Compressive Strength Of Ultra-high Performance Concrete Research Articles

Predicting the Compressive Strength of Ultra-High-Performance Concrete Based on Machine Learning Optimized by Meta-Heuristic Algorithm

Ultra-high-performance concrete (UHPC) is a recently developed material which has attracted considerable attention in the field of civil engineering because of its outstanding characteristics. One of the key factors in concrete design is the compressive strength (CS) of UHPC. As one of the most potent tools in artificial intelligence (AI), machine learning (ML) can accurately predict concrete’s mechanical properties. Hyperparameter tuning is crucial in ensuring the prediction model’s reliability. However, it is a complex work. The purpose of this study is to optimize the CS prediction method for UHPC. Three ML methods, random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN), are selected to predict the CS of UHPC. Among them, the RF model demonstrates superior predictive accuracy, with the testing dataset R2 of 0.8506. In addition, three meta-heuristic optimization algorithms, particle swarm optimization (PSO), beetle antenna search (BAS), and snake optimization (SO), are utilized to optimize the prediction model hyperparameters. The R2 values for the testing dataset of SO-RF, PSO-RF, and BAS-RF are 0.9147, 0.8529, and 0.8607, respectively. The results indicate that SO-RF exhibits the highest predictive performance. Furthermore, the importance of input parameters is evaluated, and the findings prove the feasibility of the SO-RF model. This research enriches the prediction method of the CS of UHPC.

Recycled glass powder as an alternative to fly ash in non-proprietary UHPC: A comparative study of resource-efficient design, mechanical and durability properties

Fly ash (FA) is one of the preferable supplementary cementitious materials used in the design of resource-efficient ultra-high performance concrete (UHPC). Recent changes in energy production have led to a reduced availability of FA which increases the need to find an alternative replacement. One suitable, innovative, and sustainable pozzolan with higher consistency in material properties is recycled glass powder (RGP), produced from 100% post-consumer glass. To fill the knowledge gap whether RGP is a suitable alternative to FA in non-proprietary UHPC, this research emphasizes the comparative investigation of material properties of UHPC using FA versus RGP. The comprehensive material characterization and analysis included workability, mechanical behavior such as compressive strength, single fiber pull out and direct tensile behavior, static elastic modulus, and long-term strength. Durability properties included electrical surface resistivity and freeze-thaw resistance. In addition, a comparative analysis for their resource-efficiency and service life was also performed. The results were compared to two commercial UHPCs, available in the United States. This research showed that UHPC with RGP can be designed to achieve comparable mechanical and durability properties as UHPC with FA. Compressive strength up to 176 MPa (25.5ksi) were obtained without the use of special treatment, at current material cost of about US$1145/m3 with 2% of fiber volume fraction of steel fibers. Over time, the compressive strength of UHPC with RGP increased by 15%, resulting in more than 200 MPa (29.0ksi) after three years. Both UHPCs designed with either FA or RGP showed strain-hardening under direct tension with maximum tensile strength of about 14.8 MPa (2.1ksi). Both the UHPC with RGP and FA achieved electrical surface resistivity of more than 870kΩ × cm and 822kΩ × cm in 28 days and relative dynamic modulus of 102% after 600 freeze-thaw cycles that ensure long term durability with design service life of 364 and 344 years respectively.

The Influence of Manganese Slag on the Properties of Ultra-High-Performance Concrete.

Manganese slag (MS) is a kind of chemical waste, which may pollute the environment if conventional handling methods (stacking and landfill) are applied. Ultra-high-performance concrete (UHPC)-with considerably high compactness and strength-can be used not only as a special concrete material, but also to solidify the toxic substances in solid waste. This study proposes the addition of MS to UHPC, where the mass ratio of MS varies from 0% to 40% in the total mass of MS and silica fume. The effects of MS on the fluidity, plastic viscosity, and yield shear stress are investigated, and the flexural strength, compressive strength, and dry shrinkage rate of UHPC with MS are measured. X-ray diffraction (XRD) spectrum and energy spectrum analysis (EDS) diagrams are obtained to analyze the performance mechanism of the UHPC. A rheological study confirms that the slump flow increases with the increasing rate of 0-14.3%, while the yield shear stress and plastic viscosity decrease with the rates of 0-29.6% and 0-22.2%, respectively. The initial setting time increases with the mass ratio of MS by 0-14.3%, and MS has a positive effect on the flexural and compressive strengths of UHPC. In the early curing stage (less than 14 days), the increasing rate in the specimens increases with the curing age; meanwhile, when the curing age reaches 14 days or higher, the increasing rate decreases with increasing curing age. The compactness of UHPC is increased by adding MS. Furthermore, MS can increase the elements of Al and decrease crystals of Ca(OH)2 and calcium silicate hydrate in UHPC.

