International Concrete Abstracts Portal

Ultra-high performance concrete (UHPC) has seen growing use in the construction industry because of its high compressive, tensile, and flexural strength. The tensile and flexural strength are in part due to the steel fibers added to the UHPC mix. Yet, fibers can segregate due to poor material rheological properties and construction practices, resulting in less than expected material strength. Due to the importance of these fibers, there is a need to verify the volume and orientation of the steel fibers in the UHPC. In this work, we report on the design and testing of electromagnetic sensor systems that are able to test the integrity of the steel fibers in the UHPC structure. We test our sensor system using UHPC samples containing 1% to 3% fiber content by volume and created a calibration based on the results. Our results show a linear relationship between the inductance change versus the fiber percentage with an R-squared value of 99.7 %, which shows that our approach successfully demonstrated the potential of using our approach for characterizing steel fibers in UHPCs.

Fiber-reinforced polymer (FRP) materials provide an excellent alternative for shear, flexure, and confinement retrofitting of deteriorated infrastructure. Despite the advanced technology employed in fabricating FRP materials, the monitoring and quality control of the FRP installation still present a challenge. For externally bonded FRP-rehabilitated structures, the existence of undesirable defects, including surface voids and debonding, on the concrete surface should be evaluated, as these defects would adversely affect the durability and capacity of the FRP-rehabilitated structures. Nondestructive testing has the potential to provide a fast and precise means to assess these FRP rehabilitated structures. This paper presents an experimental and theoretical investigation of the use of ground-penetrating radar (GPR) and infrared tomography (IRT) methods to evaluate reinforced-concrete (RC) slabs externally bonded with glass fiber-reinforced polymer (GFRP). Four externally bonded GFRP RC slab specimens were fabricated. Surface voids, interfacial debonding, and vertical cracks were artificially created on the concrete surface of the RC slabs. Test variables include the location and size of surface voids, interfacial debonding, and diameter of steel reinforcement. Improved two-dimensional and three-dimensional image reconstruction method, using the synthetic aperture focusing technique (SAFT), was established to effectively interpret the GPR test data. The results showed that an in-house developed software, that employed the enhanced image reconstruction technique, provided sharp and high-resolution images of the GFRP-retrofitted RC slabs in comparison to those images obtained from the device’s original software. The data suggests that the GPR testing could effectively be employed to accurately determine the size and location of the artificial voids as well as the spacing of the steel reinforcement. The GPR, however, could not well predict the debonding and concrete cracking, as the GPR signals were corrupted because of the direct wave and coupling effect of the antennae and background noise. Results obtained from the IRT testing showed that this technique can detect and locate near-surface defects including surface voids, interfacial debonding, and cracking with acceptable accuracy. The study suggests the combined use of the GPR and IRT imaging to accurately detect possible internal defects of FRP-rehabilitated concrete structures.

Since the early 1950s when prestressed concrete (PSC) bridge design was first started, major changes to the design provisions have been made. This paper aims to evaluate the historical design provisions in terms of design capacities and load distribution. Seventeen existing bridges designed using the first PSC Criteria are evaluated. Additionally, a load test is performed on one sixty-year-old PSC I-girder bridge to help validate finite element models (FEM) developed for determining the load distribution factors. The verification of the model is conducted based on the results from the diagnostic load testing and the nondestructive tests. Since the load tests may not be at the governing location, the critical live load distribution factors are determined using the verified FEM by changing the transverse positions of trucks. The results of design capacity show that the overall performance of the flexural design is more consistent than that of the shear, and in some cases, the shear could be unsatisfactory. The same set of bridges are analyzed for the distribution factors using the FEM method, and the results are compared with the four historically available prediction models. The comparison shows that all four approaches provide reasonable estimation of the distribution factors, while the AASHTO Standard Specification underestimates the live load distribution for shear.

In this paper, the artificial neural network (ANN) method is utilized to predict ground-penetrating radar reflection amplitudes from four different inputs, namely, temperature, ambient relative humidity, chloride level, and corrosion condition on the surface of the reinforcing bar. A total of 288 ground penetrating radar (GPR) data points were collected from a series of chloride-contaminated concrete slabs under various environmental profiles that were used to train, validate, and test the proposed ANN model. The ANN model performed well in predicting the GPR reflection signals, with the overall coefficient of determination (R2) being 0.9958. The overall mean squared error (MSE) and root mean squared error (RSME) values are 0.015 and 0.122, respectively. These values are very low, which means that the ANN model has an excellent prediction capability. The research results show that the GPR reflection amplitudes are more sensitive to the temperature changes and chloride level parameters than the ambient relative humidity and rust condition on the reinforcing bar surface. Using the ANN method to predict the GPR reflection amplitudes is relatively new for structural concrete applications. This study paves the way for further developments of neural networks in civil and structural engineering.

Existing road infrastructure and bridges gradually carry increasing in weight and number vehicular traffic. The objective of this study is to assess adequacy of a 100-year-old reinforced concrete framed bridge in Alabama expressed as reliability index. Geometric data about the structure was obtained using destructive and nondestructive testing methods. Material data was collected from field tests and available literature on evaluation of existing structures. Behavior of the structure was investigated during load tests performed. The most harmful load configuration for the particular bridge was established in a recent study on weigh-in-motion data for the State of Alabama. Using finite element numerical method, a three dimensional model of the bridge was developed, calibrated and used for reliability study. The statistical parameters of resistance of the bridge were obtained using Rosenblueth 2k+1 method. The reliability analysis was demonstrated on the one span structural system.