3 research outputs found

    Quality of the surface finish of self-compacting concrete

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    [EN] In this work, surface damage due to the presence of pores in self-compacting concrete specimens is studied and cataloged with the QSI method which simplifies the inspection of concrete samples with optimal results, even in curved areas. 48 test tubes distributed in a total of 12 mixtures were analyzed. The rheology of the concretes was analyzed and controlled. In concretes that obtain viscosities higher than 42 Pa s, a large number of large pores are detected on its surface, compared to concretes with lower viscosity and/or shear stress. The specimen with the worst surface finish that was manufactured (mixture n degrees. 11) had a viscosity of 58 Pa s and a shear stress of 26 Pa (QSI = 5.9%), compared to 14 Pa s and 15 Pa, respectively, of the concrete element that less surface area affected (QSI = 0.6%). The mixes H-2, H-3 and H-12 have the combination of lower values of viscosity and shear stress. This allows obtaining the best surface finishes, with average QSI values, respectively of 1.3% 1.6% and 1.6%. By increasing the flowability of the SCC, the number of pores and their size decrease. The values of viscosity and shear stress must be balanced to ensure an adequate flowability of the SCC.Miñano Belmonte, I.; Benito Saorin, FJ.; Parra Costa, C.; Valcuende Payá, MO. (2020). Quality of the surface finish of self-compacting concrete. Journal of Building Engineering. 28:1-7. https://doi.org/10.1016/j.jobe.2019.101068S1728Miller, S. A., Horvath, A., Monteiro, P. J. M., & Ostertag, C. P. (2015). Greenhouse gas emissions from concrete can be reduced by using mix proportions, geometric aspects, and age as design factors. Environmental Research Letters, 10(11), 114017. doi:10.1088/1748-9326/10/11/114017Valcuende, M., Parra, C., Marco, E., Garrido, A., Martínez, E., & Cánoves, J. (2012). Influence of limestone filler and viscosity-modifying admixture on the porous structure of self-compacting concrete. Construction and Building Materials, 28(1), 122-128. doi:10.1016/j.conbuildmat.2011.07.029Lemaire, G., Escadeillas, G., & Ringot, E. (2005). Evaluating concrete surfaces using an image analysis process. Construction and Building Materials, 19(8), 604-611. doi:10.1016/j.conbuildmat.2005.01.025Pushpakumara, B. H. J., Silva, S. D., & Silva, G. H. M. J. S. D. (2017). Visual inspection and non-destructive tests-based rating method for concrete bridges. International Journal of Structural Engineering, 8(1), 74. doi:10.1504/ijstructe.2017.081672Benito, F.; Valcuende, M.; Parra, C.; Rodríguez, C.; Miñano, I. Acabado superficial de hormigones autocompactantes. Método QSI. In Proceedings of the IV Congreso iberoamericano de Autocompactable, Porto, Portugal, 6–7 July 2015.Tong, X. A new image‐basedmethodfor concrete bridge bottom crack detection. In Proceedings of the International Conference on Image Analysis and SignalProcessing (IASP), Wuhan, China, 21–23 October2011; pp. 568–571.Majchrowski, R., Grzelka, M., Wieczorowski, M., Sadowski, Ł., & Gapiński, B. (2015). Large Area Concrete Surface Topography Measurements Using Optical 3D Scanner. Metrology and Measurement Systems, 22(4), 565-576. doi:10.1515/mms-2015-0046Krolczyk, G. M., Maruda, R. W., Nieslony, P., & Wieczorowski, M. (2016). Surface morphology analysis of Duplex Stainless Steel (DSS) in Clean Production using the Power Spectral Density. Measurement, 94, 464-470. doi:10.1016/j.measurement.2016.08.023Benito Saorin, F., Miñano Belmonte, I., Parra Costa, C., Rodriguez Lopez, C., & Valcuende Paya, M. (2018). QSI Methods for Determining the Quality of the Surface Finish of Concrete. Sustainability, 10(4), 931. doi:10.3390/su10040931Liu, B., & Yang, T. (2017). Image analysis for detection of bugholes on concrete surface. Construction and Building Materials, 137, 432-440. doi:10.1016/j.conbuildmat.2017.01.098García, L., Valcuende, M., Balasch, S., & Fernández-LLebrez, J. (2013). Study of Robustness of Self-Compacting Concretes Made with Low Fines Content. Journal of Materials in Civil Engineering, 25(4), 497-503. doi:10.1061/(asce)mt.1943-5533.0000609Zhu, Z., & Brilakis, I. (2010). Machine Vision-Based Concrete Surface Quality Assessment. Journal of Construction Engineering and Management, 136(2), 210-218. doi:10.1061/(asce)co.1943-7862.0000126Tang, P., Huber, D., & Akinci, B. (2011). Characterization of Laser Scanners and Algorithms for Detecting Flatness Defects on Concrete Surfaces. Journal of Computing in Civil Engineering, 25(1), 31-42. doi:10.1061/(asce)cp.1943-5487.0000073Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., & Fieguth, P. (2015). A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Advanced Engineering Informatics, 29(2), 196-210. doi:10.1016/j.aei.2015.01.008Jahanshahi, M. R., & Masri, S. F. (2012). Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures. Automation in Construction, 22, 567-576. doi:10.1016/j.autcon.2011.11.018Kim, M.-K., Cheng, J. C. P., Sohn, H., & Chang, C.-C. (2015). A framework for dimensional and surface quality assessment of precast concrete elements using BIM and 3D laser scanning. Automation in Construction, 49, 225-238. doi:10.1016/j.autcon.2014.07.010Adhikari, R. S., Moselhi, O., & Bagchi, A. (2014). Image-based retrieval of concrete crack properties for bridge inspection. Automation in Construction, 39, 180-194. doi:10.1016/j.autcon.2013.06.011Kumar, R., & Bhattacharjee, B. (2003). Porosity, pore size distribution and in situ strength of concrete. Cement and Concrete Research, 33(1), 155-164. doi:10.1016/s0008-8846(02)00942-0Kim, M.-K., Wang, Q., Yoon, S., & Sohn, H. (2019). A mirror-aided laser scanning system for geometric quality inspection of side surfaces of precast concrete elements. Measurement, 141, 420-428. doi:10.1016/j.measurement.2019.04.060DIN EN 206‐9:2010 Concrete ‐ Part 9: Additional Rules forSelf‐compacting Concrete (SCC); German version EN 206‐9:2010
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