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.

Structures located on the coast are subjected to the long-term influence of chloride ions, which cause the corrosion of steel reinforcements in concrete elements. This corrosion severely affects the performance of the elements and may shorten the lifespan of an entire structure. Even though experimental activities in laboratories might be a solution, they may also be problematic due to time and costs. Thus, the application of individual machine learning (ML) techniques has been investigated to predict surface chloride concentrations (C_c) in marine structures. For this purpose, the values of C_c in tidal, splash, and submerged zones were collected from an extensive literature survey and incorporated into the article. Gene expression programming (GEP), the decision tree (DT), and an artificial neural network (ANN) were used to predict the surface chloride concentrations, and the most accurate algorithm was then selected. The GEP model was the most accurate when compared to ANN and DT, which was confirmed by the high accuracy level of the K-fold cross-validation and linear correlation coefficient (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) parameters. As is shown in the article, the proposed method is an effective and accurate way to predict the surface chloride concentration without the inconveniences of laboratory tests.

Marble is currently a commonly used material in the building industry, and environmental degradation is an inevitable consequence of its use. Marble waste occurs during the exploitation of deposits using shooting technologies. The obtained elements most mainly often have an irregular geometry and small dimensions, which excludes their use in the stone industry. There is no systematic way of disposing of these massive mounds of waste, which results in the occurrence of landfills and environmental pollution. To mitigate this problem, an effort was made to incorporate waste marble powder into clay bricks. Different percentage proportions of marble powder were considered as a partial substitute for clay, i.e., 5–30%. A total of 105 samples were prepared in order to assess the performance of the prepared marble clay bricks, i.e., their water absorption, bulk density, apparent porosity, salt resistance, and compressive strength. The obtained bricks were 1.3–19.9% lighter than conventional bricks. The bricks with the addition of 5–20% of marble powder had an adequate compressive strength with regards to the values required by international standards. Their compressive strength and bulk density decreased, while their water absorption capacity and porosity improved with an increased content of marble powder. The obtained empirical equations showed good agreement with the experimental results. The use of waste marble powder in the construction industry not only lowers project costs, but also reduces the likelihood of soil erosion and water contamination. This can be seen to be a crucial factor for economic growth in agricultural production.

The thermo-mechanical behavior of polyurethane adhesive joints in wood structures is a crucial aspect that needs to be understood to ensure the durability and safety of timber structures, especially in seismic regions. As mass timber, particularly cross-laminated timber, continues to gain popularity as a building material, it is important to pay special attention to the behavior of connections between the timber elements. The use of flexible polyurethane adhesives presents a promising alternative to conventional mechanical connections in seismic-resistant timber structures. This research highlights the potential of polyurethane-based joints at elevated service temperature, offers a promising alternative to traditional wood joints, and suggests viability for post-fire restoration of wood structures. The response at the interface between wood and polyurethane under flexural stresses is also evaluated, underscoring the broader application possibilities of flexible adhesives in wood construction for mechanical and physical improvements.

Composite materials are increasingly used to strengthen existing structures or new load-bearing elements, also made of timber. In this paper, the effect of the number of layers of Carbon Fiber Reinforced Polymer (CFRP) on the load-bearing capacity and stiffness of Glued Laminated Timber beams was determined. Experimental research was performed on 32 elements—a series of eight unreinforced beams, and three series of eight reinforced beams: with one, three and five layers of laminate each. The beams with a cross-section of 38 mm × 80 mm and a length of 750 mm were subjected to the four-point bending test according to standard procedure. For each series, destructive force, deflection, mode of failure, and equivalent stiffness were determined. In addition, for the selected samples, X-ray computed tomography was performed before and after their destruction to define the quality of the interface between wood and composite. The results of the conducted tests and analyses showed that there was no clear relationship between the number of reinforcement layers and the load-bearing capacity of the beams and their stiffness. Unreinforced beams failed due to tension, while reinforced CFRP beams failed due to shear. Despite this, a higher energy of failure of composite-reinforced elements was demonstrated in relation to the reference beams.

Wood characterized by desired mechanical properties and wood joining material is essential for creating wooden structures. The polymer adhesives are suitable for such applications due to the possibility of energy dissipation from stresses generated by wooden structures and the elimination of thermal bridging, which are common problems in metal joining materials. This research focuses on the thermophysical properties of the laboratory-prepared flexible and rigid polyurethanes to select an appropriate polymer adhesive. Our results showed that the highest thermal stability was in the case of the new PSTF-S adhesive, which reached 230 °C, but the lowest mass loss in the air environment was around 54% for the PS material. The mean thermal expansion coefficient for F&R PU adhesives was 124–164∙10⁻⁶ K⁻¹. The thermal diffusivity of examined adhesives varied between 0.100 and 0.180 mm²s⁻¹. The thermal conductivity, depending on the type of polyurethane, was in the 0.13–0.29 W∙m⁻¹∙K⁻¹ range. The relative decrease in thermal diffusivity after heating the adhesives to 150 °C was from 2% for materials with the lowest diffusivity to 23% for the PU with the highest value of heat transfer. It was found that such data can be used to simulate wooden construction joints in future research.