It is known that 3D printable concrete mixtures can be costly because they contain high dosages of binder and that the drying-shrinkage performance may be adversely affected. Mineral additives and fibers are generally used to control these negative aspects. In this study, the use of silica fume, a natural viscosity modifying admixture, was investigated to improve the rheological and thixotropic behavior of 3D printable concrete mixtures reinforced with polypropylene fiber (FR-3DPC). The effect of increasing the silica fume utilization ratio in FR-3DPC on the compressive strength (CS), flexural strength (FS), and drying-shrinkage (DS) performance of the mixtures was also examined. A total of five FR-3DPC mixtures were produced using silica fume at the rate of 3, 6, 9, and 12% of the cement weight, in addition to the control mixture without silica fume. As a result of the tests, the dynamic yield stress value decreased with the addition of 3% silica fume to the control mixture. However, it was found that the dynamic yield stress and apparent viscosity values of the mixtures increased with the addition of 6, 9, and 12% silica fume. With the increase in the use of silica fume, the CS values of the mixtures were generally affected positively, while the FS and DS behavior were affected negatively.

The production of cement, an essential material in civil engineering, requires a substantial energy input, with a significant portion of this energy consumed during the grinding stage. This study addresses the gap in the literature concerning the collective impact of key parameters, including ball size, feed rate, and mill speed, on grinding efficiency. Nine spherical balls, ranging from 15–65 mm, were utilized in six distinct distributions, alongside varying feed rates and mill speeds. ANOVA, Taguchi, and regression analyses were employed to explore their influence on grinding efficiency and cement properties. The findings revealed that ball size variation significantly affects grinding performance, with smaller diameter balls yielding higher efficiency due to increased abrasion and fine formation. Conversely, elevating mill speed generally diminishes grinding efficiency, particularly at speeds approaching 90% of the critical speed, impacting ball shoulder and foot angles. Moreover, increasing the feed rate affects the grinding performance differently based on ball distribution, with finer distributions experiencing adverse effects. Signal-to-noise ratios facilitated determining the optimal control factor levels to minimize energy consumption. Quadratic regression models exhibited strong predictive capabilities for energy consumption in grinding. Ultimately, the optimal grinding performance was achieved with Bond-type ball distribution No. 6, considering ball size, mill speed, and feed-rate interactions, albeit with considerations regarding grinding time and energy efficiency.

Self-compacting concrete (SCC) is a type of concrete that can be poured into complex geometries and dense reinforcement areas without the need for mechanical vibration, exhibiting excellent segregation resistance and flowability. Its adoption in the construction industry has surged in recent years due to its environmental, technical, and economic advantages, including reduced construction time and minimized occupational hazards. The performance of SCC is significantly influenced by the properties of the aggregates used. This study investigates the effects of variations in the coarse-to-fine aggregate ratio and water/binder (w/b) ratio on the fresh, hardened, and durability properties of SCC. A total of eight different SCC mixtures were prepared, utilizing two distinct s/b ratios and four varying fine-to-coarse aggregate ratios. The results indicated that increasing the s/b ratio enhanced fresh state performance but adversely affected mechanical strength and shrinkage behavior. Furthermore, the need for admixture and flow times improved with increasing coarse aggregate content, attributed to the reduction in cohesiveness and viscosity. However, this change did not significantly impact mechanical properties, while high-temperature resistance and shrinkage exhibited an upward trend.

