Abstract This study investigates the use of machine learning algorithms to predict the plastic rotational capacity of reinforced concrete elements, a critical factor in assessing the response and vulnerability of structures under seismic loads. Traditional mathematical models developed to evaluate the rotational capacity of concrete elements are insufficient for accurately capturing the complex nonlinear behavior of structures under seismic loads. Machine learning offers a promising alternative, given its ability to model nonlinear relationships and improve predictive accuracy based on diverse datasets. The study compares the performance of six machine learning algorithms - Linear Regression (LR), Support Vector Machine for Regression (SVR), Least Squares Support Vector Machine (LS-SVM), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) - using datasets of reinforced concrete columns and walls. Results reveal that SVR and XGBoost provide the most accurate predictions for columns, with R² values up to 91%, while XGBoost performs best for walls with a maximum R² of 78%. However, the accuracy of these models is limited by the availability and diversity of the datasets, particularly for elements prone to shear failure. This research underscores the potential of machine learning in seismic design and structural assessment, though it also highlights the need for more comprehensive data to enhance model accuracy.

Abstract The use of wood as building material is considered a viable alternative to other materials in the increased seek of means to fight against the severe negative impact of construction sector on the environment. The study program aims to performing a series of adherence tests on 6 different specimen types composed by glued laminated timber elements (glulam) joined together with different polymeric materials (FRP - fiber reinforced polymer) by means of a series of different adhesives. The goal of the study is to find the composition of elements (wood, adhesive and FRP) for optimum specimens from both structural and economic point of view. Furthermore, a detailed analysis has been made on how to implement these new materials in the nowadays construction sector.

Abstract Pluvial floods are frequent phenomena in Romania occurring during torrential rainfall, causing significant damage. Pluvial floods occur due to extreme rainfall when the natural infiltration of excess water into the ground is artificially reduced and when runoff into urban areas exceeds the capacity of existing drainage systems. In urban areas, pluvial flooding is more frequent due to extended impermeable areas, buildings, and undersized drainage systems. Tulcea municipality is characterized by periodic rainfall flooding events with consequences on properties and communities, infrastructure, and on the economic activities. In the second cycle of implementation of the Floods Directive 2007/60/EC, 17 Areas of Potential Significant Flood Risk (APFSR) pluvial source areas were identified, covering 19 municipalities and towns, for which pluvial hydraulic models were carried out. Tulcea municipality was one of the cities designated as having areas of potential significant pluvial flood risk. Hydraulic modelling results have been used for the construction of pluvial flood hazard and risk maps, these being the first pluvial maps reported by Romania to the European Commission. Based on these maps, critical drainage areas have been identified at the level of Tulcea Municipality and some potential flood risk reduction measures have been recommended.

Abstract The cumulated effects of climate changes and accelerated urban development, amplified by insufficient funding and/or capacity to implement proactive methods and strategies for monitoring and optimization of water supply systems negatively impact the operation and overall efficiency of these systems, generating considerable problems regarding public comfort and safety and also service costs. Out of all problems, the most tangible issues for the final user are the ones related to the distribution network – the closest system component. In order to address these problems, an integrated analysis of the conditions of each urban area is required, accounting for actual water demand, pressure requirements, network configuration, power consumption and development forecasts. This article presents the set of measures proposed for optimisation of the distribution network of Babadag Town from Romania, consisting of changes intended to improve the hydraulic capacity of the network and also on reducing the water loss based on water balance evaluation and hydraulic modelling of existing and proposed network configurations.

Abstract The full expansion of the general formula, which calculates the money price of a forward contract at the end of the prearranged period T, makes us tangibly understand the relationship between time and money function {M(oney)=f [t(ime)]} in determining the purchase price/cost of a forward commodity. Similarly, in the case of the production process of construction, it has been shown that there is a time-money function {M(oney) = f [t(ime)]}, which determines the purchase price/cost of a technical commodity. The purpose of this paper is to introduce the possibility of studying and interpreting of the dynamic variations of the parameters that shape the final monetary exchange value of any construction as demonstrated by the time-money relationship and determined by research and financial management.