Evaluating Shear Strength of Light-Weight and Normal-Weight Concretes through Artificial Intelligence

Ebid, Ahmed M.; Deifalla, Ahmed Farouk; Mahdi, Hisham A.

doi:10.3390/su142114010

Cited by 17 publications

(4 citation statements)

References 42 publications

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“…ANN algorithm is a "black box" that houses a massively parallel system with numerous processing components that are very good at information mining. The procedure inside the box enables a careful selection of the variables and a detailed investigation of their relationships 51 , 52 . In different ANN algorithms, a back-propagation network (BPN) is frequently used to solve engineering problems with the gradient descent technique to reduce errors.…”

Section: Machine Learning Models Establishmentmentioning

confidence: 99%

Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams

Kumar

Arora

Kapoor

et al. 2023

Sci Rep

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The ability of machine learning (ML) techniques to forecast the shear strength of corroded reinforced concrete beams (CRCBs) is examined in the present study. These ML techniques include artificial neural networks (ANN), adaptive-neuro fuzzy inference systems (ANFIS), decision tree (DT) and extreme gradient boosting (XGBoost). A thorough databank with 140 data points about the shear capacity of CRCBs with various degrees of corrosion was compiled after a review of the literature. The inputs parameters of the implemented models are the width of the beam, the effective depth of the beam, concrete compressive strength (CS), yield strength of reinforcement, percentage of longitudinal reinforcement, percentage of transversal reinforcement (stirrups), yield strength of stirrups, stirrups spacing, shear span-to-depth ratio (a/d), corrosion degree of main reinforcement, and corrosion degree of stirrups. The coefficient of determination of the ANN, ANFIS, DT, and XGBoost models are 0.9811, 0.9866, 0.9799, and 0.9998, respectively. The MAPE of the XGBoost model is 99.39%, 99.16%, and 99.28% lower than ANN, ANFIS, and DT models. According to the results of the sensitivity examination, the shear strength of the CRCBs is most affected by the depth of the beam, stirrups spacing, and the a/d. The graphical displays of the Taylor graph, violin plot, and multi-histogram plot additionally support the XGBoost model's dependability and precision. In addition, this model demonstrated good experimental data fit when compared to other analytical and ML models. Accurate prediction of shear strength using the XGBoost approach confirmed that this approach is capable of handling a wide range of data and can be used as a model to predict shear strength with higher accuracy. The effectiveness of the developed XGBoost model is higher than the existing models in terms of precision, economic considerations, and safety, as indicated by the comparative study.

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Section: Machine Learning Models Establishmentmentioning

confidence: 99%

Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams

Kumar

Arora

Kapoor

et al. 2023

Sci Rep

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“…Both previously mentioned equations were developed for the purpose of predicting the shear capacity of RC beams and are shown in Table 8. The GP equation was developed in 2021 using a dataset of 553 tests [22], while the EPR equation was developed in 2022 using a dataset of 1500 tests with an accuracy of 76.5% [23]. The values of the shear capacities for the tested seven wide-shallow beam specimens were calculated from the GP and the EPR equations, as shown in Table 9.…”

Section: Comparison With Earlier Researchesmentioning

confidence: 99%

The Impact of Aspect Ratio, Characteristic Strength and Compression Rebars on the Shear Capacity of Shallow RC Beams

Soliman,

Mansour,

Khalil

et al. 2023

Civ Eng J

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This paper investigates the impact of the aspect ratio, the characteristics strength of the concrete, and the compression steel ratio on the shear capacity of wide-shallow beams. An experimental program consists of seven specimens, including a control specimen, all tested under a three-point load test. Three specimens were considered for each parameter (the control specimen was included in all three variables). The experimental results were compared to the theoretical values of six different codes of practice; they were also analyzed to determine the ductility, stiffness, and dissipated energy of each specimen. The results indicated that the shear reinforcement was fully functioning until it yielded, with a minimum contribution of 55% of the total shear capacity of the specimens. The aspect ratio and the characteristic strength had a notable impact on the shear capacity of the specimens, while the compression steel ratio had a minor effect on the shear capacity, but it improved the stiffness and the ductility of the beams. Theoretical concrete shear strengths from design codes ranged between 77 and 163% of the experimental values; EN-1992 was the closest code to the experimental results. A comparison between the experimental results and predicted values using GP and EPR methods from previous research showed accuracies of 72% and 81%, respectively. Doi: 10.28991/CEJ-2023-09-09-012 Full Text: PDF

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“…It has the potential to be employed as a structural concrete. Not only does it add to the overall strength of the concrete, but it also contributes to the long-term durability of the concrete's qualities [75].…”

Section: Green Concretementioning

confidence: 99%

Optimization of Green Concrete Containing Fly Ash and Rice Husk Ash Based on Hydro-Mechanical Properties and Life Cycle Assessment Considerations

et al. 2022

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The development of sustainable concrete in achieving the developmental goals of the United Nations in terms of sustainable infrastructure and innovative technology forms part of the focus of this research paper. In order to move towards sustainability, the utilization of the by-products of agro-industrial operations, which are fly ash (FA) and rice husk ash (RHA), in the production of concrete has been studied. Considering the environmental impact of concrete constituents, multiple mechanical and hydraulic properties of fly ash (FA) and rice husk ash (RHA) concrete have been proposed using intelligent techniques; artificial neural network (ANN) and evolutionary polynomial regressions (EPR). Also, an intelligent mix design tool/chart for this case under study is proposed. Multiple data points of concrete materials, which were further reduced to ratios as follows; cement to binder ratio (C/B), aggregate to binder ratio (Ag/B), and plasticizer to binder ratio (PL/B) were used in this exercise. At the end of the protocol, it is observed that the constituents’ ratios are dependent on the behavior of the whole, which can be solved by using the proposed model equations and mix design charts. The models performed optimally, as none showed any performance below 80%. However, ANN, which predicted Fc03, Fc07, Fc28, Fc60, Fc90, Ft28, Ff28 & Fb28, S, Ec28 & K28, and P with an accuracy of greater than 95% each with average error of less than 9.4% each, is considered the decisive technique in predicting all the studied concrete properties, including the life cycle assessment potential of the concrete materials. Doi: 10.28991/CEJ-2022-08-12-018 Full Text: PDF

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Evaluating Shear Strength of Light-Weight and Normal-Weight Concretes through Artificial Intelligence

Cited by 17 publications

References 42 publications

Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams

Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams

The Impact of Aspect Ratio, Characteristic Strength and Compression Rebars on the Shear Capacity of Shallow RC Beams

Optimization of Green Concrete Containing Fly Ash and Rice Husk Ash Based on Hydro-Mechanical Properties and Life Cycle Assessment Considerations

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