The building renovation sector is under growing pressure to balance environmental responsibility, economic efficiency, and occupant well-being simultaneously. Existing evaluation approaches are predominantly finance-driven, marginalising ecological and social dimensions. This study develops and validates a parametric multi-criteria assessment framework for building renovation elements, structured around the three pillars of sustainability: environmental, economic, and social. A dataset of 33 renovation elements—encompassing green façade systems, extensive and intensive green roofs, interior wall, floor, and ceiling solutions, and exterior envelope and site components—was compiled and digitized as BIM objects in ArchiCAD 26, enriched with non-graphic parameters including cost, lifespan, recyclability, eco-index, maintenance effort, and qualitative social descriptors. Parameters were aggregated using type-specific logic: additive summation for economic indicators, minimum-value selection for lifespan, arithmetic mean for environmental indicators, and descriptive consolidation for social attributes. Five renovation scenarios (A–E), each composed of nine elements, were evaluated to demonstrate how the sustainability profile changes with selection priorities. Scenarios A, B, and C confirmed single-dimension dominance (environmental, economic, and social, respectively), Scenario D achieved a balanced three-pillar profile, and Scenario E revealed a latent economic bias in an apparently random element selection. The framework is scalable and extensible, and its data structure may provide a basis for future exploration of integration with BIM environments.

The restoration of historical buildings is a complex and multidisciplinary process influenced by historical, technical, material, and economic factors. This paper introduces a novel hybrid methodology for cost estimation based on a decision tree structure combined with multi-criteria analysis, integrating both subjective (AHP) and objective (entropy) weighting methods. A unique contribution of this study is the development of the Intervention Effectiveness Index (IEI), which quantifies the trade-off between restoration value and cost. The inclusion of Environmental, Social, and Governance (ESG) criteria further enhances the sustainability dimension of the analysis. The proposed framework allows for the structured integration of expert knowledge, heritage guidelines, and cost-related parameters to optimize intervention strategies. A case study on historical masonry illustrates the practical application of this model, which is scalable to other architectural components. This comprehensive and adaptable decision-support tool contributes to more accurate, transparent, and sustainable restoration planning aligned with both conservation objectives and budgetary constraints.

The construction sector produces more than a 30

This study presents a comparative life cycle assessment (LCA) of two office buildings that differ in the type of roofing system: one has a biosolar green roof, and the other has a conventional roof. The main indicator monitored is Global Warming Potential (GWP), with particular attention paid to the relationship between the production phase and the operational phase, as well as the benefits of recycling and reuse of materials. The central question that the study seeks to answer is to what extent energy savings during operation and the recycling potential of roofing materials can offset the higher environmental impact associated with a biosolar roof. The findings contribute to the current scientific debate on the effectiveness of circular economy principles in construction and provide practical insights for the design of sustainable buildings in the context of current climate goals.

This paper examines four types of roof assemblies, namely intensive, semi-intensive and extensive green roofs, and conventional roof in terms of their life cycle environmental impacts. The life cycle assessment (LCA) is conducted for Cradle-to-Cradle boundary system, considering a functional unit of 1 m2 for a lifespan of 50 years and using the OneClickLCA software. The aim was to determine the extent to which each type of assembly contributes to the environmental impacts over the entire life cycle. In addition, this study includes carbon foot-print analysis, accounting for the carbon sequestration capacity of plants on green roofs. The findings contribute to identifying roof designs that minimize environmental impact over their lifespan.

The research focuses on the potential for, and perceptions of, using artificial intelligence in relation to key processes in the construction sector, such as design, resource planning, and project delivery time. The study is grounded in a survey of civil engineering professionals, examining the level of AI adoption and its impacts in terms of the range of tools used, intensity of use, and the capacity to integrate AI into established procedures and workflows, particularly with respect to processing efficiency, quality, and re-source utilization. After defining the survey instrument and verifying its content validity, we analyze relationships between AI use and its implications for planning, in the context of expected outputs for project design and the delivery of time- and material-related commitments. Our underlying assumption is that greater use of AI will be associated with higher design efficiency and improved planning performance in civil engineering (H1–H2). We further hypothesize that these effects will be more pronounced depending on firm size and/or project scale (H3). The aim of the research is to ana-lyse AI adoption and its impacts on the processes under review, and to pro-vide civil engineering professionals with relevant, research-based conclusions and evidence-informed.

