropriate models and methods during the decision-making process. This paper provides an overview of the different types of UIS that can be used in road infrastructure management. The method of selecting an appropriate UIS and the method of interaction between data and models in such a management system are also discussed.

Despite good ideas, great efforts, and high investments, many projects do not end with success. Projects often fail due to a lack of understanding of the project requirements and constraints necessary for overall success. Five selected projects were analyzed in detail through the multiple case study method followed by semi-structured interviews with 56 experts to develop a pattern for the future prediction of project success. This paper aims to identify key factors for project performance in a multi-stakeholder environment, define a performance measurement framework for construction investments, and establish a link between performance measurement and prediction of project performance. The findings could help researchers in modeling performance measurement tools for project managers to achieve their designated project goals, reach better decisions, and achieve full potential in their future projects.

Performance management belongs to crucial managerial activities. This study aims to address how performance management in construction is applied among Czech public organizations on the project level. In order to address this issue, qualitative data has been collected by semi-structured interviews of experienced experts representing organizations owning/operating important facilities and infrastructure (road and rail infrastructure, water and sewage systems, education facilities and collectors). Findings revealed that the level of performance management adoption is rather low, organizations mostly focus on supplier performance evaluation as a response to the ISO 9001 requirements. The practical use of BIM or life-cycle costing is rather in the reflection and preparation phase, on the other hand, a progressive approach is applied in terms of deployment of modern equipment for monitoring of structural defects or the use of robots for maintenance and repairs.

The construction industry is generally one of the fundamental industries of a country, weighing between 5 to 10% of the gross domestic product. Rapid changes in the construction environment require a great deal of effort for a company or project manager to maintain the project successfully. To do so, applying performance management is of crucial importance. Therefore, a systematic literature review was conducted to detect new trends and highlight the evolvement of this research topic. The conducted bibliometric analysis resulted in 1240 documents published in Scopus and Web of Science databases in the period from 2000–2021. The bibliometric indicators, network citations, and multivariate statistical analysis were obtained using JabRef, OpenRefine, Excel, and VOSviewer tools. The co-occurrence analysis showed three keywords clusters as current research hotspots that may be considered as potential research topics in the future: (1) value management in the construction industry, (2) organisation innovation and knowledge management in a particular company, and (3) project management tools and techniques for a particular construction project.

Mediterranean towns and their surroundings show specific characteristics, such as urban structure, presence of complex stratification of heritage, and often seasonality, which makes the choice of spatial organization and construction technology for building construction of high importance in relation to sustainable development. For such purpose, the SOnCT model, based on multi-criteria decision analysis, has been developed which takes into account optimal building interventions in Mediterranean towns from a sustainable development perspective, highlighting their spatial-technical aspects. The presented research answers the questions of how sustainable development goals can be implemented in the case of construction interventions in Mediterranean areas, especially in smaller settlements that present very fragile status and specific characteristics not comparable to northern towns. This paper presents the construction and verification of the evaluation and prioritization model for selecting the optimal spatial organization and construction technology based on the criteria of sustainability, spatial characteristics, and the United Nations' Sustainable development goals.