Investigation of measured prestress losses compared with design prestress losses in AASHTO Types II, III, IV, and VI bridge girders

PCI Journal ◽  
2021 ◽  
Vol 66 (3) ◽  
pp. 32-48
Author(s):  
Ahmed Almohammedi ◽  
Cameron D. Murray ◽  
Canh N. Dang ◽  
W. Micah Hale
Keyword(s):  
Compressive Strength ◽  
American Association ◽  
Concrete Compressive Strength ◽  
Prestress Losses ◽  
Design Specifications ◽  
State Highway ◽  
Concrete Properties ◽  
Shrinkage And Creep ◽  
Bridge Girder ◽  
Aashto Lrfd

Inaccurate prediction of prestress losses leads to inaccurate predictions for camber, deflection, and concrete stresses in a bridge girder. This study aims to improve the prediction of prestress losses and provides bridge designers with insights into the differences between design and actual concrete properties. Prestress losses, compressive strength, modulus of elasticity, shrinkage, and creep were measured for several American Association of State Highway and Transportation Officials (AASHTO) Types II, III, IV, and VI girders. The investigation revealed that the measured total prestress losses at the time of deck placement were lower than the design losses calculated using the refined estimates method of the 2017 AASHTO LRFD Bridge Design Specifications. This was mainly attributed to the actual concrete compressive strength at transfer being greater than the design compressive strength. This discrepancy was as high as 73% for some girders. It was also determined that the 2017 AASHTO LRFD specifications’ refined estimates method for estimating prestress losses overestimates the total prestress losses at the time of deck placement for AASHTO Types II and III girders.

scholarly journals Performance Evaluation of a Prestressed Belitic Calcium Sulfoaluminate Cement (BCSA) Concrete Bridge Girder

2021 ◽  
Vol 13 (14) ◽  
pp. 7875
Author(s):  
Nick Markosian ◽  
Raed Tawadrous ◽  
Mohammad Mastali ◽  
Robert J. Thomas ◽  
Marc Maguire
Keyword(s):  
Compressive Strength ◽  
Portland Cement ◽  
Material Testing ◽  
Vibrating Wire ◽  
Prestress Losses ◽  
Curvature Estimate ◽  
Calcium Sulfoaluminate ◽  
Wire Strain ◽  
Bridge Girder ◽  
Aashto Lrfd

Belitic calcium sulfoaluminate (BCSA) cement is a sustainable alternative to Portland cement that offers rapid setting characteristics that could accelerate throughput in precast concrete operations. BCSA cements have lower carbon footprint, embodied energy, and natural resource consumption than Portland cement. However, these benefits are not often utilized in structural members due to lack of specifications and perceived logistical challenges. This paper evaluates the performance of a full-scale precast, prestressed voided deck slab bridge girder made with BCSA cement concrete. The rapid-set properties of BCSA cement allowed the initial concrete compressive strength to reach the required 4300 psi release strength at 6.5 h after casting. Prestress losses were monitored long-term using vibrating wire strain gages cast into the concrete at the level of the prestressing strands and the data were compared to the American Association of State Highway and Transportation Officials Load and Resistance Factor Design (AASHTO LRFD) predicted prestress losses. AASHTO methods for prestress loss calculation were overestimated compared to the vibrating wire strain gage data. Material testing was performed to quantify material properties including compressive strength, tensile strength, static and dynamic elastic modulus, creep, and drying and autogenous shrinkage. The material testing results were compared to AASHTO predictions for creep and shrinkage losses. The bridge girder was tested at mid-span and at a distance of 1.25 times the depth of the beam (1.25d) from the face of the support until failure. Mid-span testing consisted of a crack reopening test to solve for the effective prestress in the girder and a flexural test until failure. The crack reopen effective prestress was compared to the AASHTO prediction and AASHTO appeared to be effective in predicting losses based on the crack reopen data. The mid-span failure was a shear failure, well predicted by AASHTO LRFD. The 1.25d test resulted in a bond failure, but nearly developed based on a moment curvature estimate indicating the AASHTO bond model was conservative.

scholarly journals Fatigue Categorization of Obliquely Oriented Welded Attachments

2020 ◽  
Author(s):  
Robert J. Connor ◽  
Cem Korkmaz
Keyword(s):  
Longitudinal Direction ◽  
Effective Length ◽  
Fillet Welds ◽  
Bridge Design ◽  
Skewed Bridges ◽  
Primary Stress ◽  
Design Specifications ◽  
State Highway ◽  
Aashto Lrfd ◽  
The Web

In current bridge design specifications and evaluation manuals from the American Association of State Highway and Transportation Officials (AASHTO LRFD) (AASHTO, 2018), the detail category for base metal at the toe of transverse stiffener-to-flange fillet welds and transverse stiffener-to-web fillet welds to the direction of the web and hence, the primary stress) is Category C′. In skewed bridges or various other applications, there is sometimes a need to place the stiffener or a connection plate at an angle that is not at 90 degrees to the web. As the plate is rotated away from being 90 degrees to the web, the effective “length” of the stiffener in the longitudinal direction increases. However, AASHTO is currently silent on how to address the possible effects on fatigue performance for other angles in between these two extremes. This report summarizes an FEA study that was conducted in order to investigate and determine the fatigue category for welded attachments that are placed at angles other than 0 or 90 degrees for various stiffener geometries and thicknesses. Recommendations on how to incorporate the results into the AASHTO LRFD Bridge Design Specifications are included in this report.

scholarly journals The effect of addition of banana tree bark for compressive strength and crack tensile strength of rice husk ash concrete

2018 ◽  
Vol 195 ◽  
pp. 01024
Author(s):  
Muhammad Rizqi ◽  
Hernu Suyoso ◽  
Gati Annisa Hayu
Keyword(s):  
Tensile Strength ◽  
Compressive Strength ◽  
Rice Husk ◽  
Rice Husk Ash ◽  
Ash Content ◽  
Tree Bark ◽  
Concrete Compressive Strength ◽  
Banana Tree ◽  
Concrete Properties ◽  
Main Material

The use of concrete as the main material in the construction does not mean it has no weaknesses. The brittle, low-density concrete properties make it collapse unexpectedly. In this work, a concrete innovation was performed to increase the compressive strength by the addition of rice husk ash as cement substitution that contains 92.31% of SiO2 and by the addition of banana tree bark. The proportion of rice husk ash used was obtained from preliminary tests to determine the proportion of rice husk ash by 5%, 7%, 10%, 12% and 15% of the cement’s weight. The result of the proportion which yielded the optimum concrete compressive strength by 24.4 MPa in the proportion of rice husk ash by 7%, then was made with the same ash content with banana tree bark fiber variation 0%; 1.5%; 2% and 3%. The Result of the test concluded that the addition of banana tree bark fiber can decrease the compressive strength and tensile strength of concrete because it is caused by the fibers that make hard concrete become solid. However, for all proportions of fiber, it still qualifies as the minimum tensile strength to be achieved i.e. 8% of the compressive strength of the plan.

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.

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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