View in EDS

From Experimental Studies to Predictive Machine Learning Modelling: Polypropylene Fibre Reinforced Concrete

Saved in:
Bibliographic Details
Title: From Experimental Studies to Predictive Machine Learning Modelling: Polypropylene Fibre Reinforced Concrete
Authors: Bayat Pour, Mohsen
Contributors: Lund University, Faculty of Engineering, LTH, Departments at LTH, Department of Building and Environmental Technology, Division of Structural Engineering, Lunds universitet, Lunds Tekniska Högskola, Institutioner vid LTH, Institutionen för bygg- och miljöteknologi, Avdelningen för Konstruktionsteknik, Originator, Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: Circular Building Sector, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: Cirkulär byggindustri, Originator
Source: Results in Materials. 28
Subject Terms: Engineering and Technology, Civil Engineering, Building materials, Teknik, Samhällsbyggnadsteknik, Byggnadsmaterial, Mechanical Engineering, Solid and Structural Mechanics, Maskinteknik, Solid- och strukturmekanik
Description: This study investigates the influence of polypropylene fibres (PF) on concrete performance across a finely resolved dosage spectrum (0.0–2.0% by cement mass, in 0.1% increments) using 2,100 laboratory specimens. The experimental programme evaluated compressive strength, tensile strength (flexural and splitting), modulus of elasticity, and water penetration depth. Predictive modelling was conducted using Random Forests (RF) and Support Vector Regression (SVR), trained and evaluated with simple cross-validation and benchmarked using mean absolute error (MAE) and coefficient of determination (R²). The results reveal distinct optima for strength indices and a threshold behaviour in permeability, with PF dosages between 0.2% and 0.6% balancing mechanical enhancement and substantial reductions in water penetration, although accompanied by a pronounced reduction in elastic modulus at very low PF contents. The RF models exhibited superior predictive performance, consistently outperforming SVR across properties. The experimental outcomes demonstrate that incorporating PF into the concrete mixture enhances its mechanical properties. However, the optimal fibre-to-cement ratios differ for various properties: compressive strength (0.3% to 0.4%), tensile strength (0.2% to 0.4%), modulus of elasticity (0.1%), and permeability (0.6%). The overall optimal fibre range is identified as 0.1% to 0.6%, which satisfies all specified criteria. Notably, the inclusion of PF results in a 60% increase in compressive strength, a 115% increase in tensile strength (bending test), a 288% increase in tensile strength (Brazilian test), a tenfold reduction in modulus of elasticity, and a twenty-fivefold reduction in permeability.
Access URL: https://doi.org/10.1016/j.rinma.2025.100777
Database: SwePub
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://doi.org/10.1016/j.rinma.2025.100777#
    Name: EDS - SwePub (s4221598)
    Category: fullText
    Text: View record in SwePub
  – Url: https://resolver.ebscohost.com/openurl?sid=EBSCO:edsswe&genre=article&issn=2590048X&ISBN=&volume=28&issue=&date=20251007&spage=&pages=&title=Results in Materials&atitle=From%20Experimental%20Studies%20to%20Predictive%20Machine%20Learning%20Modelling%3A%20Polypropylene%20Fibre%20Reinforced%20Concrete&aulast=Bayat%20Pour%2C%20Mohsen&id=DOI:10.1016/j.rinma.2025.100777
    Name: Full Text Finder
    Category: fullText
    Text: Full Text Finder
    Icon: https://imageserver.ebscohost.com/branding/images/FTF.gif
    MouseOverText: Full Text Finder
  – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Pour%20B
    Name: ISI
    Category: fullText
    Text: Nájsť tento článok vo Web of Science
    Icon: https://imagesrvr.epnet.com/ls/20docs.gif
    MouseOverText: Nájsť tento článok vo Web of Science
Header DbId: edsswe
DbLabel: SwePub
An: edsswe.oai.portal.research.lu.se.publications.2b126449.fad7.48d0.a838.5b766cea11a9
RelevancyScore: 1124
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1123.89196777344
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: From Experimental Studies to Predictive Machine Learning Modelling: Polypropylene Fibre Reinforced Concrete
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Bayat+Pour%2C+Mohsen%22">Bayat Pour, Mohsen</searchLink>
– Name: Author
  Label: Contributors
  Group: Au
  Data: Lund University, Faculty of Engineering, LTH, Departments at LTH, Department of Building and Environmental Technology, Division of Structural Engineering, Lunds universitet, Lunds Tekniska Högskola, Institutioner vid LTH, Institutionen för bygg- och miljöteknologi, Avdelningen för Konstruktionsteknik, Originator<br />Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: Circular Building Sector, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: Cirkulär byggindustri, Originator
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <i>Results in Materials</i>. 28
