This study demonstrates the effectiveness of a multi-method approach for the restoration of a historic building (train station) in Poland. The project employed field investigations, laboratory analyses, and close-range photogrammetry to create a Historic Building Information Model (HBIM). This comprehensive data set informed the development of targeted conservation strategies that addressed the station’s specific needs while respecting its historical significance. Interventions prioritized the use of locally sourced and sustainable materials, minimized the visual impact on the exterior, and achieved net-zero emissions through improvements to the building envelope and a switch to a heat pump heating system. Additionally, an intelligent monitoring system was implemented to continuously collect data on environmental conditions and structural displacement. These data will be used to develop a predictive model for future maintenance needs, allowing for a preventative approach to conservation and minimizing resource consumption. Overall, this project serves as a model for integrating advanced technologies in historical building conservation, promoting sustainable practices, and ensuring the longevity of irreplaceable cultural landmarks. The key findings derived from this approach encompass a comprehensive assessment of the station’s condition, optimized conservation strategies, insights from HBIM modeling, and the ongoing benefits of the intelligent monitoring system. Field investigations revealed several areas of concern, such as structural cracks, material deterioration, moisture infiltration, and significant heat loss through the building envelope. This information was crucial for developing targeted conservation strategies. The use of internal thermal insulation systems, particularly capillary active mineral blocks, significantly improved thermal performance. Moisture management interventions, including the restoration of the rainwater drainage system and the application of moisture-proof insulation, reduced reliance on the municipal water supply. The intelligent monitoring system, with sensors measuring temperature, humidity, and structural displacement, plays a crucial role in ongoing conservation efforts. This system allows for continuous monitoring and the development of predictive models, ensuring targeted and preventative maintenance, reducing resource consumption, and extending the lifespan of the building.

This study investigates the impact of environmental conditions on the structural integrity and energy dynamics of historical masonry buildings using an IoT (Internet of Things) LoRaWAN-based (Long Range Wide Area Network) wireless sensor system. Over a six-month period, sensors were used to monitor wall temperature, wall humidity, air temperature, air humidity, crack width, and crack displacement. The data revealed significant correlations between environmental parameters and structural changes. Higher temperatures were associated with increased crack width, while elevated humidity levels correlated with greater crack displacement, showing the potential weakening of the masonry structure. Seasonal variations highlighted the cyclical nature of these changes, emphasizing the need for seasonal maintenance. Additionally, the findings suggest that managing temperature and humidity levels can optimize the building’s energy efficiency by reducing the need for additional heating or cooling. The use of LoRaWAN sensors provided real-time, remote monitoring capabilities, offering a cost-effective and scalable solution for preserving historical buildings. This study underscores the importance of continuous environmental and structural monitoring for the preservation of heritage sites. It also highlights the potential for integrating proactive maintenance strategies and energy optimization, ensuring long-term sustainability. By leveraging this IoT-based approach, this research contributes to the broader field of heritage conservation, offering a universal framework that can be applied to historical buildings worldwide, enhancing both their structural integrity and energy performance.

This study introduces GEPReS, a Geospatially Enabled Predictive Recommendation System designed to support the preventive management of historical buildings through short-horizon risk forecasting and context-aware decision support. The system integrates Geographic Information Systems (GISs), Internet of Things (IoT) sensor networks, and authoritative meteorological data to generate timely, actionable recommendations for conservation interventions. These may include pre-emptive shutter closure during heatwaves, activation of ventilation under elevated humidity, or intensified monitoring of structurally sensitive zones during heavy precipitation. By coupling historical datasets with real-time telemetry and calibrated predictive models, GEPReS addresses the distinctive vulnerabilities of heritage structures, which arise from material sensitivity, conservation constraints, and operational limitations under contemporary climatic conditions. The architecture combines spatial analysis, typology-aware risk assessment, and reproducible modelling practices to ensure interpretability and compliance with conservation principles. Designed for scalability and online implementation, the system provides a modular framework capable of adapting to diverse building typologies and resource environments. The paper details the system architecture, data sources, modelling approach, and implementation challenges, supported by empirical evidence from multi-site pilot deployments.

W artykule przedstawiono zastosowanie technologii druku 3D betonu i zbrojenia mieszanek włóknami konopnymi. Omówiono wymagania dotyczące betonów do druku 3D, właściwości włókien oraz ich wpływ na reologię i parametry mechaniczne mieszanek, a także wyniki badań mieszanek cementowych z dodatkiem włókien, uwzględniając wpływ współczynnika w/c na konsystencję i jakość wydruku. Połączenie druku 3D z naturalnymi włóknami wspiera zrównoważone budownictwo.