An analysis of the steam curing and autoclaving process parameters for reactive powder concretes

Zdeb, Tomasz

doi:10.1016/j.conbuildmat.2016.11.026

Cited by 67 publications

(26 citation statements)

References 15 publications

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“…Then, the neurons in the hidden layer calculate a weighted sum of the input data, processes the sum by using an activation function, and finally pass the activation results to the output layer. The weighted sum of the input data can be calculated by Equation (1) [43][44][45][46].…”

Section: Artificial Neural Network Approachmentioning

confidence: 99%

“…The UHPC with steel fibers is also called ultra-high performance steel fiber reinforced concrete (UHPFRC). The steel fibers in UHPFRC improve the cohesive forces between fibers and matrix, change the granular skeleton, and increase the anchorage length between fibers and the surrounding matrix [1,2]. Besides, steel fibers bridge cracks and retard the propagation to increase…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Effects of Steel Fibers on Mechanical Properties of Ultra-High Performance Concrete Using Artificial Neural Networks

Cai

Chang

2018

Applied Sciences

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Featured Application: This work can be utilized to predict the flexural strength and the compressive strength of ultra-high performance concrete (UHPC), determine the volume fraction of steel fibers in ultra-high performance steel fiber reinforced concrete (UHPFRC), and optimize the UHPFRC mixtures.Abstract: Steel fibers enhance the flexural strength, the compressive strength and the ductility of untra-high performance concrete, predicting the flexural strength and the compressive strength of ultra-high performance steel fiber reinforced concrete (UHPFRC) accurately has significant influence on controlling steel fiber volume fraction and optimizing UHPFRC mix proportion. In this study, to evaluate the effects of steel fibers on the mechanical properties of UHPFRC, two artificial neural networks were developed in order to predict the flexural strength and the compressive strength of UHPFRC, respectively. 102 test data sets and 162 test data sets from literature were trained and tested to establish the flexural strength model and the compressive strength model, respectively. In these two models, the influential parameters, including the water to binder ratio, the diameter, the length, the aspect ratio, and the volume fraction of steel fibers, as well as the compressive strength and the flexural strength of concrete without fibers were investigated as the inputs, while the compressive strength and the flexural strength of UHPFRC were the outputs. The results show that the artificial neural network models predicted the compressive strength and flexural strength of UHPFRC accurately. Then, by comparing with existing analytical models, it was determined that the proposed models had high applicability and reliability with respect to predicting the compressive strength and the flexural strength of UHPFRC.the strength and the ductility of UHPFRC [3]. Unfortunately, too many steel fibers lead to fibers inter-wrap and interlock with each other, affecting the workability of UHPFRC, to reduce the strength of UHPFRC [4]. Furthermore, steel fibers are expensive and numerous steel fibers added to the UHPFRC cost too much. Thus, predicting the compressive strength and the flexural strength of UHPFRC accurately can optimize mix proportion, control the volume fraction of steel fibers, and decrease the costs of UHPFRC. However, evaluating the flexural strength and the compressive strength of the UHPFRC is a huge challenge due to the complex composite behavior caused by the properties steel fibers (diameter (D), length (L), aspect ratio (AR), and volume fraction (VF)) and concrete matrix (water to binder ratio (W/B) and concrete strength without fibers).Nowadays, the contribution of cement-based materials to sustainability is a topic of study [5-8] and the performance of several additions in cement-based materials, such as silica fume, fly ash, the water to cement ratio, and so on, has been analyzed, which could be also suitable for being used in high performance concrete. This fact also makes it necessary to consider their influence...

