Design Of Ultra-high Performance Concrete Research Articles

PACKING ORDER THEORY FOR ULTRA-HIGH PERFORMANCE CONCRETE MIX DESIGN: A CRITICAL REVIEW

Ultra-high-performance concrete (UHPC), also known as reactive powder concrete (RPC), is a relatively new construction material with improved mechanical properties and long-term performance. UHPC is manufactured using a specific matrix of well-graded granular particles including Portland cement, supplementary cementitious materials (SCMs), and fine aggregates. The cementitious material matrix is hydrated using a relatively low water-to-powder ratio and high range water reducing chemicals (HRWR). The hydrated mix is augmented with high grade random steel fibers. UHPC superior mechanical performance and durability is attributed to the high packing order of the granular materials within the UHPC mix resulting in lower voids, reduced permeability, and high density. Accordingly, UHPC design depends on proportioning the mix granular material to produce minimal void content (i.e., produce highest packing order). This research paper discusses UHPC mix constituents, proprietary UHPC mix designs, sizes of granular particles, and resulting packing order. Moreover, a critical review of the particles packing order approach used in UHPC mix design is presented. Possible improvement of UHPC mix design may result in economic mixes, enhanced mechanical properties, and increased durability.

Experimental Study and Bearing Capacity Calculation of Compression-Reinforced Concrete Columns Strengthened with Ultra-High-Performance Concrete

A total of five ultra-high-performance concrete (UHPC)-strengthened reinforced concrete (RC) columns and one RC column were built and subjected to eccentric compression testing to examine the force performance of UHPC-strengthened eccentrically compressed plain RC columns. This experimental study examined the crack progression, the damage morphology, the deformation ability, the maximum load-carrying capacity, and the ductile properties of the eccentrically compressed columns. It also investigated the impacts of eccentricity, the reinforcement thickness, and the addition of steel fibers on the effectiveness of reinforcement. The cracking load, peak load, and ductility coefficient of the UHPC-reinforced specimens were increased by 100.28%, 172.30%, and 56.30%, respectively, compared with the RC column at an initial eccentricity of 50 mm. As the eccentricity distance increased, the bearing capacity of the UHPC eccentrically compressed specimens decreased, and the deformation capacity increased. Increasing the steel fiber dosage within the appropriate range decreased the crack width of the specimen. The addition of 2% steel fiber resulted in a 24.8% increase in cracking load, an 8.96% increase in peak load, and a 2.60% increase in ductility coefficient compared to the addition of 1% steel fiber. However, the reinforcing effect of UHPC was weakened under high eccentric pressures. Based on the theory of concrete structure and mechanical principles, the formula for calculating the compressive bearing capacity of RC columns strengthened with high-performance concrete was proposed. The results of calculating the positive section bearing capacity of eccentrically compressed RC columns reinforced with high-performance concrete are in good agreement with the test values. The results of this paper provide an experimental basis and theoretical foundation for the cross-sectional design of UHPC eccentrically compressed columns.

Self-updatable AI-assisted design of low-carbon cost-effective ultra-high-performance concrete (UHPC)

Machine learning has exhibited high efficiency in designing concrete. However, collecting the dataset for training machine learning models is challenging. To address this challenge, this paper develops an approach to collect concrete design data automatically based on information extraction techniques. The approach enables machine learning models to automatically track, extract, and learn knowledge embedded in data from relevant publications. The approach has been incorporated into AI-assisted design of low-carbon cost-effective ultra-high-performance concrete (UHPC) via integrating the capabilities of automatically collecting and processing data, predicting UHPC properties, and optimizing UHPC properties regarding the material cost, carbon footprint, and compressive strength. A self-updating mechanism is imparted to continuously learn available data. Such a mechanism enables the self-updatable automatic discovery of low-carbon cost-effective UHPC. The results showed increasing prediction accuracy and optimization performance of the proposed approach over time when more knowledge was learned from new data, therefore accelerating the design of UHPC.