Data-driven moment-carrying capacity prediction of hybrid beams consisting of UHPC-NSC using machine learning-based models

This paper presents, for the first time in the literature, a study on the development of data-driven machine learning (ML) models to predict the moment-carrying capacity of ultra-high performance concrete (UHPC)-normal strength concrete (NSC) hybrid beams. A database of 56 specimens of rectangular-section UHPC-NSC hybrid beams subjected to flexural loading is adopted to train the models. In this context, ten ML algorithms that are most preferred in structural engineering applications are selected to develop prediction-based models: linear regression (LR), lasso regression (LASSO), ridge regression (RR), support vector regression (SVR), multilayer perception (MLP), random forest (RF), extremely randomized trees (ERT), extreme gradient boosting (XGBoost), K-nearest neighbors regression (KNN), and adaptive boosting regression (AdaBoost). Moreover, the Shapley additive explanation (SHAP) method is used to assess the impact of the input features on the prediction results. Lastly, user-friendly a graphical user interface (GUI) has been developed to ensure the interpretability of the prediction models and to overcome the "black box" problem of ML methods. The GUI, which is designed based on the model with the most effective prediction ability obtained from this work, allows design engineers to analyze their own data and customize the parameters of the model for the prediction of the moment-carrying capacity of UHPC-NSC hybrid beams. The results indicated that ML models can be an effective tool for predicting the moment-carrying capacity of UHPC-NSC hybrid beams. In this regard, notably, the XGBoost model exhibited superior performance in terms of prediction accuracy and generalization ability (R2 = 0.996 and 0.945 in the training and test datasets, respectively). On the other hand, according to the SHAP analysis results, the three most important input parameters influencing the moment-carrying capacity of UHPC-NSC hybrid beams are the effective depth (d), UHPC thickness at the bottom of the beam (UHPCbottom layer) and compressive strength of UHPC (fc,UHPC), respectively. Moreover, it has been found that the placement of the UHPC layer at the bottom of the beam rather than at the upper part of the beam is more effective in enhancing the moment-carrying capacity of UHPC-NSC hybrid beams.

Comparative study on the effect of steel and plastic synthetic fibers on the dynamic compression properties and microstructure of ultra-high-performance concrete (UHPC)

Ultra-high-performance concrete (UHPC) shows great potential in constructing concrete structures subjected to impact and explosive forces. In order to make UHPC structures more ductile and cost-effective, this study used four-volume fractions of plastic synthetic fiber (PSF) instead of conventional steel fibers (SF) to prepare ultra-high-performance hybrid fiber-reinforced concrete (UHP–HFRC) with a total fiber weight of 2%. The dynamic compression properties at four strain rates were also investigated using a split Hopkinson pressure bar (SHPB) test. The results show that the addition of PSF reduces the flowability and compressive strength of UHPC but contributes significantly to the flexural strength of UHPC. UHP-HFRC demonstrates higher integrity under impact loading and significant sensitivity to strain rates. As the strain rate increases, both the dynamic compressive strength and dynamic growth factor of UHP-HFRC increase, with the optimal mixing ratio of PSF and SF being 1:3.Combined with the SEM and nanoindentation test results, it can be found that the bonding ability of PSF to the substrate is stronger, and the interface zone between PSF and the substrate is 10um smaller than that between sf and the substrate. It can be concluded that it is difficult for PSF to fully replace steel fiber in referencing UHPC, while hybrid fibers should be a good choice to produce excellent UHPC composite.