The main objective of this study is to produce a systematic literature review that analyses key performance indicators (KPI) in the context of efficient and sustainable building renovation. Efficiency and sustainability, in combination with building renovation, are important themes due to the increasing need for creating sustainable renovations worldwide. The identification and monitoring of KPIs is fundamental in decision-making processes, but also in the monitoring of short-term and long-term project goals. In the current academic literature, existing research gaps, especially in the social aspects of sustainability and research, have also been analyzed in terms of regional differences in the approach to each KPI. The systematic literature review examined 29 studies published between 2014 and 2024, based on a literature search conducted in 2024, using databases such as Scopus and Web of Science, with the final search performed in June 2024. The inclusion criteria focused on peer-reviewed studies addressing KPIs in sustainable building renovation, while studies not directly related to renovation processes or lacking KPI analysis were excluded. The research results show that the majority of studies focus on economic and environmental factors, which are the most commonly addressed, while research on other KPIs is significantly behind. The results were synthesized using a qualitative comparative analysis of identified KPI categories. This study also highlights the importance of addressing effective and sustainable renovation for historic buildings with a focus on heritage preservation and the need to further analyze the use of KPIs with a focus on historic buildings. The limitations include the limited number of studies and the underrepresentation of social sustainability aspects.

The development of the Industry 4.0 concept has brought significant transformative impulses to the construction sector, which, thanks to digital technologies, is increasingly shifting toward the paradigm of Smart construction. A key element of this transformation is the use of artificial intelligence (AI), which is being applied across the entire construction cycle—from planning and construction to building management. Despite its potential to enhance efficiency, quality, and safety in construction processes, the adoption rate of AI technologies in practice remains uneven and is influences by numerous technical, organizational, and cultural factors. The aim of this research is to analyze the current state and prospects of AI utilization in the context of smart construction, focusing on the types of implemented technologies, their areas of application, and perceived impacts on the efficiency of construction processes. The methodological framework is based on a review of the existing literature and an analysis of primary data obtained through a questionnaire survey among professionals in the sector. Research findings indicate a growing interest in the adoption of AI technologies, with the most common applications focusing on predictive analytics, schedule optimization, and risk management. The studies also identified several benefits, such as increased productivity and cost reduction, as well as barriers, including high investment costs, a lack of qualified personnel, and a low level of digital readiness. The findings highlight the need for strategic support of AI adoption by organizational leadership and policymakers, thereby contributing to the formation of effective measures for the digitalization of the construction sector.

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.

This paper discusses the environmental life cycle aspects of three variants of ground floor constructions using different insulation materials: EPS thermal insulation (F1), foam glass (F2) and stone wool (F3). The objective is to analyse the environmental impacts of each variant throughout its entire life cycle using Life Cycle Assessment method. Regarding insulation materials, it can be characterizing foam glass as material with high moisture resistance, long-term stability, recycling potential and low overall environmental footprint. Stone wool offers excellent thermal insulation properties, is made from natural raw materials, has a long lifespan, and is recyclable. EPS insulation, while affordable and lightweight, has a higher environmental impact during production and disposal. The analysis shows that the environmental sustainability of these materials is largely influenced by their production processes and the potential for reuse and recycling. According to the multi-criteria decision analysis, F1 is the most suitable choice, with F3 variant ranking second and F2 identified as the least favourable option. These findings provide valuable insights for builders and architects seeking to minimise the environmental impact of floor structures.

The aim of this article is to define the concept of efficiency, to identify its importance in the construction industry and to present specific case studies of efficient building renovations. In exploring this topic, an analysis of the available literature and the study of real case studies from around the world was used. The findings show that efficiency in building renovation has a significant impact not only on economic performance but also on environmental and social sustainability. Economic considerations are often a priority for project participants, with the greatest emphasis on investment costs and return on investment. Social criteria such as thermal comfort are also important, especially during the occupancy phase. In particular, effective renovation can bring improvements in the environmental impact of the renovated building. The importance of efficient building renovation is evident not only from an environmental point of view, but also from an economic and social point of view.

The integration of artificial intelligence (AI) and digital technologies into cultural heritage management represents a significant advance in interdisciplinary research. These technologies, such as 3D modeling, GIS (Geographic Information System), and natural language processing (NLP), enable the analysis of large historical datasets, the prediction of degradation, and the optimization of restoration strategies. This study analyzes in detail the technical, ethical, and practical aspects of AI application in the field of cultural heritage conservation, highlighting benefits such as sustainability, improved accessibility of historical data, and enhanced interdisciplinary collaboration. The study also identifies challenges associated with implementing these technologies, including standardization of data formats and long-term compatibility of the technologies. We showed that it is possible to utilize large language models (LLMs) to process unstructured archival documents related to Slovak cultural heritage and extract key information important for the management of historical buildings.

