Technical University of Civil Engineering of Bucharest

The monitoring and conservation of built heritage is a major challenge for the scientific community, given the continuous degradation caused by natural, anthropogenic and climatic factors. The generation of high-resolution 3D documentation is important in the diagnosis of deterioration in historic buildings and the planning of conservation and restoration efforts. The present study proposes an integrated, multi-source workflow combining terrestrial laser scanning (TLS), unmanned aerial vehicle (UAV) photogrammetry, and 3D camera interior scanning. This workflow was employed to document and evaluate the Casa Rusănescu monument in Craiova, Romania. The following processes were incorporated: coordinated acquisition, processing, alignment, evaluation of geometric consistency and deviation-based diagnosis. The diagnosis process include measuring the distance between data clouds and analyzing surface roughness, curvature, planarity and linearity. The workflow was designed to be applicable in real urban conditions, ensuring the coverage of façades, interiors and roof structures. The final, combined dataset contained over 235 million points and includes both interior and exterior geometries. This process helped identify various types of damage, such as cracks, exfoliation, plaster detachment, moisture-related changes, and geometric deformations. An additional AI-assisted validation step (Twinspect) was used to cross-check the degradation indicators derived from point-cloud analyses. The findings suggest that using multiple sensors improves spatial completeness, enhances anomaly detection, and establishes a reliable baseline prior to restoration interventions and long-term monitoring. This methodology facilitates the development of digital twins and GIS-based risk assessments, thereby providing a scalable solution for heritage preservation.

Peri-urban areas in Eastern Europe are undergoing rapid land transformation driven by suburban housing expansion and infrastructure development, yet the processes through which vegetation is progressively degraded and built-up areas intensify remain insufficiently documented. This study analyses vegetation loss and urban expansion in the peri-urban belt of Timișoara, Western Romania, between 2020 and 2025 using Sentinel-2 multispectral imagery, two key spectral indices (NDVI and NDBI), and a Random Forest (RF) classifier. The results reveal a gradual, multi-stage transformation trajectory, where dense vegetation transitions first into sparse vegetation and bare soil before consolidating into built-up surfaces, rather than being replaced abruptly. Substantial vegetation decline is accompanied by notable increases in built-up land, with strong spatial differences between communes depending on development pressure. The integration of RF classification with spectral index analysis allows these transitions to be validated and interpreted more reliably, helping distinguish structural suburbanisation from short-term spectral variability. Overall, the study demonstrates the value of combining NDVI, NDBI and AI-supported land-cover classification to capture nuanced peri-urban transformation dynamics and provides actionable insights for spatial planning and sustainable land management in rapidly growing metropolitan regions.

In this article we started from the global warming trends due to climate change. These are leading to an increase in the heat risk index, particularly in areas where green infrastructure is lacking, where the population is dense and multi-storey residential buildings are dense. This is the reason why we first proposed a bibliometric study resulting from the growing interest in such analyses using GIS technology. The next step was the development of an automatic model based on specific features and its application to the Romanian capital, Bucharest, in order to identify areas in need of green infrastructure improvement, at the sector level and then at the neighborhood level. Three variables that determine the heat risk index were used: average summer surface temperature, percentage of area without tree cover and population density. The results highlight the sectors and neighborhoods of Bucharest where regeneration actions are needed. Concluding digital representations are made, related tree canopy and heat risk index for neighborhoods in Bucharest, as well as heat risk index map for prioritizing green infrastructure. This study is a good starting point for the substantiation of green infrastructure improvement policies, but also for studies to be developed at a more detailed level in the city analysis.

Purpose This study aims to develop and validate a reliable and psychometrically robust Short Form of the Remote Work Stress Scale (SRWSS), derived from the original 15-item RWSS. Design/methodology/approach Following the three-phase, nine-step scale development framework by Boateng et al. (2018), the study involved qualitative item generation, expert reviews, pilot study, exploratory and confirmatory factor analyses, and validation with a sample of 602 remote workers from Türkiye. The short form was tested for reliability, validity and path analysis performed via SmartPLS with the original form and its sub-dimensions. Findings The SRWSS demonstrated excellent internal consistency (a = 0.88), strong construct and criterion validity, and a high correlation with the original scale (0.94). Results showed that female, highly educated and non-managerial employees reported higher levels of stress. Age was negatively correlated with stress levels. Research limitations/implications The sample is confined to a specific demographic (Turkish remote workers), necessitating cross-cultural validation to ensure generalizability. Practical implications The scale enables quick stress assessments to inform targeted interventions and HR strategies in remote and hybrid work settings. Social implications Findings highlight demographic disparities in remote work stress, informing equitable workplace policy development. Originality/value This study pioneers the development of a validated, unidimensional 5-item scale, offering a novel, efficient instrument for assessing remote work stress, enhancing research and practice in HRM.

