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Evaluation of the compressive strength of polypropylene fiber reinforced high-strength concrete support with AI-based model

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Abstract

This study extensively examines the effects of introducing polypropylene fibers on the mechanical properties of high-strength concrete and develops predictive models for its compressive strength based on the mix proportions. The research is divided into two main parts. The initial section involves a thorough analysis of how polypropylene fibers influence various high-strength concrete properties, such as slump, compressive strength, splitting tensile strength, flexural strength, and modulus of elasticity, using a synthesis of experimental data. The subsequent section employs two well-known modeling approaches, linear regression (LR) and Artificial Neural Networks (ANN), to establish equations for forecasting compressive strength based on mixture proportions. LR assumes linear relationships and is less accurate for complex, nonlinear data, while ANN is more versatile and accurate for a wider range of tasks. The accuracy of both models depends on data complexity, with ANN generally performing better for nonlinear relationships. For the first time, eight effective variables were employed as input model parameters during the modeling process, including the water-to-binder ratio, cement content, fine and coarse aggregate content, silica fume content, superplasticizer content, fiber content, and specimens ages. The results show that adding polypropylene fibers significantly improve mechanical properties, particularly tensile strength. The ANN model outperforms LR in predicting compressive strength, with specific statistical metrics indicating its superiority. The ANN model exhibited RMSE, MAE, SI, OBJ, and R2 values of 4.02 MPa, 2.53 MPa, 6.3%, 5.98, and 0.96, respectively, for the training datasets. However, both models have limitations, including occasional overestimation or underestimation and complexity.

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Data availability

All data generated or analysed during this study are included in this published article.

Abbreviations

PP:

Polypropylene

PPF:

Polypropylene fiber

PVA:

Polyvinyl alcohol

LR:

Linear regression

HSC:

High-strength concrete

UHPC:

Ultra-high-performance concrete

ANN:

Artificial neural network

CFFs:

Chicken feather fibers

WTTF:

Waste tire textile fiber

GF:

Glass fibers

CS:

Compressive strength

GGBFS:

Ground granulated blast furnace slag

EGC:

Engineered geopolymer composites

SP:

Superplasticizers

R 2 :

Coefficient of determination

RMSE:

Root mean square error

MAE:

Mean absolute error

w/c :

Water-to-cement ratio

w/b:

Water-to-binder ratio

C.C:

Cement content

S.C:

Sand content

G.C:

Gravel content

SF:

Silica-fume

F.C:

Fiber content

T :

Curing time

SI:

Scatter index

OBJ:

Objective function

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Authors and Affiliations

  1. Civil Engineering Department, College of Engineering, University of Sulaimani, Sulaimani, Kurdistan Region, Iraq

    Soran Abdrahman Ahmad, Hemn Unis Ahmed & Serwan Khwrshid Rafiq

  2. Civil Engineering Department, Komar University of Science and Technology, Sulaimani, Kurdistan Region, Iraq

    Hemn Unis Ahmed

  3. Civil Engineering Department, College of Engineering, University of Garmian, Kalar, Kurdistan Region, Iraq

    Dler Ali Ahmad

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  1. Soran Abdrahman Ahmad
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Correspondence to Hemn Unis Ahmed.

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Ahmad, S.A., Ahmed, H.U., Rafiq, S.K. et al. Evaluation of the compressive strength of polypropylene fiber reinforced high-strength concrete support with AI-based model. Innov. Infrastruct. Solut. 8, 315 (2023). https://doi.org/10.1007/s41062-023-01292-6

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