Artificial intelligence (AI) is a phenomenon and a field that engages numerous experts and scholars. From a practical standpoint, it aligns with ongoing digitization and digital transformation across industrial sectors, including construction and education. Developing managers—strengthening their competencies, knowledge, and skill sets—should be a pillar of every firm’s or institution’s development plan. The push for digitization brings new challenges as well as opportunities that can benefit the sector and its people, managers included. This research examines the digital competencies and educational readiness of managers in the construction industry for the implementation of AI, providing a mapping of the issue across the sector. Its objective is to analyze the level of digital preparedness and the competency profile of construction managers necessary for the successful adoption of artificial intelligence. The study draws on questionnaire data collected from professionals in various types of construction organizations. The primary focus is the extent of construction managers’ digital skills required for the active use and implementation of AI. The survey also assesses readiness for digital transformation and AI deployment within construction enterprises. The research sample consists of representatives (managers) of construction companies in Slovakia.

Humanity’s Human construction has greatly improved living standards but at a high environmental cost. Buildings are responsible for 37

In a context of increasing criticality of the historical building heritage, interventions range between conservation and transformation, including restoration, maintenance and modernisation activities to preserve or improve the original qualities of buildings. These aim to correct structural deficiencies, such as loss of efficiency due to ageing or external factors, while adapting buildings to new economic, cultural and social needs. Among the emerging methodologies, retrofit represents an approach distinct from simple maintenance, introducing new functionalities not originally foreseen and thus modifying the functioning of technically obsolete structures. This process takes the form of a technological and cultural upgrade, oriented towards improving energy and architectural performance, responding to the challenges of environmental sustainability through innovative strategies. The article explores the concept of retrofit in an analytical and cognitive manner, exploring the use of advanced ICT technologies and Building Information Modelling (BIM) as tools for the evaluation and management of building transformations. Through the analysis of energy behaviour, dynamic simulation and performance control, BIM emerges as a technical and regulatory solution for sustainable retrofit in a context of economic, social and environmental sustainability.

In order to meet the high housing demand and location requirements, developers in large cities increasingly often purchase former industrial sites for conversion and redevelopment. The authors in the article conducted a survey to gauge interest in residential development on post-industrial sites in two countries (in Poland and Slovakia) and determined the group of people interested in this sector. The main focus of the survey was to gain insight into the factors that motivate the purchase of real estate and influence the choice of residential property location. Another goal of the study was to determine the importance of factors that influence decisions to purchase real estate in a post-industrial area. As a result of the survey, the authors also determined whether potential buyers paid attention to the previous development of a property and whether they were aware of the potential risks associated with converting a brownfield site to residential use.

Documentation of historic buildings, not only in Slovakia, is often chaotic, incomplete, and the information is unclear. Managers of historic buildings and monuments offices preserve historical and building information mainly in text documents, which are fragmented and unstructured. The aim of this article is to propose a consistent data structure in the form of a data model that will enable the systematic classification and management of specific data on the historic buildings. The model also makes it possible to identify which types of data are relevant for different user groups and supports more efficient evidence, analysis, and utilization of information in decision-making processes. The developed data model reflects the interconnection of several entities, such as Building, Architecture, Technical Specifications, Legal Protection, and Economic Data. The proposed approach provides a basis for the digitization and integration of data on historical objects, contributes to improved renovation planning, and offers a framework for more efficient decision-making in the field of cultural heritage protection.

Cost tracking is a key aspect of project management and financial planning in the construction industry. In the context of a dynamic and competitive construction industry, every project represents a significant financial investment and effective cost tracking is an essential tool for success. Effective cost tracking enables project managers and investors to accurately plan budgets, identify potential cost overruns and take action to mitigate them, optimise resources, increase transparency and improve collaboration and decision-making. Based on the above, the implementation of Life Cycle Costing (LCC) methodology is increasingly coming to the fore. The issue of LCC is a key aspect of modern cost management and sustainable resource management. Against this background, the aim of this study was to review the theoretical foundations, developments and to analyse the different approaches, models and technological innovations used for LCC assessment. The primary sources of information were the multidisciplinary citation and abstract databases Scopus and Web of Science (WoS), which are important tools for the scientific research activities of individual institutions and organisations. The paper concludes by defining the objectives of future research focusing on the possibilities of integrating innovative technologies as primary tools supporting efficient and sustainable management of building life cycle costs.