International Concrete Abstracts Portal

This research aims to prepare porous ceramsite with low thermal conductivity. The porous ceramsite was also used as fine aggregate to substitute the river sand in pumice concrete. Its impact on improving the thermal insulation performance of pumice concrete was thoroughly investigated. The experimental method included high-temperature calcination, transient planar heat source analysis, as well as the use of X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), and Mercury-Intrusion Porosimetry (MIP) techniques. The investigation revealed that the best calcination parameters were a preheating temperature of 400°C, a preheating duration of 25 minutes, a calcination temperature of 125°C, and a calcination duration of 25 minutes. Under these conditions, the crushing index of the porous ceramsite was determined to be 29.1%, with a thermal conductivity of 0.138 W/(m·K). It is worth noting that an increase in calcination temperature promotes the hole content in ceramsite, leading to a 52.19% increase in macropore volume and a corresponding decrease in thermal conductivity. Furthermore, as the replacement rate of ceramic aggregate increases, the thermal conductivity of pumice concrete gradually decreases, with values ranging from 18% to 34.8%. This reduction occurs because the replacement elevates the volume of coarse capillary pores and non-capillary pores in pumice concrete, increasing by 13.9 to 91.3% and 63.1 to 128.5%, respectively. Additionally, a prediction model for the thermal conductivity of pumice concrete has been established using the Mori-Tanaka homogenization method. The model's verification accuracy falls within an error range of 5%, demonstrating its effectiveness in accurately predicting the thermal conductivity of pumice concrete.

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.

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.

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.