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Engineering+and+Technology%22">Engineering and Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Civil+Engineering%22">Civil Engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Building+materials%22">Building materials</searchLink><br /><searchLink fieldCode="DE" term="%22Teknik%22">Teknik</searchLink><br /><searchLink fieldCode="DE" term="%22Samhällsbyggnadsteknik%22">Samhällsbyggnadsteknik</searchLink><br /><searchLink fieldCode="DE" term="%22Byggnadsmaterial%22">Byggnadsmaterial</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanical+Engineering%22">Mechanical Engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Solid+and+Structural+Mechanics%22">Solid and Structural Mechanics</searchLink><br /><searchLink fieldCode="DE" term="%22Maskinteknik%22">Maskinteknik</searchLink><br /><searchLink fieldCode="DE" term="%22Solid-+och+strukturmekanik%22">Solid- och strukturmekanik</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: This study investigates the influence of polypropylene fibres (PF) on concrete performance across a finely resolved dosage spectrum (0.0–2.0% by cement mass, in 0.1% increments) using 2,100 laboratory specimens. The experimental programme evaluated compressive strength, tensile strength (flexural and splitting), modulus of elasticity, and water penetration depth. Predictive modelling was conducted using Random Forests (RF) and Support Vector Regression (SVR), trained and evaluated with simple cross-validation and benchmarked using mean absolute error (MAE) and coefficient of determination (R²). The results reveal distinct optima for strength indices and a threshold behaviour in permeability, with PF dosages between 0.2% and 0.6% balancing mechanical enhancement and substantial reductions in water penetration, although accompanied by a pronounced reduction in elastic modulus at very low PF contents. The RF models exhibited superior predictive performance, consistently outperforming SVR across properties. The experimental outcomes demonstrate that incorporating PF into the concrete mixture enhances its mechanical properties. However, the optimal fibre-to-cement ratios differ for various properties: compressive strength (0.3% to 0.4%), tensile strength (0.2% to 0.4%), modulus of elasticity (0.1%), and permeability (0.6%). The overall optimal fibre range is identified as 0.1% to 0.6%, which satisfies all specified criteria. Notably, the inclusion of PF results in a 60% increase in compressive strength, a 115% increase in tensile strength (bending test), a 288% increase in tensile strength (Brazilian test), a tenfold reduction in modulus of elasticity, and a twenty-fivefold reduction in permeability.
– Name: URL
  Label: Access URL
  Group: URL
  Data: <link linkTarget="URL" linkTerm="https://doi.org/10.1016/j.rinma.2025.100777" linkWindow="_blank">https://doi.org/10.1016/j.rinma.2025.100777</link>
PLink https://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsswe&AN=edsswe.oai.portal.research.lu.se.publications.2b126449.fad7.48d0.a838.5b766cea11a9
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.rinma.2025.100777
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: Engineering and Technology
        Type: general
      – SubjectFull: Civil Engineering
        Type: general
      – SubjectFull: Building materials
        Type: general
      – SubjectFull: Teknik
        Type: general
      – SubjectFull: Samhällsbyggnadsteknik
        Type: general
      – SubjectFull: Byggnadsmaterial
        Type: general
      – SubjectFull: Mechanical Engineering
        Type: general
      – SubjectFull: Solid and Structural Mechanics
        Type: general
      – SubjectFull: Maskinteknik
        Type: general
      – SubjectFull: Solid- och strukturmekanik
        Type: general
    Titles:
      – TitleFull: From Experimental Studies to Predictive Machine Learning Modelling: Polypropylene Fibre Reinforced Concrete
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Bayat Pour, Mohsen
      – PersonEntity:
          Name:
            NameFull: Lund University, Faculty of Engineering, LTH, Departments at LTH, Department of Building and Environmental Technology, Division of Structural Engineering, Lunds universitet, Lunds Tekniska Högskola, Institutioner vid LTH, Institutionen för bygg- och miljöteknologi, Avdelningen för Konstruktionsteknik, Originator
      – PersonEntity:
          Name:
            NameFull: Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: Circular Building Sector, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: Cirkulär byggindustri, Originator
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 07
              M: 10
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 2590048X
            – Type: issn-locals
              Value: SWEPUB_FREE
            – Type: issn-locals
              Value: LU_SWEPUB
          Numbering:
            – Type: volume
              Value: 28
          Titles:
            – TitleFull: Results in Materials
              Type: main
ResultId 1