International Concrete Abstracts Portal

Advances in concrete material science have led to the development of a new class of cementitious materials, namely ultra-high-performance concrete (UHPC), which offers superior mechanical and durability properties. The control and characterization of the fresh properties of UHPC are crucial for successful mixture design. Among the methods for evaluating these properties, the mini-cone test has gained prominence due to its practicality. It requires smaller sample volumes than the standard slump cone test, making it especially suited for laboratory assessments of UHPC mixtures. In contrast, the slump flow test is the simplest and most widely used test for both laboratory and field testing of concrete. This study aims to establish a correlation between mini-cone flow and standard slump flow test results. A linear relationship is identified, which forms the basis for proposing consistency classes for UHPC using mini-cone flow values. These proposed classes align with the established consistency classifications for self-compacting concrete.

Integrating real-time sensor data with physics-based models enhances the accuracy and efficiency of structural simulation and prognosis. In this study, a sensing-based simulation method is introduced to compute bending moments in reinforced concrete bridge columns subjected to seismic motions, based on the measured strains continuously fed to plasticity models. The experimental program included hybrid testing of scaled reinforced concrete bridges under consecutive seismic events. The experimental columns were instrumented with embedded as well as surface-adhered fiber-optic Bragg grating (FBG) sensors for real-time monitoring of strains reflecting degradation of the columns during the formation of damage. The fundamental assumption of strain compatibility in reinforced concrete members was investigated for the successive progression of damage in the cross sections of the columns. The stress distributions within the concrete core and cover were computed through the confined and unconfined concrete stress-strain relations for loading, unloading, and reloading scenarios. The bending moments in the cross-section were computed and compared with the corresponding experimental values calculated based on direct measurements of forces. The results from this study revealed that the cross-sectional strains exhibit three primary features during the seismic events that need to be considered for the accurate calculation of bending moments. Computation of the bending moments requires considering the shifts in cyclic reference, post-event residual strains, and the real steel strains. By using these features, the computed bending moments during the column tests mimicked the experimental results based on the measured seismic forces on the columns.

This study investigates the bonding behavior of large-diameter steel bars (D43 and D57) embedded in concrete using pull-out tests coupled with acoustic emission (AE) monitoring. These large bars, commonly used in nuclear containment structures from the 1970s, were compared with conventional steel bars (D19 and D32) across three concrete strength levels. All tests were performed under displacement-controlled loading using an MTS testing machine. Results indicate that ACI 408R provisions remain valid for large-diameter reinforcing bars. The test results showed that when specimens reached ultimate bond stress, the D57 bar developed only 12 to 16% of its yield strength, whereas the D19 bar reached at least 70%. AE monitoring effectively captured the debonding process, and cumulative AE hit counts correlated with the strain energy released at each loading stage, offering insight into bond failure mechanisms.

The durability of reinforced concrete is often compromised by chloride penetration, leading to corrosion of reinforcing steel and reduced structural strength. To improve the sustainability and longevity of concrete structures, it is crucial to model and predict chloride permeability (CP) accurately, thereby minimizing the time and resources required for extensive experimental testing. This paper presents a proof-of-concept study applying Artificial Neural Networks (ANN) to predict CP in concrete structures. The model was trained on a small but carefully controlled experimental dataset of 10 concrete mixtures, considering four key parameters: water-to-cementing materials ratio, silica fume content, cementing materials content, and air content. Despite the limited dataset size, which constrains generalizability and statistical robustness, the ANN captured nonlinear relationships among the input parameters and CP. The comparison between experimental and simulated CP values showed reasonable agreement, with errors ranging between –242 and 420 Coulombs. These results establish the trustworthiness and reliability of the proposed model, providing a valuable tool for predicting CP and informing the design of durable and sustainable concrete structures.

Portland Limestone Cement (PLC) has gained widespread use as the most accessible and sustainable blended cement in the market. However, in many African countries, including Ghana, the use of clay pozzolana in the concrete industry has primarily relied on Ordinary Portland Cement (OPC). In this study, PLC Type II/B-L was partially replaced with clay pozzolana at levels ranging from 10% to 50% by weight. The investigation included compressive strength testing, non-destructive evaluations using electrical surface resistivity, pulse velocity, and chloride penetration tests, targeting a characteristic strength of 30 MPa. Additionally, an environmental impact assessment based on the carbon footprint of both control and clay pozzolana concretes was conducted. The mix design followed the EN 206 standard. A total of 72 cubic moulds were produced for the strength test. The results showed that clay pozzolana concretes with between 10 and 20% replacement achieved strength values of 35 and 33 MPa, respectively, higher than the target of 30 MPa (4351.13 psi) strength at 28 days. However, mixtures with 30% to 50% replacement required extended curing periods of 60 to 90 days to reach the desired strength. At extended curing, 10-50% clay pozzolana replacement attained strength between 32 and 41 MPa. Non-destructive test results showed no direct correlation with compressive strength, confirming that different factors govern strength, resistivity, and pulse velocity. The environmental impact assessment revealed a 14 to 51% reduction in CSi and a 19 to 36% increase in CRi with 10 to 50% clay pozzolana (for CSi) and 10 to 40% (for CRi). The thermodynamic modelling also revealed that pozzolana contents below 30% primarily promoted pozzolanic reactions, enhancing performance compared to the control mix. Based on these results, 20–30% clay pozzolana replacement is recommended to ensure reliable performance, while higher levels (>30%) require further durability evaluation for long-term use.