346 resultados para STATISTICAL STRENGTH
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The present research project was designed to identify the typical Iowa material input values that are required by the Mechanistic-Empirical Pavement Design Guide (MEPDG) for the Level 3 concrete pavement design. It was also designed to investigate the existing equations that might be used to predict Iowa pavement concrete for the Level 2 pavement design. In this project, over 20,000 data were collected from the Iowa Department of Transportation (DOT) and other sources. These data, most of which were concrete compressive strength, slump, air content, and unit weight data, were synthesized and their statistical parameters (such as the mean values and standard variations) were analyzed. Based on the analyses, the typical input values of Iowa pavement concrete, such as 28-day compressive strength (f’c), splitting tensile strength (fsp), elastic modulus (Ec), and modulus of rupture (MOR), were evaluated. The study indicates that the 28-day MOR of Iowa concrete is 646 + 51 psi, very close to the MEPDG default value (650 psi). The 28-day Ec of Iowa concrete (based only on two available data of the Iowa Curling and Warping project) is 4.82 + 0.28x106 psi, which is quite different from the MEPDG default value (3.93 x106 psi); therefore, the researchers recommend re-evaluating after more Iowa test data become available. The drying shrinkage (εc) of a typical Iowa concrete (C-3WR-C20 mix) was tested at Concrete Technology Laboratory (CTL). The test results show that the ultimate shrinkage of the concrete is about 454 microstrain and the time for the concrete to reach 50% of ultimate shrinkage is at 32 days; both of these values are very close to the MEPDG default values. The comparison of the Iowa test data and the MEPDG default values, as well as the recommendations on the input values to be used in MEPDG for Iowa PCC pavement design, are summarized in Table 20 of this report. The available equations for predicting the above-mentioned concrete properties were also assembled. The validity of these equations for Iowa concrete materials was examined. Multiple-parameters nonlinear regression analyses, along with the artificial neural network (ANN) method, were employed to investigate the relationships among Iowa concrete material properties and to modify the existing equations so as to be suitable for Iowa concrete materials. However, due to lack of necessary data sets, the relationships between Iowa concrete properties were established based on the limited data from CP Tech Center’s projects and ISU classes only. The researchers suggest that the resulting relationships be used by Iowa pavement design engineers as references only. The present study furthermore indicates that appropriately documenting concrete properties, including flexural strength, elastic modulus, and information on concrete mix design, is essential for updating the typical Iowa material input values and providing rational prediction equations for concrete pavement design in the future.
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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The implementation of warm-mix asphalt (WMA) is becoming more widespread with a growing number of contractors utilizing various WMA technologies. Early research suggests WMA may be more susceptible to moisture damage than traditional hot-mix asphalt (HMA) mixes. The objectives of this study are to test the binder and mix properties of WMA technologies for both field- and laboratory-produced mixes to determine the performance of WMA compared to traditional HMA. Field- and laboratory-produced mixes were studied. The laboratory-produced mixes compared HMA control mixes with WMA mixes that had the same mix design. The WMA technologies used for the laboratory study were Advera, Sasobit, and Evotherm. The field study tested four WMA field-produced mixes. Each of the four mixes had a corresponding control HMA mix. The WMA technologies used in the field study included: Evotherm 3G/Revix, Sasobit, and Double Barrel Green Foaming. The three main factors for this study were WMA/HMA, moisture-conditioned/not moisture-conditioned, and reheated/not reheated. Mixes were evaluated based on performance tests. Binder testing was performed to determine the rheological differences between HMA and WMA binders to determine if binder grade requirements change with the addition of WMA additives. The conclusions of this study are as follows: Reduced mixing and compaction temperatures were achieved. Statistical differences were found when comparing tensile strength ratio (TSR) values for both laboratory- and field-produced mixes. In the laboratory, none of the WMA additives performed as well as the HMA. For the field mixes, all TSR values passed Iowa’s minimum specification of 0.8 but, on average, WMA is lower compared to HMA TSR values. Dynamic modulus results show that, on average, HMA will have higher dynamic modulus values. This means the HMA exhibits stiffer material properties compared to WMA; this may not necessarily mean superior performance in all cases. Flow number results show that WMA has reduced flow number values compared to HMA. The only exception was the fourth field mix and weather delayed production of the control mix by nine days. The laboratory mixes showed that flow number values increased significantly with the addition of recycled asphalt pavement (RAP). In the laboratory study, Advera reduced TSR values. Given that Advera is a foaming agent, the increase in moisture susceptibility is likely attributed to the release of water necessary for the improvement of the workability of the asphalt mixture.
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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In this work, a previously-developed, statistical-based, damage-detection approach was validated for its ability to autonomously detect damage in bridges. The damage-detection approach uses statistical differences in the actual and predicted behavior of the bridge caused under a subset of ambient trucks. The predicted behavior is derived from a statistics-based model trained with field data from the undamaged bridge (not a finite element model). The differences between actual and predicted responses, called residuals, are then used to construct control charts, which compare undamaged and damaged structure data. Validation of the damage-detection approach was achieved by using sacrificial specimens that were mounted to the bridge and exposed to ambient traffic loads and which simulated actual damage-sensitive locations. Different damage types and levels were introduced to the sacrificial specimens to study the sensitivity and applicability. The damage-detection algorithm was able to identify damage, but it also had a high false-positive rate. An evaluation of the sub-components of the damage-detection methodology and methods was completed for the purpose of improving the approach. Several of the underlying assumptions within the algorithm were being violated, which was the source of the false-positives. Furthermore, the lack of an automatic evaluation process was thought to potentially be an impediment to widespread use. Recommendations for the improvement of the methodology were developed and preliminarily evaluated. These recommendations are believed to improve the efficacy of the damage-detection approach.
