Seweryn Malazdrewicz

Copper slag is generated in large quantities during pyrometallurgical processes and its properties may vary depending on the cooling route and production conditions. Understanding this variability is important for assessing the potential for consistent and reliable valorisation of this industrial by-product. This study provides a multi-batch physicochemical characterisation of two forms of copper slag: granulated (GR) and air-cooled/lumpy (ZL). Multiple production batches were investigated to assess differences between the two cooling routes and the repeatability of material properties within each slag type. The examined properties include particle density, particle size distribution, water absorption, porosity, chemical composition determined by EDXRF, chloride, sulphate and total sulphur contents, as well as selected mechanical and particle-shape characteristics. The results showed clear differences between GR and ZL in particle size distribution and particle density, reflecting their distinct cooling histories. Most investigated chemical and physical properties exhibited relatively limited variability between production batches, indicating a generally consistent production process. However, greater batch-to-batch variability was observed for selected parameters, particularly sulphate content, water absorption and particle size distribution, with isolated deviations also identified in chemical composition. The resulting dataset provides a baseline for subsequent investigations into the processing and CO2 mineralisation of copper slag and supports the development of more consistent and value-added reuse pathways for this industrial by-product.

Large Panel System (LPS) buildings constructed before 1990 constitute a significant portion of the national housing stock in many Central and Eastern European countries. While these buildings often exhibit flaws such as imperfect prefab dimensions and unstable construction joints, their concrete panels remain in satisfactory condition. The ongoing debate about whether to modernize or demolish these structures mirrors the trend in Western Europe, where LPS buildings have largely been dismantled—a trend likely to extend to Central and Eastern Europe. Despite this, little research has been conducted on recycling concrete panels from LPS demolitions. To mitigate future concrete waste accumulation, this study evaluates the properties of Recycled Coarse Aggregate (RCA) obtained from LPS demolition and assesses its potential use in concrete. The proposed RCA is compared to similar recycled coarse aggregate sources. Results indicate that LPS-derived RCA differs in several key aspects: it has lower density (2.24–2.47% compared to literature averages), lower water absorption (3.81–4.58%), lower mortar content (12.57–40.82%), and lower abrasion resistance (16.39–31.3 MDE). Due to its high water absorption and favorable grain shape (1.22 SF vs. 0.83 SF literature average), LPS-derived RCA is particularly suitable for concrete mixes with high water demand, offering potential benefits for fresh concrete properties.

Self-compacting concrete (SCC), which can be placed and consolidated under its weight without any vibration effort, was first developed in 1988 to achieve durable concrete structures. Since then, a lot of research has been conducted in order to reduce the consumption of raw materials in SCC. Although coarse aggregate and design methods are known to influence concrete properties, there are still knowledge gaps that need to be filled. This study uses a new source of recycled coarse aggregate (RCA), namely the concrete panels of old large panel system buildings demolished in urban areas. The aim of the study is to verify the effect of this aggregate on the rheological and mechanical properties of SCC. To enhance the properties of SCC, the modified equivalent mortar volume (mEMV) method of designing a concrete mix was used. It has not been verified before for SCC, and a literature review suggests it has the potential to eliminate the drawbacks of ordinary equivalent mortar volume methods. The SCC that was used in the study is characterized by having a high viscosity and an average consistency. The hardened SCC used in the research was highly impacted by its mix design and RCA content. Both compressive strength and abrasion resistance decreased when the RCA content increased. However, using the mEMV method resulted in a 10 and 1% improvement in compressive strength and abrasion resistance, respectively.

This paper predicts the abrasion resistance of a cementitious composite containing granite powder and fly ash replacing up to 30% of the cement weight. For this purpose, intelligent artificial neural network (ANN) models were used and compared. A database was built based on mix composition, curing time, and curing method. The model developed to predict the abrasion resistance of the cementitious composites containing granite powder and fly ash was shown to be accurate. It was proved by the very high values of the accuracy parameters that were above 0.93 in the case of the coefficient of the determination R² and very low values of the errors, which were about 10% in the case of mean average percentage error. This method can be used especially for designing cement mortars with granite powder and fly ash additives replacing cement in a range from 0 to 30% of its weight. These mortars can be used for floors in industrial buildings.

