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Revision of the Spanish quality control procedure for rofills and random fillings
R-Evolucionando el transporte
Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional
Fecha de publicación
Trabajo presentado en: R-Evolucionando el transporte, XIV Congreso de Ingeniería del Transporte (CIT 2021), realizado en modalidad online los días 6, 7 y 8 de julio de 2021, organizado por la Universidad de Burgos
Infrastructure quality control must be done through an adequate control process, which must be well planned, programmed and executed. It implies the revision of the specific control procedures as part of the general objective of continuous improvement. It must be applied to the construction of quarries with stone materials, called rockfills for large sizes or random fillings for intermediate products. There continues to be a problem in terms of compacting control methods in the execution of these diggings, with little practical development of new techniques when the spread is of good – quality material an aspect that must be revised in order to ensure the quality of the final result extended and compacted. The current procedures for Quality Control in rock compaction have limited operability. For example, the granulometric analysis with macro-pits (4m3) it is have done with heavy fractions, being a destructive testing. The average density control by nuclear methods has high heterogeneity, low performance and low thickness tested. The topographic measurement settlement is the most accurate, but it is a poorly referenced method. For the wheel impression test, the required values do not impose any limitation. This research studies the application use to granites, slates and granitic alteration soils stabilized using cement. The necessary field and laboratory works were developed in order to elaborate new test procedures for a proposed compaction control in rocks. The compaction control procedures revised were wheel impression test, topographic settlement and plate load test (PLT). Doing simple regression on SPSS, in which any predictor outcome variable (dependent) should be placed (independent). An analysis of variance ANOVA shows the sums of squares and the degrees of freedom associated with each: is significant at p < 0,05. There is less than 0,5% chance that an F Levene – ratio this large would happen if the null hypothesis were true.
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