Study on the mechanical properties and prediction model of ultra-high performance concrete containing aeolian sand and recycled mixed powder

This paper addresses the feasibility of the synergistic preparation of ultra-high performance concrete (UHPC) with recycled mixed powder (RMP) and aeolian sand (AS). In this paper, UHPC was prepared by replacing cement (0, 10%, 20%) and river sand (RS) (0, 15%, 30%, 45%) with RMP and AS (by mass). The optimized mix design of UHPC was based on the Modified Andreasen & Andersen particle packing model. The compressive, flexural, and splitting tensile strengths of different formulations of UHPC were tested. The results show that RMP and AS have a synergistic effect on the strength improvement of UHPC. When the content of RMP is 10% and the content of AS is 30%, the compressive, flexural, and splitting tensile strengths of UHPC are 131.85 MPa, 18.46 MPa, and 11.81 MPa, respectively, which increase with the largest improvement by 9.56%, 8.91%, and 8.25%, compared with the control group. The ultra-fine particles (VFP) (<175 μm) in AS adequately fill the pores generated by larger RS particles, and the void fraction of fine aggregate (FA) is minimized when the AS content is 30%. Moreover, a compressive strength model based on the pozzolanic activity effect was established. Finally, by scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses, it is found that RMP and AS can not only promote hydration nucleation and improve the hydration rate of cement, but also enhance the adhesion and occlusal force between the matrix and aggregate and improve the microstructure of the matrix and interfacial transition zone (ITZ). The results of this paper have reference value for the eco-friendly development of UHPC.

The micro–mechanical mechanism of multiple cracking behavior induced by interface strengthening for UHPC

The multiple cracking behavior of ultra–high–performance concrete (UHPC) is strongly dependent on the bridging of cracks by fibers and the spatial strength distribution of matrix. A good match between fiber bridging properties and matrix tensile strength can significantly improve the utilization of fibers and matrix. In this study, a micromechanics based theoretical model is proposed, which can match the matrix tensile strength with the fiber–matrix interface bonding strength. The spatial strength distribution of the matrix and the stress transfer between the fiber and the matrix is considered in the model. Bridge stresses, additional stresses, stress transfer distances, and pulley forces at the crack face for various crack openings are derived. Based on the calculation results, the interfacial bonding strength is determined to be 14 MPa which match the common condition of UHPC (Lf=13mm,df=0.2mm,2vol%,andσm=5.5MPa). Dense multiple cracks were observed in the designed tensile samples, and the peak strength and peak strain increase 53.3% (13.91 MPa) and 70.6% (2900με), respectively. The applicability of the model is validated by comparing the simulation results and the experimental results in previous investigations at the same paraments (τf=5.5MPa,σm=5.5MPa). With the proposed model, the mechanism of fiber–matrix interface enhancement contributing to the multiple cracking behavior can be understood. The results of this study could provide fundamental insights for the design of UHPC for various ductility and strength requirements.

Predicting ultra-high-performance concrete compressive strength using gene expression programming method

There have been extensive experimental studies available on the composition and characteristics of Ultra-High-Performance concrete (UHPC). However, the relation between UHPC characteristics and mixture content, on the other hand, is extremely non-linear and challenging to distinguish utilizing typical statistical approaches. A comprehensive literature research was carried out for this aim to acquire experimental data on the compressive strength of UHPC. The dataset contains 810 experimental values of compressive strength and 15 most influential parameters that include cement, water, nano-silica, quartz powder, limestone powder, gravel, sand, slag, superplasticizer, fiber, temperature, age, fly ash, relative humidity, and silica fume, are considered as input. The suggested gene expression programming (GEP) model can estimate the compressive strength of UHPC by using simple mathematical formulations. There is no predetermined function to evaluate in the GEP technique, and it replicates or eliminates numerous combinations of factors to create the formulation that suits the experimental results. For verification and validation of model performance, various statistical measures, SHAP analysis, external validation checks, and comparing with the regression model, are applied. SHAP analysis provided that age, fiber, silica fume, superplasticizer, cement, sand, and water have a high influence on compressive strength while other input parameters have less influence on compressive strength. The model outcomes indicate the robustness and accuracy of the predictive potential of the proposed model. As a result, the GEP model can be used to give practical insights into the mixture design of UHPC for a variety of construction applications, resulting in better predictive capacity at a cheaper cost and in a considerably shorter period. Also, the present study findings can assist the design engineers and builders to understand the significance of each constituent in UHPC.

