946 resultados para Prestressed concrete construction.


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Abstract : Although concrete is a relatively green material, the astronomical volume of concrete produced worldwide annually places the concrete construction sector among the noticeable contributors to the global warming. The most polluting constituent of concrete is cement due to its production process which releases, on average, 0.83 kg CO[subscript 2] per kg of cement. Self-consolidating concrete (SCC), a type of concrete that can fill in the formwork without external vibration, is a technology that can offer a solution to the sustainability issues of concrete industry. However, all of the workability requirements of SCC originate from a higher powder content (compared to conventional concrete) which can increase both the cost of construction and the environmental impact of SCC for some applications. Ecological SCC, Eco-SCC, is a recent development combing the advantages of SCC and a significantly lower powder content. The maximum powder content of this concrete, intended for building and commercial construction, is limited to 315 kg/m[superscript 3]. Nevertheless, designing Eco-SCC can be challenging since a delicate balance between different ingredients of this concrete is required to secure a satisfactory mixture. In this Ph.D. program, the principal objective is to develop a systematic design method to produce Eco-SCC. Since the particle lattice effect (PLE) is a key parameter to design stable Eco-SCC mixtures and is not well understood, in the first phase of this research, this phenomenon is studied. The focus in this phase is on the effect of particle-size distribution (PSD) on the PLE and stability of model mixtures as well as SCC. In the second phase, the design protocol is developed, and the properties of obtained Eco-SCC mixtures in both fresh and hardened states are evaluated. Since the assessment of robustness is crucial for successful production of concrete on large-scale, in the final phase of this work, the robustness of one the best-performing mixtures of Phase II is examined. It was found that increasing the volume fraction of a stable size-class results in an increase in the stability of that class, which in turn contributes to a higher PLE of the granular skeleton and better stability of the system. It was shown that a continuous PSD in which the volume fraction of each size class is larger than the consecutive coarser class can increase the PLE. Using such PSD was shown to allow for a substantial increase in the fluidity of SCC mixture without compromising the segregation resistance. An index to predict the segregation potential of a suspension of particles in a yield stress fluid was proposed. In the second phase of the dissertation, a five-step design method for Eco-SCC was established. The design protocol started with the determination of powder and water contents followed by the optimization of sand and coarse aggregate volume fractions according to an ideal PSD model (Funk and Dinger). The powder composition was optimized in the third step to minimize the water demand while securing adequate performance in the hardened state. The superplasticizer (SP) content of the mixtures was determined in next step. The last step dealt with the assessment of the global warming potential of the formulated Eco-SCC mixtures. The optimized Eco-SCC mixtures met all the requirements of self-consolidation in the fresh state. The 28-day compressive strength of such mixtures complied with the target range of 25 to 35 MPa. In addition, the mixtures showed sufficient performance in terms of drying shrinkage, electrical resistivity, and frost durability for the intended applications. The eco-performance of the developed mixtures was satisfactory as well. It was demonstrated in the last phase that the robustness of Eco-SCC is generally good with regards to water content variations and coarse aggregate characteristics alterations. Special attention must be paid to the dosage of SP during batching.

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Carbon fiber reinforced polymer (CFRP) bars were prestressed for the structural strengthening of 8 T-shaped reinforced concrete (RC) beams of a 21-year-old bridge in China. The ultimate bearing capacity of the existing bridge after retrofit was discussed on the basis of concrete structures theory. The flexural strengths of RC beams strengthened with CFRP bars were controlled by the failure of concrete in compression and a prestressing method was applied in the retrofit. The field construction processes of strengthening with CFRP bars—including grouting cracks, cutting groove, grouting epoxy and embedding CFRP bars, surface treating, banding with the U-type CFRP sheets, releasing external prestressed steel tendons—were introduced in detail. In order to evaluate the effectiveness of this strengthening method, field tests using vehicles as live load were applied before and after the retrofit. The test results of deflection and concrete strain of the T-shaped beams with and without strengthening show that the capacity of the repaired bridge, including the bending strength and stiffness, is enhanced. The measurements of crack width also indicate that this strengthening method can enhance the durability of bridges. Therefore, the proposed strengthening technology is feasible and effective.

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The results of an experimental and numerical investigation involving unstrengthened reinforced concrete (RC) T-beams and precracked RC T-beams strengthened in shear with prestressed carbon fiber-reinforced polymer (CFRP) straps are presented and discussed. The results provide insights into the influence of load history and beam depth on the structural behavior of both unstrengthened and strengthened beams. The strengthened beams exhibited capacity enhancements of 21.6 to 46% compared to the equivalent unstrengthened beams, demonstrating the potential effectiveness of the prestressed CFRP strap system. Nonlinear finite element (FE) predictions, which incorporated the load history, reproduced the observed experimental behavior but either underestimated or overestimated the post-cracking stiffness of the beams and strap strain at higher load levels. These limitations were attributed to the concrete shear models used in the FE analyses.

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This study explores using artificial neural networks to predict the rheological and mechanical properties of underwater concrete (UWC) mixtures and to evaluate the sensitivity of such properties to variations in mixture ingredients. Artificial neural networks (ANN) mimic the structure and operation of biological neurons and have the unique ability of self-learning, mapping, and functional approximation. Details of the development of the proposed neural network model, its architecture, training, and validation are presented in this study. A database incorporating 175 UWC mixtures from nine different studies was developed to train and test the ANN model. The data are arranged in a patterned format. Each pattern contains an input vector that includes quantity values of the mixture variables influencing the behavior of UWC mixtures (that is, cement, silica fume, fly ash, slag, water, coarse and fine aggregates, and chemical admixtures) and a corresponding output vector that includes the rheological or mechanical property to be modeled. Results show that the ANN model thus developed is not only capable of accurately predicting the slump, slump-flow, washout resistance, and compressive strength of underwater concrete mixtures used in the training process, but it can also effectively predict the aforementioned properties for new mixtures designed within the practical range of the input parameters used in the training process with an absolute error of 4.6, 10.6, 10.6, and 4.4%, respectively.