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Pujo Aji
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Prediction of Concrete Strength Based on Design Parameters, Hammer Test and Test UPV by Using Artificial Neural Network (ANN). Yulia Helena Margarita Rada; Pujo Aji
Journal of Civil Engineering Vol 34, No 1 (2019)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (387.339 KB) | DOI: 10.12962/j20861206.v34i1.5065

Abstract

This study aims to predict the compressive strength of existing concrete without using destructive tests which can damage the surface of the concrete. Destructive testing has the disadvantage of damaging the surface of the concrete, requires a long time and need expensive cost, while the Non Destructive Test (NDT) has the advantage of not damaging the surface of the concrete and faster when combined with the Artificial Neural Network (ANN) method. In this research, the Non Destructive Test (NDT) result such as hammer test and UPV were combined with concrete mix design properties and used to predict the compressive strength of concrete at three and 28 days. The Artificial Neural Network (ANN) method is used to make correlation of mix design properties data and NDT. In this study experimental tests were performed using variation of design parameters such as water per cement ratio and weight ratio of fly ash. The water per cement ratio used in this research was in range 0.45 until 0.55. Furthermore, the weight ratio of fly ash was in range 0% until 25%. Based on the modeling result using ANN method, it found that that the neural network method successfully predicts the compressive strength of concrete at three and 28 days with the mean square error (MSE) value and regression of concrete at three days are5.83 and 0.89 respectively. While at 28 days the MSE and regression value are 4.7 and 0.87 respectively.  
FINITE ELEMENT ANALYSIS ON THE NONLINEAR BEHAVIOR OF THE RC SHEAR WALL WITH REGULAR OPENINGS INFLUENCED BY HIGH-STRENGTH STEEL Ika Salsabila Nurahida; Bambang Piscesa; Pujo Aji; Asdam Tambusay
Journal of Civil Engineering Vol 37, No 2 (2022)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20861206.v37i2.13447

Abstract

This paper presented a nonlinear finite element analysis of lateral loading RC shear walls with regular openings using the 3D-NLFEA program. The RC shear walls model was generated from the available test results in the literature. To model the concrete under a complex stress state, a multi-surface plasticity model which combines compression failure surface with tension cut-off failure surface was used. The model was intended to look at the load-displacement relationship and the crack pattern between the model and the numerical model. In addition to the numerical model verification, parametric studies were carried out to investigate the use of high-strength steel (HSS) of the two different grades (grades 100 and 120) to replace all the normal-strength steel (NSS) or only some of it. The parametric studies found that the shear wall with the NSS bar demonstrated higher stiffness and achieved higher lateral load with the lowest extent of damage (compared to the RC shear wall with the HSS bar). On the other hand, using the HSS bar resulted in lower stiffness, lower lateral load, and higher damage region, which was expected as more strain is required to yield the HSS bar.
Co-Authors Asdam Tambusay Bambang Piscesa Ika Salsabila Nurahida Yulia Helena Margarita Rada