Flexural strength and toughness of self-leveling UHPFRC

Acquiring higher performance at both fresh and hardened states is critical in the practical design of ultra-high performance concrete (UHPC)/ultra-high performance fiber reinforced concrete (UHPFRC). Herein, a total of 12 self-leveling UHPC and UHPFRC mixtures were developed and evaluated for their flexural performance by three-point bending tests, together with their workability and mechanical properties. From load-deflection curves, the first cracking flexural strength, post cracking flexural strength, flexural deflection capacity, and flexural toughness were determined. By corelating the flexural responses to fiber factor and matrix strength, the flexural strengths were found to be positively affected by fiber factor and matrix strength, while the flexural deflection capacity and toughness indices were less influenced by matrix strength but can be substantially enhanced by increasing steel fiber volume. Design equations were derived by physics-informed data-fitting for flexural strengths and toughness with high accuracy, allowing for quantification of synergistic effect between fiber factor and matrix strength in resilient design.

Derivation and verification of multilevel particle packing model for Ultra-High Performance Concrete (UHPC): Modelling and experiments

This paper aims to address the conflict between gap gradation and continuous particle packing for the precise design of ultra-high performance concrete (UHPC). More exactly, a new multilevel particle packing model is proposed by deriving the modified Anderson and Andreasen (MAA) equation. The key parameters of the model are determined by Box-Behnken design method. On this basis, the dense packing states of powder and aggregate segments are achieved, and the accurate ratio of two segments are confirmed. Thus, the optimal mix proportion of UHPC matrix can be calculated. Moreover, some novel viewpoints on the development of dense particle packing theory are presented in this study, including the derivation of multilevel particle packing model with n times of gap gradation and the discussion of general mix design procedure that unifies gap gradation and continuous packing, which could further improve the design theory of UHPC composites.

Precise mix-design of Ultra-High Performance Concrete (UHPC) based on physicochemical packing method: From the perspective of cement hydration

• A physicochemical packing method to optimize the design of UHPC is proposed. • A new equivalent particle size distribution of hydrated cement is calculated. • The macro characteristics of the newly developed UHPC is evaluated. • The hydration kinetics of the newly developed UHPC is evaluated. • The microstructure development of the newly developed UHPC is evaluated. It is widely accepted that Ultra-High Performance Concrete (UHPC) could be designed by the employment of Modified Andreasen and Andersen (MAA) particle packing model. However, the traditional MAA model only takes into account that dry particles reach the dense packing status under physical conditions, without considering the chemical hydration of wet cement particles. To solve this problem, this study adopts a physicochemical packing method to optimize the mix-design of UHPC through establishing a new equivalent particle size distribution of hydrated cement by applying a multilayer spherical shell model. Based on this, the mix proportion of UHPC is redesigned and the properties of the optimized UHPC are evaluated afterwards. The results show that the optimized mix-design leads to higher particle packing density of UHPC, thereby improving the durability on the premise of guaranteeing the workability and mechanical properties. Meanwhile, the optimized mix-design can also achieve denser micro structures of UHPC matrix, which provides a valuable reference to design UHPC more scientifically and reasonably.

A review of existing codes and standards on design factors for UHPC placement and fiber orientation

• Review different construction procedure and their effects on fiber orientation. • Review existing UHPC design codes and their consideration of fiber orientation. • Recommendation on following UHPC design codes regarding fiber orientation. • Identify future research on construction procedure to ensure fiber orientation. Ultra-high-performance concrete (UHPC) has been extensively researched and implemented in various infrastructure applications for its superior structural and durability performance. Despite their successful structural level demonstrations, fiber dispersion and orientation remain a concern in the UHPC structure design. The casting flow direction, formwork geometry, rebar arrangement, use of casting device, and rheology of the mixtures are all reported to affect the fiber orientation and ultimately the structural performance of the UHPC members. A number of UHPC design codes and guidelines have been published by professional organizations across different countries, and the effect of fiber orientation were considered in several distinct ways in these specifications. In this article, a comprehensive review was conducted on the influence of various factors on the fiber orientation in structural elements. Existing structural design guidelines on UHPC, and steel fiber reinforced concrete materials from France, Germany, Japan, South Korea, Canada, Switzerland, Australia, and the U.S. were reviewed with a focus on their respective methods to account for the effect of fiber dispersion and orientation. Several design codes introduced separate design reduction factors to compensate the effect of undesirable fiber orientation. The background work and underlying rationale of these design factors were also included in this paper. Recommendations to the design professionals and for future research directions were provided at the end of the paper.