Predict the compressive strength of ultra high-performance concrete by a hybrid method of machine learning

Ultra-high performance concrete (UHPC) benefits the construction industry due to its improved flexibility, high workability, durability, and performance compared to normal concrete. Some investigators have conducted observed papers on the UHPC’s mechanical properties for establishing a reliable analytical approach for calculating the compressive strength, tensile strength, slump, etc. However, most of these studies were performed with limited samples because of the UHPC’s high cost. This study aims to predict the compressive strength (CS) of UHPC through hybrid machine-learning approaches. The model is included Adaptive-Network Fuzzy Inference System (ANFIS). Moreover, three meta-heuristic algorithms were employed to improve the developed model's accuracy, including the Generalized Normal Distribution Optimization, the COOT optimization algorithm, and the Honey Badger Algorithm. Several metrics were used to compare and assess the performance of the hybrid models in the framework of ANGN, ANCO, and ANHB. A comparison of the predicted and measured results generally shows that the proposed developed models can reasonably estimate the mechanical properties of UHPC. The results indicated that the ANHB model could estimate the CS of UHPC with the most suitable accuracy.

From machine learning to semi-empirical formulas for estimating compressive strength of Ultra-High Performance Concrete

Although some machine learning (ML) models have successfully developed for ultra-high-performance concrete (UHPC), they do not provide insights and explicit relations between all input variables and its compressive strength. This paper will address these ambiguities and provide a tool to predict the compressive strength by explainable and interpretable equations and plots. Explicit semi-empirical formulas are derived from a multivariate polynomial regression (Lasso) and automated feature engineering and selection (Autofeat) using a 810 dataset of UHPC with 15 input variables, which are collected from literature. Coefficient of determination R2 = 0.8223 is the same for the first-order degree (linear regression) of both models, however, the Autofeat achieves a better result than Lasso for the third-order degree with R2 = 0.9616 vs. R2 = 0.9503. A comprehensive parametric study is carried out via relative feature importance and partial dependence plots to explain and gain profound insights into the effects of some important input variables on the compressive strength of UHPC. Some details discussions related to these effects with previous studies are also presented. The proposed models not only show better performance with those from reference in terms of R2, especially for Autofeat model but also have explicit relations of compressive strength with 15 input variables. Hence, they can be used as a reliable tool in mixture design optimization of the UHPC.

Ensemble XGBoost schemes for improved compressive strength prediction of UHPC

XGBoost is a promising machine learning method capable of predicting essential concrete properties and enhancing advanced concrete design. However, its underlying version still requires further study and development. In this investigation, the effectiveness of advanced XGBoost versions, including Ada-XGBoost, Bagging-XGBoost, Stacking-XGBoost, and Voting-XGBoost, to predict the compressive strength (CS) of Ultra-high-performance concrete (UHPC) was accessed. A database covering 810 results from in the literature, including 15 inputs, such as 12 UHPC components, two curing conditions, and sample age, was utilized for training the models. The performance criteria for the five models, including RMSE, MAE, and R2, were evaluated using a combination of 10-Fold CV and Monte Carlo (MC) simulation. The results showed that the Stacking-XGBoost and XGBoost models outperformed other models in terms of prediction accuracy for the CS of UHPC. Based on SHAP values analysis, features such as age, fiber, slag, cement, sand, superplasticizer, water, relative humidity, and temperature were identified as the key parameters affecting UHPC’s CS. Furthermore, a quantitative analysis of their combined impact on UHPC's CS was also provided.

Preparation of sustainable ultra-high performance concrete (UHPC) with ultra-fine glass powder as multi-dimensional substitute material

High cost and CO2 emissions are the issues that need to be addressed in the widespread application of UHPC. The application of mineral admixtures in UHPC has become a new development direction. Ultra-fine glass powders with median diameter d50 of 316 nm (named as nGP) and 5.33 μm (named as mGP) were fabricated to replace silica fume and blast furnace slag, respectively, to prepare eco-friendly UHPC. The outcomes of the study revealed that both nGP and mGP can significantly promote the hydration of UHPC pastes. The incorporation of ultra-fine GP increased the hydration products and improved the microstructure of UHPC due to its pore filling effect, nucleation effect and pozzolanic activity. The compressive strength of UHPC containing ultra-fine glass powders was larger than the reference ones and the optimal substitution rates of silica fume by nGP and blast furnace slag by mGP were 60% and 50%, respectively. Ultra-fine GP is feasible to be employed as multi-dimensional substitute material to produce low cost eco-friendly UHPC.

