scholarly journals EVALUATION ON THE EFFECTS OF CONCRETE COMPRESSIVE STRENGTH UNDER THE FATIGUE RESISTANCE LOAD FOR RC SLABS IN HIGHWAY BRIDGE

2010 ◽  
Vol 64 (1) ◽  
pp. 552-559
Author(s):  
Tadashi ABE ◽  
Tetsukazu KIDA ◽  
Makiko TAKANO ◽  
Yutaka KAWAI
Keyword(s):  
Compressive Strength ◽  
Fatigue Resistance ◽  
Highway Bridge ◽  
Concrete Compressive Strength ◽  
Rc Slabs

Variasi Penambahan Sika Cim Dan Fiber Kawat Pada Beton Mutu Fc’ 30 Mpa

2018 ◽  
Vol 9 (2) ◽  
pp. 67-73
Author(s):  
M Zainul Arifin
Keyword(s):  
Tensile Strength ◽  
Compressive Strength ◽  
Curing Temperature ◽  
Test Results ◽  
Concrete Compressive Strength ◽  
Normal Concrete ◽  
Astm Method ◽  
Additive Type ◽  
Composition Variation ◽  
Compressive Strength Of Concrete

This research was conducted to determine the value of the highest compressive strength from the ratio of normal concrete to normal concrete plus additive types of Sika Cim with a composition variation of 0.25%, 0.50%, 0.75%, 1.00%, 1.25%, 1 , 50% and 1.75% of the weight of cement besides that in this study also aims to find the highest tensile strength from the ratio of normal concrete to normal concrete in the mixture of sika cim composition at the highest compressive strength above and after that added fiber wire with a size diameter of 1 mm in length 100 mm with a ratio of 1% of material weight. The concrete mix plan was calculated using the ASTM method, the matrial composition of the normal concrete mixture as follows, 314 kg / m3 cement, 789 kg / m3 sand, 1125 kg / m3 gravel and 189 liters / m3 of water at 10 cm slump, then normal concrete added variations of the composition of sika cim 0.25%, 0.50%, 0.75%, 1.00%, 1.25%, 1.5%, 1.75% by weight of cement and fiber, the tests carried out were compressive strength of concrete and tensile strength of concrete, normal maintenance is soaked in fresh water for 28 days at 30oC. From the test results it was found that the normal concrete compressive strength at the age of 28 days was fc1 30 Mpa, the variation in the addition of the sika cim additive type mineral was achieved in composition 0.75% of the cement weight of fc1 40.2 Mpa 30C. Besides that the tensile strength test results were 28 days old with the addition of 1% fiber wire mineral to the weight of the material at a curing temperature of 30oC of 7.5%.

PENGKAJIAN KEKUATAN BETON STRUKTUR JEMBATAN PASCA KEBAKARAN

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Sudarmadi Sudarmadi
Keyword(s):  
Compressive Strength ◽  
Concrete Strength ◽  
Ultrasonic Pulse Velocity ◽  
Ultrasonic Pulse ◽  
Pulse Velocity ◽  
Test Results ◽  
Concrete Compressive Strength ◽  
Strength Assessment ◽  
Concrete Testing

In this paper a case study about concrete strength assessment of bridge structure experiencing fire is discussed. Assessment methods include activities of visual inspection, concrete testing by Hammer Test, Ultrasonic Pulse Velocity Test, and Core Test. Then, test results are compared with the requirement of RSNI T-12-2004. Test results show that surface concrete at the location of fire deteriorates so that its quality is decreased into the category of Very Poor with ultrasonic pulse velocity ranges between 1,14 – 1,74 km/s. From test results also it can be known that concrete compressive strength of inner part of bridge pier ranges about 267 – 274 kg/cm2 and concrete compressive strength of beam and plate experiencing fire directly is about 173 kg/cm2 and 159 kg/cm2. It can be concluded that surface concrete strength at the location of fire does not meet the requirement of RSNI T-12-2004. So, repair on surface concrete of pier, beam, and plate at the location of fire is required.

ALCOA 2026

Alloy Digest ◽  
2006 ◽  
Vol 55 (10) ◽  
Keyword(s):  
Compressive Strength ◽  
Corrosion Resistance ◽  
Tensile Properties ◽  
Fatigue Resistance ◽  
Selection Criteria ◽  
Damage Tolerance ◽  
Structural Applications ◽  
Extrusion Alloy ◽  
High Degree

Abstract Alcoa extrusion alloy 2026 was developed as an improvement over 2024 and 2224 extrusions in aerospace structural applications where the governing selection criteria are high damage tolerance, good fatigue resistance, and a high degree of manufacturability. In addition to improved damage tolerance, extrusion alloy 2026-T3511 also has a significantly higher A-basis minimum strength than 2024-T3511 and 2224-T3511. This datasheet provides information on composition, tensile properties, and compressive strength as well as fatigue. It also includes information on corrosion resistance as well as forming. Filing Code: AL-401. Producer or source: AEAP-Alcoa Engineered Aerospace Products.

scholarly journals Study on Influence of Range of Data in Concrete Compressive Strength with Respect to the Accuracy of Machine Learning with Linear Regression

2021 ◽  
Vol 11 (9) ◽  
pp. 3866
Author(s):  
Jun-Ryeol Park ◽  
Hye-Jin Lee ◽  
Keun-Hyeok Yang ◽  
Jung-Keun Kook ◽  
Sanghee Kim
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Linear Regression ◽  
Coarse Aggregate ◽  
Learning Algorithm ◽  
Linear Regression Analysis ◽  
Aggregate Size ◽  
Training Dataset ◽  
Machine Learning Algorithm ◽  
Concrete Compressive Strength

This study aims to predict the compressive strength of concrete using a machine-learning algorithm with linear regression analysis and to evaluate its accuracy. The open-source software library TensorFlow was used to develop the machine-learning algorithm. In the machine-earning algorithm, a total of seven variables were set: water, cement, fly ash, blast furnace slag, sand, coarse aggregate, and coarse aggregate size. A total of 4297 concrete mixtures with measured compressive strengths were employed to train and testing the machine-learning algorithm. Of these, 70% were used for training, and 30% were utilized for verification. For verification, the research was conducted by classifying the mixtures into three cases: the case where the machine-learning algorithm was trained using all the data (Case-1), the case where the machine-learning algorithm was trained while maintaining the same number of training dataset for each strength range (Case-2), and the case where the machine-learning algorithm was trained after making the subcase of each strength range (Case-3). The results indicated that the error percentages of Case-1 and Case-2 did not differ significantly. The error percentage of Case-3 was far smaller than those of Case-1 and Case-2. Therefore, it was concluded that the range of training dataset of the concrete compressive strength is as important as the amount of training dataset for accurately predicting the concrete compressive strength using the machine-learning algorithm.

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