This paper demonstrates reliability-based design for tensile rupture and pullout limit states for mechanically stabilized earth (MSE) walls constructed with geosynthetic (geogrid) reinforcement. The general approach considers the accuracy of the load and resistance models that appear in each limit state equation plus uncertainty due to the confidence (level of understanding) of the designer at the time of design. The reliability index is computed using a closed-form solution that is easily implemented in a spreadsheet. The general approach provides a quantitative link between nominal factor of safety, which is familiar in allowable stress design practice, and reliability index used in modern civil engineering reliability-based design practice. A well-documented MSE wall case study is used to demonstrate the general approach and to compare margins of safety using different load and resistance model combinations. A practical outcome from the case study example is the observation that the pullout limit state is much less likely to control design than the ultimate tensile rupture state for walls with continuous reinforcement coverage. The more accurate “simplified stiffness method” that is used to compute tensile loads in the reinforcement under operational conditions is shown to generate a more cost-effective reinforcement option than the less accurate American Association of State Highway and Transportation Officials (AASHTO) simplified method.

Deterioration modeling is an important analytical component in infrastructure asset management. It concerns the prediction of performance and remaining service life of assets of different designs under omnifarious working environments. For long-term prediction, it also requires to characterize maintenance effectiveness because maintenance activities do not necessarily bring an asset to a completely renewed status. Deterioration modeling research has for decades been largely focusing on the modeling of the natural deterioration process per se, whereas the modeling of maintenance effectiveness is only a recent focus of investigation, mainly in pavement research. In practice, the asset conditions immediately before and after a given maintenance treatment both are not often known. This has made the modeling of maintenance effectiveness and long-term deterioration prediction a challenging task. To bridge the gap, this paper presents a novel approach that integrates the modeling of deterioration and maintenance effectiveness into one process. The natural deterioration of asset performance is modeled as a continuous-time Markov chain, whereas the effectiveness of a maintenance measure is modeled as a discrete-time Markov chain. To account for missing condition data before and after the maintenance event, the paper also develops a robust statistical method based on Markov chain Monte Carlo simulation. A real-life case study on a municipal sewer pipe system is carried out for demonstration of the proposed integrated modeling approach. The functional deterioration of sewer pipes and the effectiveness of flushing operations that target to bring flow capacity to intact state are modeled. Influences of pipe length, diameter, slope, and sewershed area are examined. The present work is a valuable step toward development of evidence-based risk-informed asset management framework.

A total of 123 measured maximum nail-load data were collected from instrumented soil nail walls reported in the literature. Filtered data sets corresponding to short-term and long-term measurements were used to evaluate the accuracy of the current Federal Highway Administration (FHWA) simplified method to calculate maximum nail loads under operational conditions. The accuracy of load predictions was quantified by the mean and coefficient of variation (COV) of the ratio (bias) of measured load to predicted load. Data in short-term and long-term categories were also investigated according to frictional-cohesive and frictional soil types. Based on the available data, the current FHWA simplified method was found to overestimate both long-term and short-term maximum nail loads on average. The spreads in prediction accuracy measured by the COV of bias were 38 and 52% for long-term and short-term data, respectively. Large spreads in prediction accuracy were also found using data for walls with cohesive-f...

Cross correlations between nominal load and resistance terms in limit state functions for geotechnical soil–structure interaction problems can be expected. A closed-form solution for the reliability index for a simple linear limit state function is used to examine the influence of nominal load and resistance correlations on computed margins of safety. The formulation also includes the contribution of the underlying accuracy of the load and resistance equations (method bias) and bias dependencies with the magnitude of nominal load and resistance values assumed in the limit state design function. Sensitivity analyses and example problems for the external sliding limit state for a cantilever wall and the pullout limit state for internal stability of reinforced soil walls with different soil reinforcement types are presented. Ignoring nominal correlations where they exist is shown to underestimate the reliability index in some cases and to overestimate the reliability index in other cases. In the example problems, these differences are shown to exceed one order of magnitude in terms of probability of failure, but in the sensitivity analyses using a wider range of input parameter values, the differences can be several orders of magnitude.

A general approach for calibration of resistance factors for load and resistance factor design (LRFD) of pullout and tensile failure internal limit states of soil nail walls is presented. The calibration of resistance factor values includes the influence of nominal load and nominal resistance model type and accuracy (method bias), bias dependencies, uncertainty in nominal load and resistance values, and possible cross-correlation (dependency) between nominal load and resistance terms. The current default Federal Highway Administration simplified method and an improved version are used to compute maximum nail loads under operational (in-service) conditions. Nail pullout capacity is calculated using the effective stress method adopted in Hong Kong and a more accurate modified version. Resistance factors are calibrated for a range of load factors and target reliability indexes. Example design outcomes for internal limit states of a soil nail wall using different load and resistance model combinations are presented. The benefit of using the more accurate improved load and resistance models for soil nail wall pullout design is demonstrated by a reduction in total length of soil nails.