The effects of calcium content of fly ash on hydration and microstructure of ultra-high performance concrete (UHPC)

In this study, the effects of low-calcium fly ash (LFA) and high-calcium fly ash (HFA) on C–S–H structure, hydration products, hydration heat, and compressive strength of UHPC paste/concrete were investigated and compared via XPS, MIP, FT-IR, 29Si NMR, XRD, SEM, and the thermal analysis method. The results showed that fly ash reduced the rate of exothermic hydration and early strength, but it increased the compressive strength and reduced the porosity in the middle and late ages, and denied the microstructure of UHPC. By contrast, HFA exhibited the higher band fluctuation of Si–O and Al–O and lower Ca2p-Si2p energy separation values, which indicated that more amorphous silicon oxide and alumina participated in the pozzolanic reaction. This also demonstrated that HFA played an important role in enhancing the polymerization degree and increasing a higher level of Al-substitution for Si in C–S–H than LFA. HFA enhanced the pore volume with diameters less than 10 nm from 28 to 90 days due to pozzolanic reaction and filling effect, whereas the LFA mainly played a filling role and a lower pozzolanic reaction during this period. Durability results showed that fly ash reduced the drying shrinkage and chloride impermeability coefficient of UHPC, and the effect of HFA was better than that of LFA.

Effect of Hollow 304 Stainless Steel Fiber on Corrosion Resistance and Mechanical Properties of Ultra-High Performance Concrete (UHPC).

This study investigated the effect of hollow 304 stainless-steel fiber on the corrosion resistance and mechanical properties of ultra-high-performance concrete (UHPC), and prepared copper-coated-fiber-reinforced UHPC as the control group. The electrochemical performance of the prepared UHPC was compared with the results of X-ray computed tomography (X-CT). The results reveal that cavitation can improve the distribution of steel fibers in the UHPC. Compared with solid steel fibers, the compressive strength of UHPC with hollow stainless-steel fibers did not exhibit significant change, but the maximum flexural strength increased by 45.2% (2 vol% content, length-diameter ratio of 60). Hollow stainless-steel fiber could better improve the durability of UHPC compared with copper-plated steel fiber, and the gap between the two continued to increase as the durability test progressed. After the dry-wet cycle test, the flexural strength of the copper-coated-fiber-reinforced UHPC was 26 MPa, marking a decrease of 21.9%, while the flexural strength of the UHPC mixed with hollow stainless-steel fibers was 40.1 MPa, marking a decrease of only 5.6%. When the salt spray test had run for seven days, the difference in the flexural strength between the two was 18.4%, but when the test ended (180 days), the difference increased to 34%. The electrochemical performance of the hollow stainless-steel fiber improved, owing to the small carrying capacity of the hollow structure, and more uniform distribution in the UHPC and lower interconnection probability were achieved. According to the AC impedance test results, the charge transfer impedance of the UHPC doped with solid steel fiber is 5.8 KΩ, while that of the UHPC doped with hollow stainless-steel fiber is 8.8 KΩ.

Ultra-high performance concrete prepared with ceramic polishing waste: Hydration, microstructure and mechanical property

This study aims to clarify the effects of ceramic polishing waste (CPW) on the hydration properties, microstructure development and mechanical properties of ultra-high performance concrete (UHPC). In present work, CPW was used as an auxiliary cementitious material to replace cement(C), fly ash (FA) and silica fume (SF). Firstly, the pozzolanic activity of CPW was examined via Frattini test method. Then, the hydration kinetics of UHPC with CPW was characterized by isothermal calorimetry, low-field NMR and chemical shrinkage. The hydration product and microstructure of UHPC with CPW were also studied by XRD, TGA and SEM. Finally, the effect of CPW substitute on workability and mechanical properties of UHPC was investigated. Results showed that CPW performed excellent pozzolanic activity, and it could participate in the hydration reaction at the late age. When the replacement ratio of cement with CPW was less than 20%, the compressive strength of UHPC was slightly decreased in the early stage, however, the strength could be comparable to that of UHPC without CPW at 56 d. Besides, it is noteworthy that the compressive strength of all UHPC with CPW exceeded 120 MPa at 28 d. This paper developed a novel application scenario for CPW that would help alleviate the problem of tight supply of raw materials such as SF and FA.