Abstract The goal of this work is to numerically assess the performance of a horizontal drain water heat recovery unit, proposed for small shower trays. The analysis is fully based on Computational Fluid Dynamics (CFD) modelling. The simulations have been carried out both for unbalanced and balanced configurations in terms of wastewater flow rate and cold-water flow rate. The numerical results indicate that the effectiveness of the horizontal drain water heat recovery device is between 15-32%. These values are similar to those from the literature for comparable configurations of horizontal drain water heat recovery systems.

This study presents a low-cost irrigation prototype developed on an Arduino platform, integrating soil moisture sensors and a fuzzy logic controller. The system was tested under contrasting weather scenarios to evaluate its capacity to stabilize soil water content. Results showed clear differences between cloudy and sunny conditions, with the latter leading to accelerated drying and reduced infiltration. Soil sensor data confirmed distinct spatial patterns of moisture distribution, while statistical analysis revealed a strong negative correlation between soil moisture and irrigation volume. The fuzzy algorithm responded proportionally to water deficits, applying larger volumes during recovery phases and frequent low-volume cycles for stabilization. These findings demonstrate the potential of fuzzy logic-based irrigation to enhance water efficiency and maintain soil moisture balance under variable environmental stresses.

To increase the adoption of recycled plastics in the construction industry and to make this material economically viable, it is of outmost importance to develop technological processes that can make contaminated or dirty plastics useful. This study applies an Euler-Lagrange numerical approach to evaluate the suitability of injection moulds for optimized structural elements made from contaminated recycled polyethylene and to check if the mould shapes are compatible with a normal injection technological process. The viscosity of the material is strongly related to the injection temperature, and as such, a temperature dependent formulation was considered in the numerical model. Furthermore, by simulating the injection moulding process with varying levels of contamination and temperature gradients, researchers can eventually establish a database of material properties and predict the structural performance of the final products using machine-learning methods. This data-driven approach is crucial for establishing quality control measures and building confidence in the use of recycled polyethylene for load-bearing applications. This type of modelling will enable designers to account for structural defects in the material due to the injection process and the nature of the contaminates present in the recycled polyethylene. Also, modelling the process in this manner may allow for developing new topological optimization rules and objective functions aimed at enhancing the performance of recycled polyethylene components, ultimately driving more sustainable practices in the construction industry.

This article focuses on the research and advancement of an optimal system for the automatic regulation of the water level in a reservoir to eliminate flooding in the area where it is located. For example, in this article, the regulation of the level in the Mariselu Reservoir from the dam in Bistrita–Nasaud County, Romania, was considered as a practical application. Industrial PID controller tuning provides robust and stable solutions; however, the controller parameters may require frequent tuning owing to uncertainties and changes in operating conditions. Considering this inconvenience, an adaptive adjustment of the PID controller parameters is necessary, combining various parameter optimization methods, namely reinforcement learning and Particle Swarm Optimization. A new optimization method was developed that uses a mathematical equation to guide the Particle Swarm Optimization method, which in essence enhances the fitness function of reinforcement learning, thus obtaining a control system that combines the advantages of the two methods and minimizes their disadvantages. The method was tested by simulation using MATLAB and Python, obtaining very good results, after which it was implemented, which successfully prevented floods in the area where it was placed. This optimal automation system for dams should be implemented and adapted for several dams in Romania

Understanding long-term precipitation variability is essential for assessing the climate’s impact on sensitive ecosystems, particularly in regions of high environmental value, such as the Danube Delta Biosphere Reserve (DDBR). This study examines the temporal dynamics of monthly precipitation in the Danube Delta, Romania, spanning the period from 1965 to 2019. Three approaches were used to analyze climatic variability: Change Point detection (CPD) to identify shifts in precipitation regimes, the De Martonne Index (IM) to assess aridity trends, and the Standardized Precipitation Index (SPI) to evaluate drought conditions across annual and monthly scales. Using robust monthly precipitation and temperature datasets from the Sulina meteorological station, CPD analysis revealed statistically significant structural breaks in precipitation trends, suggesting periods of altered climate behavior likely associated with broader regional or global climate changes. IM values indicated mostly hyper-aridity and aridity at monthly and annual scales, respectively. No monotonic trend was found in this index during the analyzed segments, as emphasized by the Mann–Kendall (MK) test. SPI values provided further evidence of variability in the precipitation regime, highlighting a transition toward more extreme hydrological conditions in the region. The combined use of these indices offers a comprehensive view of the evolution of climatic conditions in the Danube Delta. The findings underscore the growing vulnerability of this unique wetland ecosystem to climatic variability, supporting the need for adaptive water management strategies in the face of anticipated future changes.