Fiber-Type Influence on the Flexural Behavior of RC Two-Way Slabs with an Opening

Hussain, Haleem K.; Abbas, Abdulnasser M.; Ojaimi, Mohammed Farhan

doi:10.3390/buildings12030279

Cited by 16 publications

(5 citation statements)

References 14 publications

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“…Therefore, the objective of this study is to enhance the mechanical behavior of pervious concrete by incorporating reinforcing materials into the lower layer. This involves the addition of fiberglass mesh and steel wire mesh to the lower layer of pervious concrete, and the study examines the resulting improvements in flexural strength and toughness (referenced in sources [29][30][31][32][33]). In addition, carbon nanotubes [34] are also one of the methods to enhance the structural and mechanical properties of cement-based composites.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on the Mechanical Properties of Reinforced Pervious Concrete

Lee,

Wang,

Wang

et al. 2023

Buildings

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Pervious concrete (PC) has gained popularity as an environmentally friendly solution for mitigating the urban heat island effect and promoting sustainable construction. However, its lower compressive strength, attributed to its higher porosity required for permeability, poses challenges for withstanding heavy vehicle loads on pavements. Our study aims to improve the flexural strength of regular PC by adding advanced reinforcing materials like steel wire mesh or glass fiber mesh. This results in reinforced pervious concrete, referred to as RPC, which offers enhanced strength and durability. The primary objective of our research is to investigate the mechanical behavior of RPC, with a specific emphasis on essential design parameters such as PC elastic modulus, modulus of rupture, and stress–strain characteristics under both single and repeated loading conditions. Our findings reveal that the influence of repeated loading on the compressive strength and elastic modulus of PC pavement is negligible, as there are no significant differences observed between the two loading protocols. Notably, our statistical analysis indicates that the PC strength (fc′) averages around 15 MPa. Moreover, empirical formulas for the elastic modulus (Ec = 3072fc′) and modulus of rupture (fr = 0.86fc′) are derived from our research. Furthermore, our study establishes that the stress–strain behavior of PC closely aligns with the general concrete model proposed by a previous scholar, providing valuable insights into the material’s structural performance. These findings contribute to a better understanding of RPC’s mechanical properties and offer potential solutions for improving its suitability for heavier vehicular loads.

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Section: Introductionmentioning

confidence: 99%

Experimental Study on the Mechanical Properties of Reinforced Pervious Concrete

Lee,

Wang,

Wang

et al. 2023

Buildings

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“…The inclusion of BF into cementitious composites improved the flexural strength by bridging the components in the composites and increasing the energy absorption. Furthermore, the higher bond and friction between BF and concrete matrix, leading to the improvement of the flexural strength [ 20 , 51 ].…”

Section: Resultsmentioning

confidence: 99%

Prediction of the Mechanical Properties of Basalt Fiber Reinforced High-Performance Concrete Using Machine Learning Techniques

et al. 2022

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In this research, we present an efficient implementation of machine learning (ML) models that forecast the mechanical properties of basalt fiber-reinforced high-performance concrete (BFHPC). The objective of the present study was to predict compressive, flexural, and tensile strengths of BFHPC through ML techniques and propose some correlations between these properties. Moreover, the modulus of elasticity (ME) values and compressive stress–strain curves were simulated using ML techniques. In this regard, three predictive algorithms, including linear regression (LR), support vector regression (SVR), and polynomial regression (PR), were considered. LR, SVR, and PR were utilized to forecast the compressive, flexural, and tensile strengths of BFHPC, and the PR technique was employed to simulate the compressive stress–strain curves. The performance of the models was also determined by the coefficient of determination (R2), mean absolute errors (MAE), and root mean square errors (RMSE). According to the obtained values of R2, MAE, and RMSE, the performance of PR was better than other types of algorithms in estimating the compressive, tensile, and flexural strengths. For example, R2 values were 0.99, 0.94, and 0.98 in predicting the compressive, flexural, and tensile strengths using PR, respectively. This shows the higher accuracy and reliability of the PR technique compared with other predictive algorithms. Finally, we concluded that ML techniques can be appropriately applied to assess the mechanical characteristics of BFHPC.

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“…H. K. Hussain et al revealed that steel fibers in concrete remarkably enhanced the strength and durability properties of hardened concrete. The flexural strength significantly increased with the incorporation of hooked end textured steel fibers [ 92 ]. According to Hyun-Oh Shin et al, in terms of flexural behavior of ultra-high-performance fiber-reinforced concrete under uniaxial and biaxial stress states, the straight steel fiber is the most effective [ 93 ].…”

Section: Data Descriptionmentioning

confidence: 99%

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence

Zheng

Sufian

et al. 2022

Materials

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Research has focused on creating new methodologies such as supervised machine learning algorithms that can easily calculate the mechanical properties of fiber-reinforced concrete. This research aims to forecast the flexural strength (FS) of steel fiber-reinforced concrete (SFRC) using computational approaches essential for quick and cost-effective analysis. For this purpose, the SFRC flexural data were collected from literature reviews to create a database. Three ensembled models, i.e., Gradient Boosting (GB), Random Forest (RF), and Extreme Gradient Boosting (XGB) of machine learning techniques, were considered to predict the 28-day flexural strength of steel fiber-reinforced concrete. The efficiency of each method was assessed using the coefficient of determination (R2), statistical evaluation, and k-fold cross-validation. A sensitivity approach was also used to analyze the impact of factors on predicting results. The analysis showed that the GB and RF models performed well, and the XGB approach was in the acceptable range. Gradient Boosting showed the highest precision with an R2 of 0.96, compared to Random Forest (RF) and Extreme Gradient Boosting (XGB), which had R2 values of 0.94 and 0.86, respectively. Moreover, statistical and k-fold cross-validation studies confirmed that Gradient Boosting was the best performer, followed by Random Forest (RF), based on reduced error levels. The Extreme Gradient Boosting model performance was satisfactory. These ensemble machine learning algorithms can benefit the construction sector by providing fast and better analysis of material properties, especially for fiber-reinforced concrete.

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Fiber-Type Influence on the Flexural Behavior of RC Two-Way Slabs with an Opening

Cited by 16 publications

References 14 publications

Experimental Study on the Mechanical Properties of Reinforced Pervious Concrete

Experimental Study on the Mechanical Properties of Reinforced Pervious Concrete

Prediction of the Mechanical Properties of Basalt Fiber Reinforced High-Performance Concrete Using Machine Learning Techniques

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence

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