To improve the moisture susceptibility of concrete structure that will be exposed to water, an admixture should be added. One of the additives that can be added is Xypex C-1000 NF, which a crystalline material. A number of past studies have shown that this additive is effective in improving the moisture resistance of concrete structure. However, the application of this additive in Indonesia was found to be challenging as the performance of the concrete mixture was not as good as expected. This research aims to evaluate the effect of adding various percentages of this additive, various amount of water to be added to the concrete mixture, and the methods of mixing the materials, which are wet and dry methods, by looking at both the slump value and the compressive strength of the concrete specimens. From the research results, it was found that the methods used to mix the additive matter to produce a desirable concrete quality. The wet mix method is the preferable method as it produces less variations in compressive strength test results regardless of the additional water amount. It was also found that, by using both dry and wet mix methods, the usage of Xypex C-1000 NF can improve the compressive strength value.
The Structural Health Monitoring System is part of tool and method for bridge operation and maintenance to make sure that the bridge is in good condition. Deflection is one of the main parameters to be measured and various methods has been introduced to measure deflection. Accuracy and economically are two main aspects when selecting the method for determine the real time deflection. This study presents a method of determining the maximum deflection of simple span continuous bridge using strain data obtained from a strain gauge sensor for simple span steel box girder bridge. Initial data were obtained from FEM simulations under various load conditions to obtain the maximum deflection value and the strain value. Linear regression and quadratic regression were carried out to obtain the relationship between the strains data and the maximum deflection. The accuracy of the maximum deflection value is then compared with the results of the analysis using FEM. It was found that the accuracy is about 91.16 % for quadratic regression and 9.07 for linear regression 90.93 %.
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