International Concrete Abstracts Portal

To address the autogenous shrinkage issue of ultra-high-performance concrete (UHPC), internal curing technology has shown great potential in resolving this challenge by providing additional moisture. To further improve its curing efficiency, this study proposes an innovative internal curing technology that can significantly reduce autogenous shrinkage without increasing the amount of internal curing water or compromising mechanical strength. This approach utilizes perforated cenospheres (PCs) as internal curing agents while substituting internal curing water with urea solutions. In addition to replenishing water, urea solutions, once released into the cement paste, can react with portlandite. This reaction generates CaCO₃; owing to the intrinsic properties of CaCO₃, it has a larger macroscopic volume and a much higher elastic modulus than portlandite. This approach effectively reduces chemical shrinkage while concurrently increasing the stiffness of the cement paste, thereby achieving a significant reduction in autogenous shrinkage. As a result, replacing water with 3% urea solution in PCs enhances the autogenous shrinkage of UHPC, reducing it from less than 50% to over 90%.

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.

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.

Ultra-high performance concrete (UHPC) is a forward-looking material ideal for use in large-scale civil infrastructure systems. However, due to its unique mix, when UHPC is used in actual structures in conjunction with materials like steel reinforcement, it may lead to unexpected behavior. Therefore, this study analyzed the behavior of reinforced UHPC (R-UHPC) for use in actual structures, focusing specifically on beams among various structural components, with a particular emphasis on their flexural behavior. For this purpose, the study collected and comprehensively analyzed experimental data from flexural tests of R-UHPC beams conducted to date, identifying basic mechanics, peculiarities, and considerations in structural design. This study highlights that, besides the commonly known longitudinal reinforcement ratio, numerous factors such as beam length, height, number of tension reinforcement layers, strength, etc., can influence the flexural behavior of R-UHPC beams and demonstrate how these elements impact the performance.

This paper presents the interface shear between ordinary concrete and ultra-high-performance concrete (UHPC) connected with glass fiber-reinforced polymer (GFRP) reinforcing bars. Following ancillary tests on reinforcing bar fracture under in-plane shear loading, concrete-reinforcing bar assemblies are loaded to examine capacities and failure modes as influenced by the size, spacing, and number of the reinforcing bars. While the shear behavior of bare reinforcing bars is primarily governed by the orientation of the load-resisting axes in the glass fibers and their volume, the size and spacing of the reinforcement largely control the interface capacity by affecting the load-transfer mechanism from the reinforcing bar to the concrete. The degree of stress distribution affects the load-displacement response of the interface, which is characterized in terms of quasi-steady, kinetic, and failure regions. The primary failure modes of the interface comprise rebar rupture and concrete splitting. The formation of cracks between the ordinary concrete and UHPC results from interfacial deformations, leading to spalling damage when applied loads exceed service levels. An analytical model is formulated alongside an optimization technique. The capacities of the interface in relation to the reinforcing bar rupture and concrete splitting failure modes are predicted. Furthermore, a machine learning algorithm is used to define a failure envelope and propose practice guidelines through parametric investigations.