| 1. |
- Alyaseen, Ahmad, et al.
(author)
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Behavior of CFRP-strengthened RC beams with web openings in shear zones : Numerical simulation
- 2022
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In: Materials Today-Proceedings. - : Elsevier BV. - 2214-7853. ; , s. 3229-3239
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Conference paper (peer-reviewed)abstract
- The passing of service ducts and pipes often necessitates web openings in reinforced concrete (RC) beams. The strength and stiffness of the beam are reduced due to such web opening(s). Existing experimental research has shown the possibility of utilizing externally bonded Carbon Fiber-reinforced polymer (CFRP) to compensate for the strength loss of beams. However, there have been few finite element (FE) methodologies for predicting and estimating the performance of such RC beams. This paper uses nine dif-ferent FE models to simulate a CFRP strengthened web opening using ABAQUS. This paper evaluates RC beam performance with web openings in the shear zone and proposed reinforcement of CFRP. Nine RC beams with openings, numerical tested under 2-loads. The models were constructed using the ABAQUS software by FE to simulate RC beams with a CFRP strengthened by two rectangular web openings. This paper studies the sequential effect of the different fundamental parameters on the strengthened RC beams' overall stiffness and shear strength response, achieving optimum utilization of the strengthened technique in load-bearing capacity and potential deflection. Results have shown no need to strengthen small web openings in the RC beams' shear zone. Beams with large openings in the shear zone need strengthening with CFRP; the beam's efficiency has been significantly improved using 2-layers of CFRP sheets around the web opening and a single-layer CFRP strip in the flexural zone bottom the chord.
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| 2. |
- Alyaseen, Ahmad, et al.
(author)
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High-performance self-compacting concrete with recycled coarse aggregate : Soft-computing analysis of compressive strength
- 2023
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In: Journal of Building Engineering. - : Elsevier BV. - 2352-7102. ; 77
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Journal article (peer-reviewed)abstract
- The growth of cities and industrialization has led to an increase in demand for concrete, resulting in resource depletion and environmental issues. Sustainable alternatives such as using recycled concrete aggregate (RCA) and industrial waste have been proposed to meet construction material demands while adhering to building codes and promoting sustainability. However, compressive strength (CS) is a crucial property of concrete, and the design parameters have different effects on CS for various grades. Recently, researchers have focused on partially replacing natural coarse aggregate (NCA) with RCA in concrete to achieve sustainability goals. This study aims to examine the influence of design parameters (w/c: water-cement ratio, w/b: water-binder ratio, A/c: total aggregate-cement ratio, FA/CA: fine-coarse aggregate ratio, SP: superplasticizer, w/s: water-solid ratio and RCA%) on concrete CS and address controversies in the insights gained from pairwise comparisons using Pearson's correlation coefficient (PCC) analysis. Additionally, five techniques (M5P, RF, SVM, LR, and ANNs) were used to predict the CS of high-performance self-compacting concrete (HP-SCC) with RCA, and the results were compared with an ANNs-based model as was the commonly used one in literature. The approaches were assessed based on their accuracy measured using correlation coefficient (CC), mean absolute error (MAE), Root Mean Square Error (RMSE), Mean absolute percentage error (MAPE), Scatter index (SI), and comprehensive measure (COM) indicators. Accordingly, the analysis indicated that SVM-PUK-based model is the most appropriate and effective technique to predict the CS of HP-SCC for the given datasets, with CC = 0.894, 0.900, MAE = 1.721, 3.813, RMSE = 5.137, 6.306, and MAPE = 4.5%, 7.6% for the training and testing stages, respectively. The uncertainty analysis results were 21%, 20.7%, 19%, 22%, and 19% for M5P, RF, SVM, LR, and ANN-based models, respectively, whereby all of them were under threshold of 35%. Moreover, according to sensitivity analysis, w/c, w/b, and w/s variables influences the most on CS prediction, while the RCA(%) variable has the least impact.
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