Compressive performance of all-grade engineered cementitious composites: Experiment and theoretical model

Ding, Yao; Yu, Kequan; Mao, Weihao

doi:10.1016/j.conbuildmat.2020.118357

Cited by 66 publications

(6 citation statements)

References 34 publications

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“…In the Pandas library, data cleaning and analysis features are applied. A total of 324 and 147 SM concrete mix specimens (Laboratory cast) were checked with various researchers' data for considering compressive strength and split tensile strength, respectively [5][6][7][8][9][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. The available categorical parameters used for the prediction model are noted in Table 1 and non-categorical mention in Table 2.…”

Section: 𝑍 𝑗"mentioning

confidence: 99%

“…In order to attain acceptable accuracies in the literature prediction of the SM strength [10,[13][14][15], several ANN models have been constructed for numerous source types or different admixture forms. These models are quite precise, but both they are exclusive to particular SM type, and the results of the experiment are acquired from a single lot of SM that has have very low noise levels and input data unpredictability [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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An artificial neural network for the prediction of the strength of supplementary cementitious concrete

Mahajan¹,

Bhagat²

2022

Res. Eng. Struct. Mater.

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Supplementary materials (SM) for cement replacement became more feasible in the previous decade due to their pozzolanic strength and durability properties. The strength variation according to the age of the binding material is a critical subject for SM concrete. The time of water curing is critical in order to maintain the pozzolanic reaction in SM concrete, which assists in the development of strength in cementitious properties.In this study, the laboratory results of concrete specimens were assessed for various mix designs, and the obtained corresponding strengths were also predicted with ANN techniques. The difference between experimental and ANN predicted values was marginally low. Thus, the ANN model applicability emphasised the productive use in predicting the strength of supplementary materials like fly ash or any similar pozzalanic mineral admixture. This research proposes an ANN-based technique for predicting the strength of fly ash added SM concrete. Typical experimental data is used to build, train, and test the artificial neural network (ANN) model. With ANN and input parameters, a total of 324 distinct data points for SM concrete were utilised to estimate SM concrete strength. Various combinations of layers, number of neurons, and learning rate were examined during the training phase. When the root mean square error (RMSE) reached or remained below 0.001, the training was stopped, and the findings were verified using a test data set. With respect to the relative error provided for trained model data, the results achieved were typically below 10% for compressive strength and below 5% for split tensile strength. The ANN models predict concrete strength with excellent accuracy, and the findings show that utilising ANNs to predict concrete strength is both practicable and advantageous.

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Section: 𝑍 𝑗"mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An artificial neural network for the prediction of the strength of supplementary cementitious concrete

Mahajan¹,

Bhagat²

2022

Res. Eng. Struct. Mater.

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“…The mixture proportion of reference HS-SHCC sample without NS incorporation (NS0) was designed according to previous works [8][9][10] and is illustrated in Table 1. The water-to-binder ratio and the PE fiber volume content were set constant at 0.14 and 2%, respectively.…”

Section: Specimens Preparationmentioning

confidence: 99%

“…Its tensile strain capacity can reach up to 3-5% under the assistance of polyvinyl alcohol (PVA) fibers [5][6][7] or polyethylene (PE) fibers. [8][9][10][11][12][13][14][15] Recently, the authors have successfully developed a cementitious material featuring both high strength and excellent ductility, combining the advantages of UHPC and SHCC simultaneously, [8][9][10] which is termed as high-strength strain-hardening cementitious composite (HS-SHCC) with compressive strength beyond 100 MPa, tensile strength larger than 15 MPa and tensile strain capacity of 6-10%.…”

Section: Introductionmentioning

confidence: 99%

Performance‐enhancement of high‐strength strain‐hardening cementitious composite by nano‐particles: Mechanism and property characterization

Ding

Mao

et al. 2021

Structural Concrete

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Nano-SiO 2 (NS), due to its prominent nucleation and pozzolanic effects, holds the promise to effectively enhance the tensile and compressive properties of highstrength strain-hardening cementitious composite (HS-SHCC) at both early age (1d, 3d) and normal curing age (28d). The influence of nano-SiO 2 (NS) on the hydration, microstructure and mechanical properties of HS-SHCC with different dosages (i.e., 0%, 1%, 2%, and 3% by mass of cement) were explored in this research. The nucleation effect of NS particles accelerated the hydration process, Ultra-high performance concrete (UHPC) with extremely low water-to-binder ratio has attracted increasing attention because of its high compressive strength and excellent durability. 1 Nonetheless, it is still challenging for UHPC to Discussion on this paper must be submitted within two months of the print publication. The discussion will then be published in print, along with the authors' closure, if any, approximately nine months after the print publication.

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“…Compared with ordinary concrete or conventional FRC, ECC exhibits much superior tensile strain capacity (typically 2-8%) under increasing tension (Li et al, 2002;Ye et al, 2021;Huang et al, 2022). In recent studies, some investigators developed ECC with tensile strain capacity ranging from 8% to 12%, tensile strength ranging from 4 MPa to 20 Mpa, and a compressive strength ranging from 20 Mpa to 120 Mpa (Yu et al, 2017;Yu et al, 2018a;Yu et al, 2020a;Ding et al, 2020). For the first time, a cementitious material showed comparable ductility to steel reinforcement, and it has been first used to build a frame structure, which succeeded in surviving nine major earthquake impacts (Yu et al, 2018b).…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation on seismic performance of plain engineered cementitious composite beams

et al. 2022

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The authors proposed using plain engineered cementitious composite (ECC) in construction without aligned reinforcement. In this study, ECC with a tensile strain capacity of up to 10% was produced, which has a comparable deformability level to steel reinforcement. To demonstrate the feasibility of plain ECC construction, five plain ECC beams and two steel reinforced concrete reference beams were prepared, and four-point bending reversed cyclic loading tests were conducted on the beam specimens. The experimental tests indicated that the plain ECC beams showed more stable hysteresis loops with improved energy dissipation capacity, deformation restoring capacity, and lower stiffness degradations than normal RC beams. Two categories of failure modes were observed, i.e., flexural failure mode and shear failure mode. With the increase in the shear span ratio, the failure mode of ECC beams changed from shear failure to flexural failure. Additionally, the increase in sectional dimension had a positive influence on the stiffness and energy dissipation capacity of ECC beams. It is experimentally verified that the plain ECC beams have sufficient load-bearing capacity under static and seismic loading.

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Compressive performance of all-grade engineered cementitious composites: Experiment and theoretical model

Cited by 66 publications

References 34 publications

An artificial neural network for the prediction of the strength of supplementary cementitious concrete

An artificial neural network for the prediction of the strength of supplementary cementitious concrete

Performance‐enhancement of high‐strength strain‐hardening cementitious composite by nano‐particles: Mechanism and property characterization

Experimental investigation on seismic performance of plain engineered cementitious composite beams

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