Large panel system (LPS) buildings, during the 1950s–1990s, were the most widespread among precast systems and are still a huge part of some national housing stocks. Disadvantages in characteristics, poor quality of all construction stages, sudden disasters and poor retrofit and repair techniques can result in demolishing some LPS buildings and thus the creation of huge amounts of waste. There is urgent need of evaluating the possibility of reusing the elements of LPS buildings. The novelty of the article is based on the formulation of research gaps in terms of recycling these buildings and the justification of such need. The authors reviewed the current state of knowledge regarding characteristics of LPS buildings, directions of retrofitting, durability and their locations within cities. The possibility of recycling elements of LPS buildings was analysed. The findings are that concrete load bearing panels, proved to be in satisfactory conditions in terms of strength and durability, have the possibility to be recycled into recycled coarse aggregate (RCA), so such waste could be beneficial for concrete. However, there are research gaps of such an application highlighted in the text. The authors suggest that conducting comprehensive basic research in this area could have significant benefits for the next generation of researchers and engineers.

Fibre-reinforced polymer materials (FRP) are increasingly used to reinforce structural elements. Due to this, it is possible to increase the load-bearing capacity of polymer, wooden, concrete, and metal structures. In this article, the authors collected all the crucial aspects that influence the behaviour of concrete elements reinforced with FRP. The main types of FRP, their characterization, and their impact on the load-carrying capacity of a composite structure are discussed. The most significant aspects, such as type, number of FRP layers including fibre orientation, type of matrix, reinforcement of concrete columns, preparation of a concrete surface, fire-resistance aspects, recommended conditions for the lamination process, FRP laying methods, and design aspects were considered. Attention and special emphasis were focused on the description of the current research results related to various types of concrete reinforced with FRP composites. To understand which aspects should be taken into account when designing concrete reinforcement with composite materials, the main guidelines are presented in tabular form.

To avoid time-consuming, costly, and laborious experimental tests that require skilled personnel, an effort has been made to formulate the depth of wear of fly-ash concrete using a comparative study of machine learning techniques, namely random forest regression (RFR) and gene expression programming (GEP). A widespread database comprising 216 experimental records was constructed from available research. The database includes depth of wear as a response parameter and nine different explanatory variables, i.e., cement content, fly ash, water content, fine and coarse aggregate, plasticizer, air-entraining agent, age of concrete, and time of testing. The performance of the models was judged via statistical metrics. The GEP model gives better performance with R² and ρ equals 0.9667 and 0.0501 respectively and meet with the external validation criterion suggested in the previous literature. The k-fold cross-validation also verifies the accurateness of the model by evaluating R², RSE, MAE, and RMSE. The sensitivity analysis of GEP equation indicated that the time of testing is the influential parameter. The results of this research can help the designers, practitioners, and researchers to quickly estimate the depth of wear of fly-ash concrete thus shortening its ecological susceptibilities that push to sustainable and faster construction from the viewpoint of environmentally friendly waste management.

Artificial intelligence and machine learning are employed in creating functions for the prediction of self-compacting concrete (SCC) strength based on input variables proportion as cement replacement. SCC incorporating waste material has been used in learning approaches. Artificial neural network (ANN) support vector machine (SVM) and gene expression programming (GEP) consisting of 300 datasets have been utilized in the model to foresee the mechanical property of SCC. Data used in modeling consist of several input parameters such as cement, water–binder ratio, coarse aggregate, fine aggregate, and fly ash (FA) in combination with the superplasticizer. The best predictive models were selected based on the coefficient of determination (R²) results and model validation. Empirical relation with mathematical expression has been proposed using ANN, SVM, and GEP. The efficiency of the models is assessed by permutation features importance, statistical analysis, and comparison between regression models. The results reveal that the proposed machine learning models achieved adamant accuracy and has elucidated performance in the prediction aspect.