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.

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.

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.

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.