International Concrete Abstracts Portal

This study presents a machine learning–driven framework for the sustainable design of ultra-high-performance concrete (UHPC) mixtures with a focus on maximizing flexural strength while minimizing material cost and embodied CO₂ emissions. A curated dataset of 333 UHPC mixtures was developed, incorporating 13 input features including binder composition, steel fiber dosage, and curing parameters. A Bayesian Neural Network (BNN) was trained to predict flexural strength with high accuracy (R² = 0.936, RMSE = 1.37 MPa, MAE = 1.09 MPa), supported by residual analysis confirming minimal prediction bias and robust generalization. SHAP analysis was used to interpret model predictions and identify key drivers of flexural behavior—namely, curing time, steel fiber dosage, and silica fume content. The BNN was coupled with the NSGA-III algorithm to perform multi-objective optimization and generate Pareto-optimal UHPC mixtures. A utility-based scoring method was introduced to select designs aligned with different project priorities—enabling the identification of fiber-rich, high-strength mixtures as well as low-emission, cost-efficient alternatives. The framework supports field-level implementation and is well-suited for integration with sustainability rating systems such as LEED or Envision. It provides a transparent, generalizable, and industry-ready tool for intelligent UHPC mixture optimization, advancing data-driven design practices for green infrastructure applications.

The influence of axial compression is not incorporated into the design provisions for concrete breakout or pryout strength of anchors under shear. This study experimentally evaluated the shear capacities of anchors subjected to axial compression on a base plate using ten large-scale specimens. The test variables included axial compression N, edge distances from the anchor shaft in the direction of applied shear, edge distances perpendicular to the applied shear, and the compressive strength of concrete. The results showed little difference in crack initiation and propagation with varying axial compression. However, axial compression significantly improved the concrete breakout strength of anchors in shear. The applied axial compression reached up to 2.5 times the mean concrete breakout strength V_cbgo, as determined by the Concrete Capacity Design (CCD) method, and the average increase in shear strength was approximately 0.6 times the applied compression. In addition, axial compression suppressed concrete pryout failure by preventing the uplift of base plates. Based on the lowest N/V_cbgo ratio used in the tests, if axial compression of at least 0.5V_cbgo is applied to a base plate, pryout failure need not be considered.

In high-rise buildings, lower-story columns must withstand significant seismic shear forces while maintaining sufficient deformation capacity. This capacity is provided through effective confinement using transverse reinforcement. The ACI 318-25 building code specifies that confining reinforcement should be proportional to the applied axial load when the axial load exceeds 0.3A_gf'c and requires all longitudinal bars to be laterally supported with seismic hooks. However, the implementation of seismic hooks at both ends of crossties brings challenges for on-site reinforcement assembly.

This study experimentally investigates full-scale RC column specimens subjected to quasi-static cyclic loading while under a constant high axial load. The objectives are to validate the ACI 318-25 confinement requirements and to evaluate the feasibility of relaxing seismic hook requirements. The results confirm that columns designed in accordance with the ACI 318-25 building code satisfy the required 3% deformation capacity. Furthermore, satisfactory seismic performance can be achieved with crossties incorporating alternating 135-degree and 90-degree hooks, although at the expense of increased confining reinforcement.

Reinforced concrete members derive flexural strength from reinforcing steel, which acts together with the concrete to mobilize the required capacity. Design standards stipulate mandatory norms that must be complied with during the design process. Non-compliance with these provisions can increase the risk of corrosion, compromising the safety and integrity of the structure. Concrete protects the reinforcing steel against corrosion, but it can also become a contributing factor when its microstructure is poor due to non-compliance with these norms. Assessing the residual flexural capacity is essential for making informed decisions regarding repair or demolition. The proposed model in this paper enables computation of the reduction in flexural strength based either on gravimetric mass-loss percentage or on measured corrosion current density. A design chart is also proposed to facilitate practical application, enabling engineers to assess residual capacity and decide on repair or demolition.

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.