International Concrete Abstracts Portal

This study employs machine learning (ML) to predict ultrasonic pulse velocity (UPV) based on the mix composition and curing conditions of concrete. A dataset was compiled using 1495 experimental tests. Extreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) were applied to predict UPV in both direct and surface transmissions. The Monte Carlo approach was used to assess model performance under input fluctuations. Feature importance analyses, including the Shapley Additive Explanation (SHAP), were conducted to evaluate the influence of input variables on wave propagation velocity in concrete. Based on the results, XGBoost outperformed SVR in predicting both direct and surface UPV. The accuracy of the XGBoost model was reflected in average R² values of 0.8724 and 0.9088 for direct and surface UPV, respectively. For the SVR algorithm, R² values were 0.8362 and 0.8465 for direct and surface UPV, respectively. In contrast, linear regression exhibited poor performance, with average R² values of 0.6856 and 0.6801 for direct and surface UPV. Among the input features, curing pressure had the greatest impact on UPV, followed by cement content. Water content and concrete age also demonstrated high importance. In contrast, sulfite in fine aggregates and the type of coarse aggregates were the least influential variables. Overall, the findings indicate that ML approaches can reliably predict UPV in healthy concrete, offering a useful step toward more precise health monitoring through the detection of UPV deviations caused by potential damage.

This study investigates the feasibility of utilizing a hybrid combination of recycled steel fibers (RSF) obtained from scrap tires and manufactured steel fibers (MSF) in concrete developed for pavement overlay applications. A total of five concrete mixtures with different combinations of MSF and RSF, along with a reference concrete mixture, were studied to evaluate fresh and mechanical properties. The experimental findings demonstrate that the concretes incorporating a hybrid combination of RSF with hooked-end MSF exhibit comparable or higher splitting tensile strength, flexural strength, and residual flexural strength to that of concretes containing only hooked-end MSF, straight MSF, and RSF. This enhanced mechanical performance can be ascribed to the multiscale fiber reinforcement effect that controls different scales (micro to macro) of cracking, thereby providing higher resistance to crack propagation. The concretes containing only RSF show lower splitting tensile strength, flexural strength, and residual flexural strength compared to concrete solely reinforced with straight MSF or other steel fiber-reinforced concrete (SFRC) mixtures due to the presence of various impurities in the RSF, such as thick steel wires, residual rubber, and tire textiles. Interestingly, blending RSF with hooked-end MSF overcomes these limitations, enhancing tensile strength, flexural strength, and residual flexural strength, while significantly reducing costs and promoting sustainability. Lastly, the findings from the pavement overlay design suggest that utilizing a hybrid combination of RSF with hooked-end MSF can reduce the design thickness of bonded concrete overlays by 50% compared to plain concrete without fiber reinforcement, making it a practical and efficient solution.

Ultra-high-performance geopolymer concrete (UHP-GPC) can exhibit high to exceptional strength. Given the importance of UHP-GPC’s mechanical properties, the prediction of its 28d compressive strength (f’_c) remains insufficiently explored. This study predicts UHP-GPC’s f’_c based on alkali-activated materials, sand, fiber volume, water-to-geopolymer binder, and alkali activator ratios. Advanced statistical modeling and a spectrum of ensemble machine learning (ML) algorithms, including random forest (RF), gradient boosting (GB), extreme gradient boosting (XGB), and stacking, are utilized to predict UHP-GPC’s strength. The derived models reveal the significance of fiber, slag, and sand as the most significant factors influencing the 28d f’_c of UHP-GPC. All the ML models demonstrate higher precision in forecasting f’_c of UHP-GPC compared to statistical modeling, with R²s peaking at 0.85. Equations are derived to predict the strength of UHP-GPC. This article reveals that UHP-GPC with superior mechanical properties can be designed for further sustainability.

Corrosion of prestressing steel can threaten the durability of prestressed concrete. To ensure the durability of unbonded post-tensioning (PT) systems, it is crucial to investigate the effects of construction defects such as grease leakage and high-density polyethylene (HDPE) sheath damage. This study quantified the thickness of grease coating (PT-coating) and HDPE sheath damage as experimental variables. An accelerated corrosion test was conducted in two environments: 1) chloride ions only (Cl-) and 2) both chloride ions and dissolved oxygen (Cl- + DO). The corrosion current density and weight loss of prestressing strands and the suspended concentration density of corrosion cell solution were measured to quantify the corrosion performance. Increasing the grease coating thickness over 0.3 mm (0.012 in.) did not significantly enhance corrosion resistance. Realistic levels of HDPE sheath damage had no significant detrimental effects on durability; however, excessive HDPE sheath area loss must be avoided for long-term durability. It was examined to quantify the interrelationship between three data: electrochemical measurement, weight loss, and suspended concentration density as quantitative corrosion data. The findings of this study can serve as a basis for developing durability-related provisions, as well as controlling the construction defects of unbonded PT systems in field applications.

Prefabricated structural wall buildings exhibit superior strength, stiffness, and ductility under seismic loading effects. Segmental wall construction is popular due to easy transportation and on-site assembly. The present study deals with the performance of precast wall elements connected through welded plates vertically subjected to the seismic loading conditions. The study proposes welded plates with varying thickness to connect two structural walls on one or both faces. Full-scale quasi-static load tests have been performed to analyze the seismic behavior of the connections. The conventional foundation with loading beams at top and bottom, to test the structural walls, was replaced with a special steel shoe set-up, achieving the real conditions, to minimize the testing cost. It has been observed that the connections using mild steel plates achieve the most desirable characteristics, like plate yielding, energy dissipation, and ductility. High-strength steel plates fail in brittle mode with poor post-peak response, indicating precautions in selecting the type of connecting steel plates in precast construction. The proposed connecting plates improve the ductility and post-peak response for easy retrofitting of the precast wall system. The study brings out improvement in the seismic performance, selection of materials, and connection detailing for resilient precast structures.