Considering the remarkable characteristics of nanomaterials, previous research studies investigated the effects of incorporating different types of these materials on improving the concrete properties. However, further studies are required to evaluate the complementary hybridization and synergistic influence of nanomaterials. In this research, the combined effect of adding nano silica particles (NS) and multi-walled carbon nanotubes (MWCNT) on enhancing both the compressive and flexural strengths of the cement paste was investigated. Moreover, the morphology of the interface between cement paste and aggregates was studied by scanning electron microscopy (SEM). The mixtures were prepared using three different portions of MWCNT and NS. Electron microscopy images indicated a uniform distribution of nanoparticles in the cement matrix, enhanced hydration reactions, and increased density. Based on the experiments’ outcomes, the combined utilization of silica and carbon nanomaterials in the cement paste did not necessarily result in the maximum compressive and flexural strengths. Furthermore, it was observed that the use of higher percentages of pristine NS in the absence of MWCNT can lead to further enhancement of strength properties of the cement paste.

The primary objective of this study is to investigate the benefits of adding tire rubber as an inclusion to backfill behind integral bridge abutments. In this respect, four physical model tests that enable cyclic loading of the backfill-abutment are conducted and evaluated. Each test consisted of 120 load cycles, and both the horizontal force applied to the top of the abutment wall and the pressures along the wall-backfill interface is measured. The primary variable in this study is the tire rubber content in the backfill soil behind the abutment. Results show adding tire rubber to the backfill would be beneficial for both pressure and settlement behind the abutment. According to results, adding tire rubber to soil decreases the equivalent peak lateral soil coefficient (Keq-peak) up to 55% and earth pressure coefficient ($${K}^{*}$$ K ∗ ) at upper parts of the abutment up to 59%. Moreover, the settlements of the soil behind the wall are decreased up to 60%.

No abstract

The performance of both non-spliced and spliced steel bars significantly affects the overall performance of structural reinforced concrete elements. In this context, the mechanical properties of reinforcement bars (i.e., their ultimate strength and strain) should be determined in order to evaluate their reliability prior to the construction procedure. In this study, the application of Tree-Based machine learning techniques is implemented to analyze the ultimate strain of non-spliced and spliced steel reinforcements. In this regard, a database containing the results of 225 experimental tests was collected based on the research investigations available in peer-reviewed international publications. The database included the mechanical properties of both non-spliced and mechanically spliced bars. For better accuracy, the databases of other splicing methods such as lap and welded-spliced methods were excluded from this research. The database was categorized as two sub-databases: training (85%) and testing (15%) of the developed models. Various effective parameters such as splice technique, steel grade of the bar, diameter of the steel bar, coupler geometry—including length and outer diameter along with the testing temperatures—were defined as the input variables for analyzing the ultimate strain using tree-based approaches including Decision Trees and Random Forest. The predicted outcomes were compared to the actual values and the precision of the prediction models was assessed via performance metrics, along with a Taylor diagram. Based on the reported results, the reliability of the proposed ML-based methods was acceptable (with an R2 ≥ 85%) and they were time-saving and cost-effective compared to more complicated, time-consuming, and expensive experimental examinations. More importantly, the models proposed in this study can be further considered as a part of a comprehensive prediction model for estimating the stress-strain behavior of steel bars.

Flame retardant (FR) additives may degrade polymers’ mechanical performance. In this work, FR epoxy composites fabricated based on two popular FR agents of ammonium polyphosphate (APP) and silica aerogel (SAG) are investigated. Several mechanical properties of these composites, including compressive, micro-hardness, and Izod impact, were investigated for different filler loadings. Although the addition of 10 vol.% APP improved compressive modulus, yield strength, and micro-hardness, it degraded the impact strength. The incorporation of SAG made the composites more ductile, improved the impact strength, but deteriorated their compressive properties. Samples containing both SAG and APP demonstrated synergetic effects evident by their enhanced compressive properties and hardness. The findings of this study can guide the design of epoxies with both exceptional FR and mechanical performance.