Ductility evaluation and flexural failure mode recognition of reinforced Ultra-High performance concrete flexural members

Although ultra-high performance concrete (UHPC) exhibits material tensile ductility far in excess of that expected from conventional or high-strength concretes, structural UHPC members typically fail in flexure due to reinforcement rupture after crack localization, which may result in a low structural ductility. In the flexural design of reinforced UHPC (R-UHPC) members, attention should be paid to addressing the issue related to the low structural ductility due to crack localization. This study firstly develops a two-dimensional numerical model to simulate the flexural response of R-UHPC beams, and the numerical results agree well with the experimental results of the load–deflection curves as well as the failure modes. Parametric analyses are then carried out to examine the effect of the flexural reinforcement ratio, tensile strength of steel reinforcement, tensile behavior of UHPC and compressive strength of UHPC on the flexural failure mode and ductility behavior of R-UHPC members. The results indicate that when the failure is controlled by fracture of tensile steel bars after crack localization, higher reinforcing ratio, higher steel post-yield hardening strength, and lower steel fiber volume lead to more ductile behavior. When the failure is controlled by crushing of UHPC, however, higher reinforcing ratio and lower UHPC compressive strength lead to a lower ductility. In addition, a flexural failure mode recognition method is proposed for R-UHPC flexural members, whose accuracy is verified based on the available experimental results of 105 R-UHPC flexural members. Finally, a minimum flexural reinforcement ratio is proposed to avoid low structural ductility in R-UHPC flexural members.

Comparison of the Mechanical Properties and Crack Expansion Mechanism of Different Content and Shapes of Brass-Coated Steel Fiber-Reinforced Ultra-High-Performance Concrete

Steel fiber-reinforced ultra-high-performance concrete (UHPC) is becoming an important type of concrete reinforcement. After mixing with the reinforced steel fibers, the UHPC has perfect flex resistance, shear strength, crack resistance, shock resistance, and anti-seepage. In this study, the influence of straight, corrugated, and hooked brass-coated steel fibers (BCSFs) on the microstructure, mechanical properties, and crack expansion mechanism of ultra-high-performance concrete (UHPC) with varying content of 1-6 wt.% under different curing times were investigated. Field emission scanning electron microscopy and energy dispersive X-ray spectrometry were employed to characterize the microstructure of the BCSF-reinforced UHPC mix specimens. X-ray computed tomography was employed to determine the porosity of the BCSF-reinforced UHPC mix specimens. The obtained results indicate the flexural strength and compressive strength of BCSF-reinforced UHPC mix specimens are enhanced, along with increasing the content of BCSFs reinforcement with different shapes (straight, corrugated, and hooked). The embedded BCSFs play a major role in the adhesive property and stress transfer of the BCSFs-UHPC matrix interface. Different from many studies, the flexural strength of mix UHPC with straight BCSFs is higher than those with corrugated and hooked BCSFs. However, the compressive strength of UHPC with corrugated BCSFs is higher than those with straight and hooked BCSFs. The flexural strength of mix UHPC with 6 wt.% straight BCSFs at 28 days reaches the maximum value of 26.2 MPa, and the compressive strength of UHPC with 6 wt.% corrugated BCSFs at 28 days reaches the maximum value of 142.3 MPa. With the increase in straight BCSF content from 1 wt.% to 6 wt.%, the porosity in mix UHPC reduces gradually from 18.4% to 8.3%. The length of average crack spacing is dependent on the straight BCSF content. With the increase in straight BCSF content from 1 wt.% to 6 wt.%, the average crack length reduces gradually from 34.2 mm to 12.1 mm, and the average crack width reduces gradually from 0.78 mm to less than 0.1 mm. During crack extension, part of the energy in the UHPC mixture specimen with the 6 wt.% BCSF content flows into the crack tip region converted into the work dissipated during the bridging process. The crack propagation resistance of the UHPC mixture with straight BCSFs was improved compared with those with corrugated and hooked BCSFs. The bond strength between the BCSFs and UHPC matrix was enhanced by using vibrational mixing, and the brass film coated on the BCSFs contributes to increase the flexural and compressive strength of the UHPC mixture.