Mechanisms of autogenous shrinkage for Ultra-High Performance Concrete (UHPC) prepared with pre-wet porous fine aggregate (PFA)

Here, the intrinsic driving mechanisms of autogenous shrinkage for Ultra-High Performance Concrete (UHPC) incorporating porous internal curing (IC) medium are detailly investigated, including characterization and monitoring of UHPC hydration kinetics, internal relative humidity (IRH) and internal temperature (IT) fields. The results show that the autogenous shrinkage evolution of UHPC prepared with pre-wet porous fine aggregate (PFA) undergoes multiple stages. The potential expansion drivers of UHPC with IC at super early hardening period are identified as the extra liquid volume compensation and the effect of thermal expansion. Furthermore, the rapid shrinkage growth stage of the UHPC matrix is governed by the hydration dynamic, while the sustained shrinkage growth stage is managed by cold shrinkage, chemical shrinkage and capillary pressure. Finally, the feasibility of the Powers model in the design of UHPC incorporating wet PFA is carefully revealed, and the key parameters for obtaining UHPC with advanced volumetric stability are elucidated. The strategy in terms of the autogenous shrinkage for the UHPC designed with PFA is revealed.

Machine learning-based mix design tools to minimize carbon footprint and cost of UHPC. Part 1: Efficient data collection and modeling

The emergence of ultra-high-performance concrete (UHPC) as an attractive solution for precast and prestressed applications has coincided with global efforts towards sustainable construction. Tools capable of intuitively demonstrating the tradeoffs between mix proportions and mechanical performance are in increasing need for this industry given the embodied carbon footprint associated with typical UHPC compositions. Meanwhile, the ingredients and their proportions required to produce UHPC synergistically dictate material performance. This makes linear regression ineffective for optimization purposes, while presenting a great opportunity for artificial intelligence (AI) techniques. Yet, the effectiveness of AI models is highly dependent on the size, distribution, and quality of the data. This has led researchers to use datasets from multiple sources to develop their models in lieu of performing extensive experimental runs, which can become exhausting, time-consuming and resource-intensive. This study presents a novel and efficient methodology to optimize mixture design of UHPC while performing reduced experimental runs, allowing concrete producers to quickly generate efficient models using their own data and materials. In this work (Part 1), orthogonal arrays are utilized to collect data needed to enable machine learning (ML) design optimization. ML techniques in random forest and k-nearest neighbors are ensembled to generate Performance Density Diagrams (PDDs), an innovative mix design tool developed to facilitate decision-making by designers, where changes in strength associated with changes in mix proportioning can easily be evaluated by industry personnel. Results indicate that the generated PDDs effectively predicted the trends, magnitude and ranking of most mixtures stored in a test set. Moreover, PDDs enabled the design of an UHPC mixture averaging 155 MPa at age 56 days, while maintaining the fine-aggregate-to-cementitious ratio above unit. This methodology is later extended in “Part 2” of this research, where predicted strengths from the model developed herein are used as inputs to calculate multi-objective comparison indices that allow performance, cost and carbon footprint to be evaluated concurrently..

You only design once (YODO): Gaussian Process-Batch Bayesian optimization framework for mixture design of ultra high performance concrete

