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.

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 (K_eq-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%.

The construction of integral bridges is one of the most effective methods to reduce bridges’ construction and in-service costs. However, there are associated geotechnical problems with their abutments backfill due to the integrated abutments. The main goal of this study is to evaluate and quantify the benefits of geogrid reinforcement for reducing the backfill’s geotechnical problems. For this purpose, using small-scale physical modeling, the benefits of geogrid reinforcing of the backfill of an integral abutment bridge subjected to cyclic movements are evaluated. The results are then compared with a previous study performed on unreinforced backfill and two types of geocells. In this study, 120 loading cycles are applied to geogrid-reinforced soil to simulate the cyclic loadings on integral abutment backfill due to seasonal abutment displacement. The horizontal reaction load at the top of the wall, changes in pressure behind the wall, and deformation in backfill soil are measured during the test. Then the results are discussed in terms of equivalent peak lateral soil coefficient (K_peak), lateral earth pressure coefficient (K*), and normalized settlement behind the wall (S_g/H). The derived lateral soil coefficients and settlement behind the abutment show that geogrid substantially reduces pressure and settlements after 120 cyclic loads. Based on the results, K_peak and K* of the geogrid-reinforced backfill decrease by up to 36%, and S_g/H behind the wall decreases by 62%. In addition, the comparison of the results for geogrid with two geocell types shows that geogrid is more efficient in terms of lateral soil coefficients.

One of the most effective ways to increase the longevity of pavement structures is through the integration of geosynthetic reinforcement. Geosynthetics are synthetic materials such as geotextiles, geogrids, or geocomposites that are added to the interface between the subgrade and the base layer of a pavement structure. To evaluate the effect of various parameters on the structural benefits of geosynthetic reinforcement on the pavement structure of low-volume traffic flexible pavements, a finite element (FE) study was performed using the ABAQUS program. These parameters included the geosynthetic type, geosynthetic tensile stiffness, subgrade stiffness, and base thickness. The FE rutting curves for the 100 cycles were calibrated using the mechanistic–empirical (M-E) transfer functions, which were then used to calculate the long-term rutting curves. The traffic benefit ratio (TBR) was initially calculated based on the calibrated rutting curves for each pavement layer. The calculated TBRs were then used as an input in AASHTOWare to compute the base effective resilient modulus (M_R-eff) and the factor of base course reduction (BCR). The results showed that adding one layer of geosynthetics enhanced the rutting performance of pavement structures significantly (up to 8.9 in TBR, 322% in M_R-eff, and 64% in BCR). Geogrids showed higher benefits than geotextiles due to the interlocking between base aggregates and geogrid aperture. The values of TBR, M_R-eff, and BCR increase with the increasing tensile stiffness of the geosynthetics and the rutting target and with the decreasing subgrade stiffness. The results also demonstrated peak values of TBR, M_R-eff, and BCR for a base thickness of 25.4 cm.