Mechanical Strength and Microstructure of Ultrahigh-Performance Concrete under Long-Term Autoclaving

Solar thermal energy is a technology that collects radiant energy from the sun to generate heat or electricity. Thermal energy storage is required to compensate for the solar thermal energy variations across time scales, and a popular strategy is storing thermal energy in hot water tanks. Due to superior strength and durability, ultrahigh-performance concrete (UHPC) is exploited to construct such water tanks for applications at 200°C. Therefore, the mechanical strength and microstructure of UHPC after long-term temperature-pressure load (autoclaving) is studied. The compressive strength of UHPC can stay robust due to the accelerated formation of hydrates, while the flexural strength is vulnerable to long-term autoclaving due to the transformation of amorphous calcium silicate hydrate (C-S-H) to more ordered phases. The main hydrates in autoclaved samples are poorly crystallized C-S-H, hydroxylellestadite, and hydrogarnet. The porosity of autoclaved samples is not strictly related to the mechanical strength, and the influence of hydrate assemblage outweighs that of porosity on mechanical strength after long-term autoclaving. The partial replacement of cement by limestone powder can decrease crystalline hydrates and increase poorly crystallized C-S-H, which densifies the microstructure and enhances the mechanical strength. However, excessive poorly crystallized C-S-H aggravates the thermal mismatch between the matrix and quartz aggregates after autoclaved samples cool to room temperature, leading to interstice in matrix-quartz interfaces and, thus, reduced mechanical strength. Therefore, an appropriate addition of limestone powder is required to induce the hydrate assemblage with appropriate poorly crystallized C-S-H, ensuring durable UHPC structures under autoclaving.

Advanced machine learning algorithms to evaluate the effects of the raw ingredients on flowability and compressive strength of ultra-high-performance concrete.

The estimation of concrete characteristics through artificial intelligence techniques is come out to be an effective way in the construction sector in terms of time and cost conservation. The manufacturing of Ultra-High-Performance Concrete (UHPC) is based on combining numerous ingredients, resulting in a very complex composite in fresh and hardened form. The more ingredients, along with more possible combinations, properties and relative mix proportioning, results in difficult prediction of UHPC behavior. The main aim of this research is the development of Machine Learning (ML) models to predict UHPC flowability and compressive strength. Accordingly, sophisticated and effective artificial intelligence approaches are employed in the current study. For this purpose, an individual ML model named Decision Tree (DT) and ensembled ML algorithms called Bootstrap Aggregating (BA) and Gradient Boosting (GB) are applied. Statistical analyses like; Determination Coefficient (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) are also employed to evaluate algorithms' performance. It is concluded that the GB approach appropriately forecasts the UHPC flowability and compressive strength. The higher R2 value, i.e., 0.94 and 0.95 for compressive and flowability, respectively, of the DT technique and lesser error values, have higher precision than other considered algorithms with lower R2 values. SHAP analysis reveals that limestone powder content and curing time have the highest SHAP values for UHPC flowability and compressive strength, respectively. The outcomes of this research study would benefit the scholars of the construction industry to quickly and effectively determine the flowability and compressive strength of UHPC.