Ultra-high-performance concrete (UHPC) has superior strength and durability, and hence it has been primarily favored in a variety of applications in structural engineering. While the open literature presents a series of predictive machine learning (ML) models to predict the strength of UHPC from its constituent materials, the reverse problem of identifying possible concrete mixtures with a targeted (pre-tailored) performance persists to exist. Unlike other works, and in an effort to bridge this knowledge gap, this study proposes a Gaussian process (GP) modeling with batch Bayesian optimization (BBO) framework (GP-BBO) to infer the mixture design of UHPC. In this framework, the GP is used as a predictive surrogate model constructed from experimental measurements. After the GP is trained and validated, BBO is used to infer the plausible formulae for the targeted strength by optimizing an acquisition function that trades off exploitation and exploration based on the optimality and variability of the surrogate model. As such, the proposed framework offers a list of possible UHPC formulae of a targeted strength. To facilitate a wide spread of the proposed framework, The ML code is shared for interested researchers to verify and expand upon. In addition, and to negate arising hurdles associated with GP-BBO programming, also an open-source and coding-free software (App) is created that can be directly deployed by UHPC fabricators. In contrast to the conventional trial-and-error-based mixture design, GP-BBO provides a self-adaptive paradigm for efficient sampling of design space for identifying the optimum sampling points. This framework can be extended to infer formulae that satisfy multiple performance objectives such as strength, workability, and durability.

Utilizing Iron Ore Tailing as Cementitious Material for Eco-Friendly Design of Ultra-High Performance Concrete (UHPC).

In this research, iron ore tailing (IOT) is utilized as the cementitious material to develop an eco-friendly ultra-high performance concrete (UHPC). The UHPC mix is obtained according to the modified Andreasen and Andersen (MAA) packing model, and the applied dosage of IOT is 10%, 20%, and 30% (by weight), respectively. The calculated packing density of different mixtures is consistent with each other. Afterwards, the fresh and hardened performance of UHPC mixtures with IOT are evaluated. The results demonstrate that the workability of designed UHPC mixtures is increased with the incorporation of IOT. The heat flow at an early age of designed UHPC with IOT is attenuated, the compressive strength and auto shrinkage at an early age are consequently reduced. The addition of IOT promotes the development of long-term compressive strength and optimization of the pore structure, thus the durability of designed UHPC is still guaranteed. In addition, the ecological estimate results show that the utilization of IOT for the UHPC design can reduce the carbon emission significantly.

The Role of Iron Tailing Powder in Ultra-High-Strength Concrete Subjected to Elevated Temperatures

Iron tailing powder (ITP) is considered to have the potential to replace cement to manufacture ultra-high-performance concrete (UHPC). However, the performance of UHPC with the addition of ITP after exposure to high temperatures is more complex. This investigation prepares seven UHPC formulations by introducing different contents of ITP and investigates the mechanical behavior (residual strength), bound water content, and microstructural properties (crystalline and amorphous phases, chemical structure, and morphology) of UHPC subjected to elevated temperatures. The experimental results show that the addition of ITP postpones the spalling of concrete when exposed to high temperatures. The concrete incorporating 15% ITP maintains 53.8% of its original strength at 800°C, unlike the concrete without ITP that maintains 31.6% of its original strength. The addition of ITP increases the number of micropores/cracks in concrete and helps release the vapor pressure caused by water evaporation. The findings of this investigation highlight the potential application of ITP for future UHPC design and manufacture.

Valorization of converter steel slag into eco-friendly ultra-high performance concrete by ambient CO2 pre-treatment

The converter steel slag is a by-product during the steel-making process, which usually acts as an inert ingredient in construction due to the relatively low reactivity. However, its mineral composition results in a high reactivity in a CO2 rich environment. This study reveals the modification of converter steel slag by an ambient CO2 pretreatment, and the application of modified converter steel slag as the supplementary cementitious material (SCM) in the design of eco-friendly ultra-high performance concrete (UHPC) by using a particle packing model. The results show that converter steel slag after ambient CO2 pretreatment is feasible to be used in eco-friendly UHPC design, which achieves a relative higher compressive strength over 150 MPa with the cement substitution from 15% to 45%, compared to non-carbonated steel slag. The ambient CO2 pretreatment can modify the physical and chemical properties of converter steel slag particles, which produces a rough and porous surface of slag particles because of the precipitation of calcium carbonate and amorphous silica gel as carbonation products. Consequently, the incorporation of carbonated steel slag improves the cement hydration, enhance the formation of ettringite and C-S-H, while densify the microstructure compared to non-carbonated slag in eco-friendly UHPCs. The leaching of potentially hazardous elements, e.g. Cr and V from carbonated steel slag can be efficiently reduced below legal limits in UHPC mixtures.

