The Top-Bar Effect in Specimens with a Single Casting Point at One Edge in High-Performance Self-Compacting Concrete

Dybeł, Piotr; Wałach, Daniel; Ostrowski, Krzysztof

doi:10.3151/jact.16.282

Cited by 11 publications

(3 citation statements)

References 22 publications

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“…Silica fume is a by-product of the silicon metals production. It is used in the production of additives for cement mortars, industrial concrete, insulation materials, ceramic products, products with an increased resistance to high temperature and high-performance self-compacting concrete [ 29 ]. It has been reported that industrial waste and numerous artificial and natural fibers can be successfully utilized to enhance the properties of concrete [ 30 , 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms

Ahmad

Chaiyasarn

et al. 2021

Polymers

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The innovation of geopolymer concrete (GPC) plays a vital role not only in reducing the environmental threat but also as an exceptional material for sustainable development. The application of supervised machine learning (ML) algorithms to forecast the mechanical properties of concrete also has a significant role in developing the innovative environment in the field of civil engineering. This study was based on the use of the artificial neural network (ANN), boosting, and AdaBoost ML approaches, based on the python coding to predict the compressive strength (CS) of high calcium fly-ash-based GPC. The performance comparison of both the employed techniques in terms of prediction reveals that the ensemble ML approaches, AdaBoost, and boosting were more effective than the individual ML technique (ANN). The boosting indicates the highest value of R2 equals 0.96, and AdaBoost gives 0.93, while the ANN model was less accurate, indicating the coefficient of determination value equals 0.87. The lesser values of the errors, MAE, MSE, and RMSE of the boosting technique give 1.69 MPa, 4.16 MPa, and 2.04 MPa, respectively, indicating the high accuracy of the boosting algorithm. However, the statistical check of the errors (MAE, MSE, RMSE) and k-fold cross-validation method confirms the high precision of the boosting technique. In addition, the sensitivity analysis was also introduced to evaluate the contribution level of the input parameters towards the prediction of CS of GPC. The better accuracy can be achieved by incorporating other ensemble ML techniques such as AdaBoost, bagging, and gradient boosting.

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Section: Introductionmentioning

confidence: 99%

Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms

Ahmad

Chaiyasarn

et al. 2021

Polymers

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“…In comparison to ordinary concretes, the designing process of HPSCC mix is determined by the increased cement content, superplasticizers, and an additive of reactive materials, i.e., silica fume. HPSCC is thus characterized by its ability to fill a form with congested steel rebars and self-leveling without mechanical compaction and it yields exceptionally high strength and durability [ 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Assessment of High Performance Self-Consolidating Concrete through an Experimental and Analytical Multi-Parameter Approach

Ahmed

Ali

et al. 2021

Materials

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High-performance self-consolidating concrete is one of the most promising developments in the construction industry. Nowadays, concrete designers and ready-mix companies are seeking optimum concrete in terms of environmental impact, cost, mechanical performance, as well as fresh-state properties. This can be achieved by considering the mentioned parameters simultaneously; typically, by integrating conventional concrete systems with different types of high-performance waste mineral admixtures (i.e., micro-silica and fly ash) and ultra-high range plasticizers. In this study, fresh-state properties (slump, flow, restricted flow), hardened-state properties (density, water absorption by immersion, compressive strength, splitting tensile strength, flexural strength, stress-strain relationship, modulus of elasticity, oven heating test, fire-resistance, and freeze-thaw cycles), and cost of high-performance self-consolidating concrete (HPSCC) prepared with waste mineral admixtures, were examined and compared with three different reference mixes, including normal strength-vibrated concrete (NSVC), high-strength self-compacted concrete (HSSCC), and high-performance highly-viscous concrete (HPVC). Then, a multi parameter analytical approach was considered to identify the optimum concrete mix in terms of cost, workability, strength, and durability.

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“…For instance, in a study conducted on 1500 mm high columns, [12] made of normal and self-compacting concretes with a varying water-to-cement ratio, the reduction of ultimate bond stress for self-compacting concretes ranged between 32%-55% and from 60% to 74% for normal concretes. However, it has been noted [20] that modification of self-compacting concrete with silica fume in an adequate amount-less than 10%-in relation to the cement mass practically eliminates bond reduction along the height.…”

Section: Introductionmentioning

confidence: 99%

Effect of Placing Self-Compacting Concrete Bottom up on the Quality of Bond Conditions

Kucharska

Dybeł

2020

Materials

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A self-compacting concrete (SCC) mixture, due to its special rheological properties, may be placed differently than in traditional variants. The paper presents the results of a study on the effect of a bottom-up placing direction on the quality of bond conditions between steel and self-compacting concrete. Elements with overall dimensions of 160 × 480 × 1600 mm consisting of elementary samples with dimensions of the bond test basic modules were constructed. Ribbed steel rebars with diameters of 16 mm were used and located in formworks perpendicularly to the concrete placing direction. Bond tests were carried out according to the pull-out method. The bottom-up placing contributed to the uniformity of the bond conditions quality in the test elements and an overall improvement of the bond properties, especially in its top part. Given the increasing implementation of alternative methods of mix placing and the promising results, the topic should be further investigated.

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The Top-Bar Effect in Specimens with a Single Casting Point at One Edge in High-Performance Self-Compacting Concrete

Cited by 11 publications

References 22 publications

Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms

Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms

Assessment of High Performance Self-Consolidating Concrete through an Experimental and Analytical Multi-Parameter Approach

Effect of Placing Self-Compacting Concrete Bottom up on the Quality of Bond Conditions

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