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6.Databaseofloaddata Miyata and Bathurst (2019) developed a database of 113 measured steel grid loads (T max) from 11 instrumented steel grid walls constructed with cohesive-frictional soils and purely frictional soils. Reliability-based design and analysis for internal limit states of steel grid-reinforced mechanically stabilized earth walls. Thegeneralapproachdescribedin thispaperisanimportantcontributiontonext-generationanalysis and design using modern concepts of RBD for internal stability design of both steel and polymeric soil-reinforced MSE walls and is thus in alignment with the international movement towards RBD for geotechnical structures (e.g., Low and Phoon 2015;Phoon2017;PhoonandChing2015;ISSMGE2017). Allen et al. (2001, 2004) quantified the level of accuracy of the Simplified Method for steel MSE walls using bias statistics deduced from a large number of instrumented walls. 1.Introduction This paper is focused on reliability-based design (RBD) and analysis for tensile strength and pullout limit states of steel grid-reinforced mechanically stabilized earth (MSE) walls constructed with frictional soils and subjected to typical operational conditions. [Extracted from the article]

Abstract: North American design codes are now committed to the development of load and resistance factor design (LRFD) for reinforced soil structures including internal stability limit states. Reliability-based analysis is required to carry out these calibrations. A framework for LRFD calibration has been proposed by the writers that requires bias statistics for load and resistance terms for each limit state function. In this paper the formulation of the limit state for reinforcement tensile rupture is developed and the component strength-reduction bias statistics identified. The paper describes how to compute bias statistics from project-specific installation damage trials for use in reliability-based design for the reinforcement rupture limit state or using data from multiple sources for LRFD calibration. A database of results from field installation damage trials on 103 different geosynthetic products was collected from 20 different sources. A total of 799 and 2248 in-air tensile test results were reviewed for undamaged and damaged geosynthetic specimens, respectively. This database is used to compute installation damage bias statistics for six different categories of geosynthetic and four categories of backfill soils classified according to the D 50 particle size. A practical outcome is that for analysis purposes, bias statistics can be grouped into two ranges for each geosynthetic type based on D 50 of the soil greater than or less than 19 mm. The paper shows how bias statistics together with load and resistance factors for the geosynthetic rupture limit state function recommended by can be used to calculate probability of failure using Monte Carlo simulation and demonstrates the sensitivity of probability of failure to magnitude of installation damage bias statistics. The installation damage data is valuable for future LRFD calibration to select resistance factors for use in design codes for the geosynthetic rupture limit state in reinforced soil structures. [Copyright &y& Elsevier]