Effect and mechanism of composite early-strength agents on sulfoaluminate cement-based UHPC

Some major construction projects require materials that can quickly and easily obtain high mechanical properties. Nowadays sulfoaluminate cement (SAC) has attracted attention for such projects due to its fast hardening rate, high early strength, and low alkalinity. Added early strength agents are commonly used to improve the early strength of Ultra-high performance concrete (UHPC). Most early-strength agent research has focused on Portland cement systems. Due to the different components of SAC and Portland cement, the compatibility between different early strength agents and SAC needs to be further explored.This paper selected three inorganic early-strength agents, calcium formate (Ca(HCOO)2), aluminum sulfate (Al2(SO4)3), and lithium carbonate (Li2CO3), and an organic early-strength agent, triethanolamine (TEA), which are commonly used. Experimental studies were carried out for single and compound early-strength agent systems in a SAC-based Ultra-high performance concrete (UHPC) system with a low water-cement ratio of 0.23. The results showed three inorganic early-strength agents reduced the setting time and improved the strength of the UHPC. The 4 h compressive strength of UHPC with 0.4 % Ca(HCOO)2 increased from 22.1 MPa to 41.2 MPa due to the improved microstructure and denser matrix.The composite mixture of Ca(HCOO)2 and Li2CO3 had a good synergistic effect in the early and middle stages, the compressive strength can reach 88.7 when curing 3 days. Different from Portland cement system, TEA had an adverse effect in the both single and compound systems.

Prediction of compressive strength and elastic modulus for ultra-high-performance concrete

This study conducted compression tests to assess the behavior of ultra-high-performance concrete (UHPC) subjected to uniaxial compression. The primary test parameter is the fiber volume fraction in the 0 % to 2 % range. According to the test results, the presence of steel fibers in the matrix significantly affected the failure mode of UHPC. However, the compressive behavior of the test specimens appeared to be very similar, regardless of the fiber volume fraction. The compressive strength of UHPC was improved by approximately 10 % by the addition of the steel fibers. However, the fiber volume fraction and transverse strain had a strong association. The steel fibers significantly affected the volume change of the UHPC. Based on the test results and numerous test data sets, this study suggests that the compressive strength of the UHPC with steel fibers is a function of the UHPC compressive strength without steel fibers and design compressive strength. In addition, the elastic modulus for design is suggested. The proposed equations are applicable for the design and analysis of the UHPC with a fiber volume fraction of up to 6 % and a compressive strength between 120 and 255.9 MPa.

Influence of sand grain size distribution and supplementary cementitious materials on the compressive strength of ultrahigh-performance concrete

ultrahigh-performance concrete is a type of advanced concrete that can improve the resilience and durability of concrete structures. The utilization of available supplementary cementitious materials is a critical step in saving energy and materials, as well as lowering the cost of concrete. This research investigated the impact of sand grain size distribution, supplementary cementitious materials, and curing regimes (CRs) on the compressive strength of ultrahigh-performance concrete. The findings showed that a 90-d strength of 175 MPa could be obtained with a microsilica volume of 5 % and without heat-curing. The highest strength was obtained after curing for 48 h at 60 °C, followed by 72 h at 90 °C. Moreover, when compared to natural grain size distribution sand, the usage of finer sand enhances compressive strength. When microsilica is added, however, this impact is minimized.

Sustainable use of recycled autoclaved aerated concrete waste as internal curing materials in ultra-high performance concrete

Great autogenous shrinkage has become a common problem in cement-based materials with a low water-to-binder ratio (w/b). To limit the autogenous shrinkage in ultra-high performance concrete (UHPC) during the process of hydration and curing, sustainable use of recycled micropowder (RMP) made from autoclaved aerated concrete waste (AACW) to cure UHPC internally is proposed in this paper. The influence of RMP (at the levels 5%, 10%, and 15% by mass) with three particle size classes ranging from 0 to 600 μm on the autogenous shrinkage, hydration reaction, internal relative humidity, and compressive strength of UHPC were investigated. The results show that mixtures with different dosages and particle sizes improve the fluidity of UHPC, and its internal curing effect can effectively inhibit the early-age autogenous shrinkage of UHPC and maintain a higher internal humidity compared with the control group. The incorporation of RMP can advance the appearance of a hydration peak and increase the cumulative heat of hydration. Microscale analyses show that the curing water released from RMP promotes the hydration of the unhydrated cement around RMP. The generated hydration product filled the surface pores of RMP and weakened the negative effect of the pores introduced by RMP on the compressive strength of UHPC. Thus, RMP can be utilized as internal curing materials for UHPC to reduce autogenous shrinkage.