AbstractThis work presents a critical evaluation of the model factor for the mobilized strength design (MSD) method for cantilever deflection in undrained soft to medium-stiff clay. The model factor is characterized in two parts. A correction factor η is first defined as the ratio between the wall-top deflection computed from the FEM and its corresponding value computed from the MSD. The hardening soil with a small-strain model is used in FEM. The statistics of η are evaluated using 82 numerical simulations. Because η is not random, and therefore cannot be modeled directly as a random variable, it is decomposed as a product of a systematic part (f) and a random part (η∗). The random part is modeled as a lognormal random variable with a mean of 1.01 and a coefficient of variation (COV) of 0.18. The model factor for FEM (eFEM) is next defined as the ratio between the measured wall-top deflection and the corresponding FEM result. The statistics of eFEM are evaluated using 45 field cases. The ratio eFEM is ra...

Closure of "Characterization of Model Uncertainty for Cantilever Deflections in Undrained Clay"

Corner and partition wall effects on the settlement of a historical building near a supported subway excavation in soft soil

Statistical evaluation and calibration of two methods for predicting nail loads of soil nail walls in China

This study compiles a broad database containing 312 measured maximum soil nail loads under operational conditions. The database is used to re-assess the prediction accuracies of the default Federal...

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Mapping soil nail loads using Federal Highway Administration (FHWA) simplified models and artificial neural network technique

Machine learning (ML) approaches have stormed nearly all engineering fields since recent years. However, the situation is somehow subtle in civil engineering practice, especially in the sub-field of geotechnical engineering where data from real-life projects are usually scarce, which in turn prevents development of meaningful mapping tools based on ML techniques. This study first shares a database containing a total of 376 measured horizontal displacements from instrumented soil nail walls reported in the literature. Then, these data are utilized to develop three types of ML models for mapping the wall horizontal displacement along depth, including artificial neural network (ANN), random forest (RF), and support vector machine (SVM). The uncertainties of the ANN, RF, and SVM models are then quantitatively evaluated using bias statistics where bias is defined as the ratio of measured to predicted horizontal displacement. The three ML models are proved to be accurate on average with medium dispersions in prediction, which outperform the existing simple empirical regression models. Probability distribution functions for those biases are also characterized. This study demonstrates that introduction of machine learning approaches into the reliability-based design framework for soil nail walls and other geotechnical structures is promising.

Mapping horizontal displacement of soil nail walls using machine learning approaches

Statistical prediction of deformations of soil nail walls

This paper presents the development of a simplified model for estimation of maximum nail loads during or at completion of construction of soil nail walls. The developed simplified nail load model consists of two multiplicative components: the theoretical nail load and the correction factor. The theoretical nail load is computed as the product of lateral active Earth pressure at nail depth and the nail tributary area. The correction factor is introduced to account for the difference between the theoretical and the measured nail loads. A total of 85 measured nail load data were collected from the literature; out of which, 74 were used to develop a simple formulation for the correction factor, whereas the remaining 11 were used for validation. After the validation, the model was updated using all 85 data. The updated simplified nail load model was demonstrated to be accurate on average (mean of model factor equal to 1), and the spread in prediction quantified as the coefficient of variation of the model factor was about 40%. Here, model factor is the ratio of measured to estimated nail load. The randomness of the model factor was also verified. Finally, the model factor was demonstrated to be a lognormal random variable. The proposed simplified nail load model is beneficial due to its simplicity and quantified model uncertainty; thus it is practically valuable to both direct reliability-based design and load and resistance factor design of soil nail wall internal limit states.

/pdf/probabilistic-prediction-of-maximum-tensile-loads-in-soil-2b4fl712m9.pdf

Probabilistic Prediction of Maximum Tensile Loads in Soil Nails

This study first presents accuracy assessments of two default models proposed in two popular soil nail wall design specifications and their calibrated versions for estimation of maximum soil nail loads under operational conditions. The assessments are based on a general nail load database reported in the literature. Evaluation results showed that predictions using the default and calibrated models are unsatisfactory as the dispersions are high to extremely high. Simple calibration terms are introduced to the calibrated models for performance enhancement. The recalibrated models are shown to be accurate on average, and the prediction dispersions are significantly reduced. The model biases for the above six models are demonstrated to be lognormal random variables. Individual reliability-based designs for the nail pullout and nail-in-tension limit states using the six models are carried out. System reliability of nails against internal failures is also explored. The correlation between the two limit states is investigated, and its influences on system reliability evaluation are discussed. The practical value of model calibration is demonstrated as using the recalibrated models for nail designs leads to the most cost-effective design outcomes. This study represents a solid step toward development of reliability-based design framework for soil nail walls.

Yongqiang Hu

Papers

Statistical prediction of deformations of soil nail walls

Probabilistic Prediction of Maximum Tensile Loads in Soil Nails

Assessment and calibration of two models for estimation of soil nail loads and system reliability analysis of soil nails against internal failures