It is known that the most critical factor affecting fire resistance requirements in buildings is the building envelope, and therefore, improving the thermal properties of facade materials has become an important research area. In this context, studies examining the high temperature resistance properties of various facade materials have indicated that concrete has higher heat resistance compared to many facade materials. Lightweight concrete facade elements stand out for their structural durability and fire resistance at high temperatures. However, the different thermal expansion coefficients of aggregates and cement paste in concrete mixtures can lead to adverse outcomes under high temperatures, such as cracking or structural degradation. To mitigate these adverse effects, it has been suggested that adding fibers to lightweight concrete mixtures could enhance durability and improve fire resistance. This study investigated the effects of different fiber types, lengths, and usage rates on the high temperature resistance of lightweight concrete mixtures. In the experimental study, three different types of fibers—polypropylene, polyamide, and glass—were used in varying proportions of 0%, 0.25%, 0.5%, and 0.75% of the total volume. Polypropylene fibers were included at lengths of 3, 6, and 12 mm; polyamide fibers at 6 and 12 mm; and glass fibers at 13 and 25 mm. When observing the behavior of the mixtures under high temperatures, it was noted that mixtures with glass fibers performed best at 300 °C, while those with polypropylene fibers showed superior performance at 600 °C. This demonstrates the advantages of glass and polypropylene fibers in providing resilience at different temperature ranges. Furthermore, the optimal fiber usage rate for high temperature resistance was determined to be 0.25%. These findings highlight the importance of considering factors such as fiber type, length, and usage rate in the development of fire-resistant facade materials.

In today’s environment, where energy is desired to be used more efficiently, it has been understood that the interest in the use of lightweight concrete with superior performance in terms of thermal insulation properties has increased. On the other hand, it has been stated that construction waste increases rapidly, especially after severe earthquakes. In this context, encouraging the use of recycled concrete waste and efficient disposal of construction and demolition waste is of great importance for the European Green Deal. It is also known that pollutants such as CO_x and NO_x stick to facades over time, causing environmental pollution and visual deterioration. It has been reported that materials with photocatalytic properties are used in lightweight concrete facade elements to prevent such problems. This study examines the effect of using recycled concrete aggregates on the thermal properties of self-cleaning lightweight concrete mixtures (SCLWC). For this purpose, an SCLWC containing 1% TiO₂ and 100% pumice aggregate was prepared. By replacing pumice aggregate with recycled concrete aggregate at the rates of 15%, 25%, 35%, 45% and 50%, four different SCLWCs with self-cleaning properties were produced. High-temperature resistance, thermal conductivity performance, microstructure analysis and photocatalytic properties of the produced mixtures were examined. It has been understood that the unit volume weight loss of SCLWC mixtures exposed to high temperatures generally decreases due to the increase in the recycled concrete-aggregate substitution rate. However, it was determined that the loss of compressive strength increased with the increase in the amount of recycled concrete-aggregate replacement. Additionally, it was determined that the thermal-conductivity coefficient values of the mixtures decreased with the use of pumice. After SCLWC mixtures were exposed to 900 °C, small round-shaped crystals formed instead of C–S–H crystals.

Accurate prediction of temperature-induced degradation in concrete is essential for improving structural fire safety and supporting reliable post-fire engineering decisions. However, previous studies have generally focused on conventional machine learning applications or limited optimization strategies, while integrated frameworks combining systematic input screening, robust validation, large-scale metaheuristic optimization, and interpretable analysis remain limited. This study aims to develop a comprehensive predictive framework for estimating the temperature-induced weight loss and compressive strength of concrete using advanced machine learning techniques. First, a detailed collinearity analysis was performed to filter the input dataset, eliminate redundant correlations, and improve statistical reliability. For modeling consistency, all fiber-containing mixtures were treated as polymer-fiber systems, and fiber-related variables were interpreted as polymer-fiber descriptors. To reduce overfitting and ensure robust validation, 5-fold cross-validation was applied during training, while 23% of the dataset was reserved as a strictly independent test set. In addition, 25 metaheuristic algorithms were evaluated under a standardized computational budget of 5000 function evaluations to perform neural architecture search. The results showed that the Marine Predators Algorithm (MPA), Symbiotic Organisms Search (SOS), and Kepler Optimization Algorithm (KOA) achieved superior convergence behavior in optimizing hybrid Levenberg–Marquardt-trained networks. SHapley Additive exPlanations (SHAP)-based sensitivity analysis further revealed that matrix-related properties, particularly unit weight and water absorption capacity, were the dominant drivers of thermal degradation. Overall, the proposed framework provides not only a robust benchmarking platform for predictive modeling but also a practically relevant and interpretable tool for post-fire structural assessment and thermally resilient concrete design.