Tajasosi, S., Barandoust, J. orcid.org/0000-0002-9196-2437, Saradar, A. orcid.org/0000-0003-4696-3382 et al. (3 more authors) (2025) Investigation of mechanical and fresh properties of ultra-high-performance concrete incorporating second-generation superplasticizers. Applied Sciences, 15 (9). 5133. ISSN 2076-3417
Abstract
Ultra-high-performance concrete (UHPC) has been following economic and environmental trends for the past two decades. Limited research has been conducted on the significance of superplasticizers in UHPC products, despite the high costs they entail for projects. The current study assesses UHPC based on rheological properties and mechanical characteristics considering different factors. In this study, the effects of different levels of superplasticizer derived from sulfonated naphthalene formaldehyde (SNF: 0.7%, 0.8%, and 0.9%), silica fume (SF: 15%, 20%, and 25%), and the water-to-binder ratio (w/b: 0.18, 0.20, and 0.22) were examined. Fresh tests such as slump flow, Vicat needle, and squeezing, as well as hardened tests like compressive strength, flexural strength, and electrical resistivity, were conducted. In the analysis, an artificial neural network (ANN) model and a fuzzy logic (FL) model were employed to forecast compressive strength results at 7 and 28 days. The results indicated that a higher SF dosage reduced slump flow and set time, whereas the opposite was observed for SNF and the w/b ratio. Three distinct behaviors were identified in the squeezing flow test findings: (1) specific elastic behavior and low plasticity, (2) extensive plastic behavior and significant dilatancy, and (3) heightened responsiveness to compressive flow rate and material ratio. SNF demonstrated promise in enhancing compressive, flexural, and electrical strength. The prediction models suggested that the FL (error range 3.18–4.36%) and ANN (0.74–1.03%) models performed well in predicting compressive strength at 7 and 28 days. The encouraging findings from this study set the stage for further sustainable and cost-effective construction methods.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | ultra-high-performance concrete; superplasticizers; squeeze flow; mechanical properties; artificial neural networks; fuzzy logic |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Mechanical, Aerospace and Civil Engineering |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Jun 2025 11:54 |
Last Modified: | 16 Jun 2025 11:54 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/app15095133 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:227869 |