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Record W6939451372 · doi:10.60692/dy8f9-3qq05

Strength analysis of concrete pavement deformation due to Alkali Silica Reaction (ASR)

2020· article· en· W6939451372 on OpenAlexaboutno aff

Bibliographic record

VenueGreater South Information System · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and construction materials studies
Canadian institutionsnot available
Fundersnot available
KeywordsCompressive strengthFlexural strengthDeformation (meteorology)CementCube (algebra)Alkali–silica reactionProperties of concrete

Abstract

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Alkali Silica Reaction (ASR) is a chemical reaction that negatively affects concrete pavements strengths and integrity. ASR impedes concrete pavements' performance due to the formation of cracks and ultimate deformation if not properly controlled. Concrete pavements are gaining more relevance due to their ability to be constructed on soils with low bearing capacity and support high traffic loadings, thus increasing the need for studies on how ASR in the concrete pavements can be mitigated. This study employed compressive and flexural strength tests to determine the strength properties and deformation of concrete pavements due to ASR when partially replaced with CBA at varying percentages. Static structural modelling of the concrete as a multiphase material in which aggregates, cracks and gel formations are considered as embedded inclusions in the cement paste is then carried out. The results are then compared with relevant standards and findings of other researchers. The study's findings reveal that all the concrete cube samples passed the recommended compressive strength for rigid pavement, which range from 35 - 40 N/mm2 at 28th day. The concrete cube samples also passed the target strength of 48.25 N/mm2 obtained from the mix design. The effect of ASR resulted in lower compressive and flexural strengths observed at 180th and 240th days with lower CBA addition, while samples containing higher CBA contents had increasing compressive strength. The static structural modelling results reveal that the maximum deformation was obtained for the concrete cubes admixed with 0% CBA with 47.045 mm while the least deformation was obtained at 30% CBA replacement with deformation value of 5.542 mm on application of a 900 KN force. Therefore, the study posits that CBA addition will help reduce Portland Cement Concrete Pavement deformation due to ASR in relation to traffic loadings. Cite as: Adanikin A, Falade F, Olutaiwo A. Strength analysis of concrete pavement deformation due to Alkali Silica Reaction (ASR). Alg. J. Eng. Tech. 2020; 3: 020-027. http://dx.doi.org/10.5281/zenodo.4400227 References Hajighasemali S, Ramezanianpour A, Kashefizadeh M. The effect of alkali–silica reaction on strength and ductility analyses of RC beams. Magazine of concrete research. 2014;66(15):751-760. Grimal E, Sellier A, Multon S, Le Pape Y, Bourdarot E. Concrete modelling for expertise of structures affected by alkali aggregate reaction. Cement and Concrete Research. 2010 ;40(4):502-507. Monette LJ, Gardner NJ, Grattan-Bellew PE. Residual strength of reinforced concrete beams damaged by alkali-silica reaction—Examination of damage rating index method. Materials Journal. 2002 ;99(1):42-50. Huaquan YA, Zhen LI, Meijuan RA, Xiaomei SH. Study on Influence of Aggregate Combination and Inhibition Material ofAlkali-silica Reaction in Fully-Graded Concrete. Materials Science. 2020;26(3):363-372. Malhotra VM, Mehta PK. High-performance, high-volume fly ash concrete: materials, mixture proportioning, properties, construction practice, and case histories. Supplementary Cementing Materials for Sustainable Development, Incorporated. Ottawa Canada, 2002: 101p. Falade F, Ikponmwosa E, and Fapohunda C. Potential of Pulverized Bone as a Pozzolanic material. International Journal of Scientific & Engineering Research. 2012; 3(7): 1-6. Evon, D. Is This 'Goliath Skeleton' Real? Retrieved from: https://www.snopes.com/fact-check/is-this-goliath-skeleton-real/; (2018). BS 1881-116. Testing concrete. Method for determination of compressive strength of concrete cubes. 1983. ASTM, C78M. Standard Test Method for Flexural Strength of Concrete (Using Simple Beam with Third-Point Loading). ASTM International, West Conshohocken, PA. 2018. Ahmed T, Burley E, Rigden S, Abu-Tair AI. The effect of alkali reactivity on the mechanical properties of concrete. Construction and Building Materials. 2003;17(2):123-144. Smaoui N, Berube MA, Fournier B, Bissonnette B, Durand B. Effects of alkali addition on the mechanical properties and durability of concrete. Cement and concrete research. 2005;35(2):203-12. Ankit K. Kisku N. Effect of silica fume and fly ash as partial replacement of cement on strength of concrete. International Journal of Innovative Research in Science, Engineering and Technology. 2016;5(10), 18618 – 18624. Subbaramaiah G, Sudarsana HR, Vaishali GG. Effect of addition and partial replacement of cement by wood waste ash on strength properties of structural grade concrete. International Journal of Innovative Science, Engineering & Technology. 2015; 2(9): 736-743 Olutaiwo AO, Yekini OS, Ezegbunem II. Utilizing Cow Bone Ash (CBA) as partial replacement for cement in highway rigid pavement construction. SSRG International Journal of Civil Engineering. 2018; 5(2): 13-19. Adanikin A, Falade F, Olutaiwo AO, Faleye ET. Ajayi AJ. Investigation of the effect of Alkali-Silica Reaction (ASR) on Properties of Concrete Pavement Admixed with Cow Bone Ash (CBA) by Electrical Resistivity Method. IOP Conf. Series: Materials Science and Engineering. 2019, 640(1): 1-9 Kadyali LR. Lal NB. Principles and practices of highway engineering including expressways and airport engineering. (Khanna Publishers, New Delhi). 2014. Marzouk H. Langdon S. The effect of alkali aggregate reactivity on the mechanical properties of high and normal strength concrete. Cement and Concrete Composites. 2003;25: 549–556. Giaccio G, Zerbino R, Ponce JM, Batic OR. Mechanical behavior of concretes damaged by alkali-silica reaction. Cement and Concrete Research. 2008;38(7):993-1004.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.184
Teacher spread0.165 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
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