Multi-level micromechanical analysis of elastic properties of ultra-high performance concrete at high temperatures: Effects of imperfect interface and inclusion size

Ultra-high performance concrete (UHPC), as a typical multi-phase composite material, is vulnerable to mechanical degradation and explosive spalling at high temperatures. In this work, the temperature-dependent elastic modulus of UHPC is estimated through the step-by-step homogenization from gel matrix level to ultra-high performance fiber-reinforced concrete level. Totally three forms of C-S-H gels are considered to determine the elastic properties of the initial matrix, and the phase transformation and volume fractions of individual phases at high temperatures are calculated based on the hydration and dehydration kinetics models. Moreover, the effects of imperfect interface and inclusion size are taken into account by introducing the spring-layer interface model and log-normal size distribution function to the homogenization scheme. The predicted elastic moduli are validated by analytical and experimental results, showing that accurate predictions can be obtained at up to 800°C. Interestingly, the imperfect interface matters when high-modulus inclusions are embedded in the low-modulus matrix, and the effect of imperfect interface on effective elastic modulus varies a lot at different microscopic levels. The findings highlight the synergistic effects of imperfect interface and inclusion size on the elastic properties, which is helpful to the mechanical design of UHPC at high temperatures.

Optimized design of steel fibres reinforced ultra-high performance concrete (UHPC) composites: Towards to dense structure and efficient fibre application

This paper presents a method for designing steel fibre reinforced Ultra-High Performance Concrete (UHPC) composites with dense micro/meso structure and efficient fibre application. Based on the modified Andreasen & Andersen packing model, the UHPC skeleton is first designed. Then, the effects of steel fibre shape and content on particle packing of UHPC are evaluated, which depicts that the hooked-end fibres have the most obvious negative effects on this system compared to other shaped fibres. Hence, to minimize the negative effects of fibres, the D-Optimal Design (DOD) method is utilized for the theoretical optimized design of UHPC via modelling of wet packing density with hybrid fibres. Meanwhile, an aided design model called Artificial Neural Network (ANN) is built for the prediction of wet packing density against different fibres, which shows very satisfactory accuracy, convenience and practicability. Finally, the basic properties of the developed UHPC are evaluated in detail to prove the feasibility and advancement of the proposed design method. In general, based on the approach proposed herein, an optimized UHPC composite with dense structure, high fibre efficiency and admirable mechanical properties could be produced.

A novel development of green ultra-high performance concrete (UHPC) based on appropriate application of recycled cementitious material

Recycled construction waste cementitious material (RCWCM) can be considered as a kind of waste solid with potential cementitious characteristics. In the past, landfill and stacking methods were generally used for treating this type of wastes, which is harmful for the sustainable development of construction industry. Hence, based on this premise, an effective method for developing a green Ultra-High Performance Concrete (UHPC) by incorporating RCWCM is addressed in this study. Specifically, dehydrated cementitious powder (DCP) is obtained from heating treatment of RCWCM. Then, the DCP is used to gradually replace the cement and included in the design of UHPC based on Modified Andreasen & Andersen (MAA) particle packing model. Afterwards, the physical and chemical characteristics of the newly produced UHPC are evaluated. The results show that when DCP is used to replace up to 25% cement, the variation of UHPC compressive strength is difficult to be noticed. Corresponding to that, the microstructure and durability of the developed green UHPC are also advanced when the added DCP content is less than 25%. Additionally, the ecological assessment shows that the UHPC incorporating DCP has a relatively small impact on the environment, which provides a broad prospect for the future sustainable development of UHPC.

Study on Optimization of Working Performance of Ultra High Performance Concrete

The water binder ratio is a key parameter in the mix design of ultra-high performance concrete. Aiming at the high sensitivity of ultra-high performance concrete to water consumption, the influence of water consumption on the performance of ultra-high performance concrete was studied in a narrow range. The compatibility ratio of raw materials of ultra-high performance concrete can be adjusted, but the space is small, so we try to improve the fluidity of concrete by physical and chemical means. The experimental results show that the fluidity of concrete increases slightly with the addition of glass beads, but the flexural properties of the concrete are adversely affected. With the addition of viscosity reducer, the workbility of concrete increases, but the compressive strength decreases.