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Pujo Aji
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Civil Engineering, Building, Construction & Architecture Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation

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Journal : Journal of Civil Engineering

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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.
Optimization of Pre-Treatment Process in Spent Bleaching Earth (SBE) on The Characteristics of Pre-Treated SBE as Supplementary Cementitious Material Christian Y. Pramono; Wahyuniarsih Sutrisno; Triwulan Triwulan; Pujo Aji
Journal of Civil Engineering Vol. 39 No. 1 (2024)
Publisher : Institut Teknologi Sepuluh Nopember (ITS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20861206.v39i1.7303

Abstract

The palm oil processing industry in Indonesia has experienced significant growth,bringing both positive economic impacts and negative consequences, specificallythe generation of spent bleaching earth (SBE), a waste product of bleaching earth.Despite its potential as a substitute material for cement due to its pozzolanicproperties, challenges arise from SBE's oil content. Hence, this study introducespre-treatment methods involving extraction and calcination to optimize the use ofSBE, referred to as Pre-treated Spent Bleaching Earth (PSBE). This research aimsto analyze the optimized PSBE material through the optimization of the pretreatment process in the usage of mortar. The optimized PSBE is compared toanother supplementary cementitious material, which is fly ash to see theperformance of optimized PSBE as supplementary cementitious material. Theperformance of the mortar was evaluated through tests including slump test,compressive strength test, and mortar hydration temperature analysis. The pretreatment process of SBE was optimized by combining extraction and calcinationmethods, which yielded the most effective results from oil content test. One of theperformance analysis results showed that the compressive strength test revealed a28-day compressive strength value of 50,22 MPa for the optimized PSBE mortar,while the fly ash mortar had a compressive strength of 37,36 MPa. In conclusion,the optimized PSBE shows promising potential as a supplementary cementitiousmaterial.
FINITE ELEMENT MODELING OF CIRCULAR REINFORCED CONCRETE COLUMN CONFINED WITH CFRP UNDER ECCENTRIC LOADING Angga Bayu Christianto; Bambang Piscesa; Faimun Faimun; Pujo Aji
Journal of Civil Engineering Vol. 34 No. 2 (2019)
Publisher : Institut Teknologi Sepuluh Nopember (ITS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20861206.v34i2.7435

Abstract

This paper presents nonlinear finite element analysis of eccentrically loaded circular Reinforced Concrete (RC)column confined with Carbon Fiber Reinforced Polymer (CFRP) wraps. The concrete constitutive model uses a plasticityfracture model which is restraint sensitive, utilize a non-constant plastic dilation rate, and is able to simulate the plasticvolumetric compaction of concrete core under high confining pressure. For validation of the models, two available specimensfrom the literature are used in the validations. Excellent agreement between the numerical models and the available test resultsare obtained in this study. A detailed investigation on the confinement effectiveness of both external and internal confiningdevices are presented and discussed. This discussion of the confinement effectiveness is important to be included in the designformula.
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 (ITS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20861206.v34i1.7465

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 (ITS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20861206.v37i2.7616

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 Angga Bayu Christianto Asdam Tambusay Bambang Piscesa Christian Y. Pramono Faimun Faimun Ika Salsabila Nurahida Triwulan Triwulan Wahyuniarsih Sutrisno Yulia Helena Margarita Rada