Improving the structural behavior of shear-deficient RC deep beams using steel fibers: Experimental, numerical and probabilistic approach

Galal

Beni-Suef Univ J Basic Appl Sci

et al. 2022

Background Rigid pavements have become an urgent demand in recent years, as these pavements need less maintenance and renovation than other types. However, traditional rigid pavement faces various challenges and difficulties over its lifetime. It has a much higher initial erection cost than asphalt pavements, a greater sensitivity to dynamic stresses, and a highly susceptible to temperature variations causing cracking. Previous works dealt with these drawbacks by using effective materials as alternatives to cement and/or aggregates in pavements mixtures. In the last few years, much interest has been carried out in nanomaterial applications to improve the mechanical performance of construction materials, which can also be used for rigid pavement constructions. This improvement is due to nanomaterials' role in concrete as nanoreinforcements and nanofillers. On the other hand, various types of fibers have been used to improve the performance of concrete constructions. This study investigates the effect of adding carbon nanotubes (CNTs) and steel fibers (SFs) to concrete mixtures. A series of experiments on concrete mixes with various weight percentages of CNTs (0%, 0.025%, 0.050%, and 0.075%) were added to the mixtures to determine the best cost and amount of CNTs to add to a concrete mix. Compressive, tensile, and flexure strength characteristics are investigated. In the second experimental stage of this work, the effect of adding steel fibers to the mixture was investigated. Results According to the results, the optimal carbon nanotube content in concrete is 0.05%. Compared to other concrete combinations with varying proportions of CNTs, this quantity offers the maximum compressive, tensile, and flexural strength. Additionally, SFs can improve the mechanical properties of the mix as well as enhance its post-cracking and fatigue behavior. Adding both CNTs with SFs increased compressive, tensile, and flexural strength by 22.7%, 29.3%, and 70.8%, respectively, more than the traditional pavement. Conclusion This work found that combining SFs with CNTs improves the mechanical properties of the concrete mortar, resulting in a stronger mortar that can withstand more loads than the traditional one.

“…Ref. [36,37] investigated the failure mechanism of steel-concrete beams and observed that SFs could modify the mode of failure from shear to bending. Ref.…”

Section: Introductionmentioning

confidence: 99%

Utilization of carbon nanotubes and steel fibers to improve the mechanical properties of concrete pavement

Galal

Beni-Suef Univ J Basic Appl Sci

et al. 2022

“…This investigation used curvature ductility because it can be evaluated using only the sectional properties of the beam and is therefore more practical. Accordingly, we calculated the ductility parameter (

) [ 55 , 56 ] by dividing the curvature at failure by the curvature at yielding (i.e.,

) and summarized it in Table 9 . This table indicates that B2–B4 (concrete–UDFRC composite beams) had inferior ductility compared to B1 (the control beam).…”

Section: Resultsmentioning

confidence: 99%

Behavioral Evaluation of Strengthened Reinforced Concrete Beams with Ultra-Ductile Fiber-Reinforced Cementitious Composite Layers

Khan

Abbas

2023

Materials

Self Cite

In the literature, there is little information available regarding the behavior of composite beams made up of reinforced concrete (RC) and ultra-ductile fiber-reinforced concrete (UDFRC). In this study, UDFRC was examined for its effectiveness in enhancing the strength of RC beams. With a tensile strength of 4.35 MPa and a strain capacity of 2.5%, PVA-based UDFRC was prepared. The performance of 12 medium-sized reinforced concrete (RC) beams was measured under four-point flexural loading. The beams measured 1800 mm long, 150 mm wide, and 200–260 mm deep. The experimental program on beam specimens was divided into two phases. In the first, four 150 × 200 × 1800 mm RC beams with UDFRC layer thicknesses of 0, 30, 60, and 90 mm were tested. Additionally, four concrete and four concrete–UDFRC beams were investigated, measuring 150 × 230 × 1800 mm and 150 × 260 × 1800 mm, respectively. The study focused on medium-sized, slender RC beams under quasi-static loads and room temperature with additional or substituted UDFRC layers. As a result of replacing concrete with UDFRC, the load-carrying capacity at first crack and steel yield significantly increased between 18.4 and 43.1%, but the ultimate load-carrying capacity increased only in the range of 6.3–10.8%. Furthermore, beams with additional UDFRC layers could carry 30–50% more load than their concrete counterparts. An RC-UDFRC beam had a load-carrying capacity 10–15% greater than that of a comparable RC beam. Generally, there is a lower deflection response in UDFRC–concrete composite RC beams than in control concrete beams. The UDFRC layering can potentially improve the load-carrying capacity of RC beams, at least when ductility provisions are considered.

“…Furthermore, compelling evidence suggests that the inclusion of steel fibers may reduce the need for conventional steel shear reinforcement. This conclusion is drawn from a comprehensive array of tests and analyses conducted by the authors and other esteemed researchers in the field [18][19][20].…”

Section: Introductionmentioning

confidence: 96%

Robust Machine Learning Framework for Modeling the Compressive Strength of SFRC: Database Compilation, Predictive Analysis, and Empirical Verification

Abbas,

Khan

2023

Materials

Self Cite

In recent years, the field of construction engineering has experienced a significant paradigm shift, embracing the integration of machine learning (ML) methodologies, with a particular emphasis on forecasting the characteristics of steel-fiber-reinforced concrete (SFRC). Despite the theoretical sophistication of existing models, persistent challenges remain—their opacity, lack of transparency, and real-world relevance for practitioners. To address this gap and advance our current understanding, this study employs the extra gradient (XG) boosting algorithm, crafting a comprehensive approach. Grounded in a meticulously curated database drawn from 43 seminal publications, encompassing 420 distinct records, this research focuses predominantly on three primary fiber types: crimped, hooked, and mil-cut. Complemented by hands-on experimentation involving 20 diverse SFRC mixtures, this empirical campaign is further illuminated through the strategic use of partial dependence plots (PDPs), revealing intricate relationships between input parameters and consequent compressive strength. A pivotal revelation of this research lies in the identification of optimal SFRC formulations, offering tangible insights for real-world applications. The developed ML model stands out not only for its sophistication but also its tangible accuracy, evidenced by exemplary performance against independent datasets, boasting a commendable mean target-prediction ratio of 99%. To bridge the theory–practice gap, we introduce a user-friendly digital interface, thoroughly designed to guide professionals in optimizing and accurately predicting the compressive strength of SFRC. This research thus contributes to the construction and civil engineering sectors by enhancing predictive capabilities and refining mix designs, fostering innovation, and addressing the evolving needs of the industry.