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Section: Artificial Neural Network Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating the Effects of Steel Fibers on Mechanical Properties of Ultra-High Performance Concrete Using Artificial Neural Networks

Cai

Chang

2018

Applied Sciences

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“…Hydrothermal treatment parameters were selected and justified on the basis of the author's research (Zdeb 2016). Generally, the conclusion of that research is that the effectiveness of these technological treatments with respect to the mechanical properties of UHPC composites increases with temperature both in the case of steaming and autoclaving.…”

Section: Detailed Research Programmementioning

confidence: 99%

“…This valuable healing under autoclaving conditions can be attributed to fibrous xonotlite and tobermorite microcrystals, which were observed during the analysis of the microstructure of the composite within the crack. As described in (Zdeb 2016), the length of the resulting whiskers depends on the temperature and pressure achieved during autoclaving. At a temperature of 250°C they can reach lengths of up to 20 μm.…”

Section: Autogenous Self-healing -Mechanical Propertiesmentioning

confidence: 99%

“…A linear relationship between stress and deformation is observed during both squeezing and bending until the composite reaches maximum load. In other words, UHPC matrix when is deprived of fibres hardly exhibits any plastic deformation, and the formation of a crack is associated with its immediate propagation, which leads to a sharp drop in recorded post-critical stress (Zdeb 2009(Zdeb , 2016. Thus the research programme presented below is based on the following assumptions: -the effect of autogenous healing of ultra-high performance composite will be observed on specimens equipped with counter-weights and preloaded during 3-point notch bending up to deflection where the values of post-critical stress were negligible (200 µm); -the self-healing effect is due to the low value of the water-binder ratio typical of this group of materials, which results in a low degree of cement hydration, allowing the relict clinker grains on the fracture surface of the specimen to further react with water.…”

Section: Introductionmentioning

confidence: 99%

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Autogenous Healing Effect of Ultra-High Performance Cementitious Composites

Zdeb

2018

ACT

Self Cite

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This paper presents the results of research concerning the potential ability of ultra-high performance cementitious composites to self-heal the cracks that appear at various curing stages, and the effect of additional hydrothermal treatment on this ability. Test specimens were fractured in a 3-point bending test. The preload was induced during the initial curing period, i.e. 24 hours after the mix was made, and also in matured materials: concrete that was cured for 28 days in water or subjected to low-pressure steaming or autoclaving. Flexural strength and total fracture energy tests were carried out on reference specimens and also on specimens with a crack healed by immersion in water and by the additional use of steaming at 90°C and autoclaving at 250°C. The results of studies performed with respect to mechanical properties and microstructure observations show that material that was cracked at early curing stages can be most effectively healed (i.e. its strength restored) by the autoclaving process. The main reason for this is the crystallisation of xonotlite and tobermorite whiskers in the healed crack. The self-healing effect tested in matured composites where binder hydration was determined to be of around 40% turned out to be virtually negligible.

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Effects of key parameters on fluidity and compressive strength of ultra‐high performance concrete

Chang

Zheng

2019

Structural Concrete

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The compositions and curing regimes have significant effects on the compressive strength and the fluidity of ultra-high performance concrete (UHPC). Fifty-eight mixtures were examined to determine the effects of key factors on the fluidity and the compressive strength of UHPC. These parameters include water-binder ratio, cement strength, steel fiber content, fly ash content, nanoparticles content, silica fume grade, and curing regimes. The fluidity of UHPC was in the ranges 122-237 mm, while the 28d compressive strength on 70.7 mm cube specimens of UHPC were in the ranges 77-211 MPa. The results showed that fly ash and nanoparticles had significant effects on the fluidity and slight effects on the compressive strength; silica fume grade had slight effects on both the compressive strength and the fluidity; the water to binder ratio, curing regimes, and steel fiber had noticeable effects on the compressive strength. Based on results, the influence coefficients of the investigated key parameters were obtained by regression analysis. Furthermore, the prediction models of the fluidity and the compressive strength of UHPC were proposed by combining the influence coefficients of the investigated key parameters. Therefore, the prediction models can be utilized to optimize the proportions of UHPC mixtures within the ranges of the investigated parameters. K E Y W O R D Scompressive strength, fluidity, key parameters, prediction model, ultra-high performance concrete

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An analysis of the steam curing and autoclaving process parameters for reactive powder concretes

Cited by 67 publications

References 15 publications

Evaluating the Effects of Steel Fibers on Mechanical Properties of Ultra-High Performance Concrete Using Artificial Neural Networks

Evaluating the Effects of Steel Fibers on Mechanical Properties of Ultra-High Performance Concrete Using Artificial Neural Networks

Autogenous Healing Effect of Ultra-High Performance Cementitious Composites

Effects of key parameters on fluidity and compressive strength of ultra‐high performance concrete

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