This chapter discusses the major mechanisms for strain localization in geomaterials (i.e. soil and rock) and their possible implications for slope instability. In certain geological environments and slopes subjected to external forces, soil or rock does not completely fail, but deformation zones are created due to intense strain localization, resulting in morphological and geotechnical changes. Strain localization is a feature of elastoplastic materials where shear bands are formed as a result of inhomogeneous material deformation leading to permanent expressions of intense strain zones. Strain localization can be considered an instability in material constitutive behavior. Localized elevated pore pressures can drive the growth of a slip-failure surface in a manner similar to that observed during earthquake nucleation. The material within a deformation band is thought to strain harden as a result of the deforming mechanism. In porous geomaterials such as sandstone, deformation bands are the most common strain localization feature. The localized strain bands can occur in a shear or compaction form. Particle grains within deformation bands tend to be smaller, more compact, possess stronger preferred orientations, and have more elongate shapes than particles outside the band. To illustrate, the Oso Landslide that struck Snohomish County, Washington on Saturday, March 22, 2014, resulting in 43 fatalities, several injuries, and significant destruction of property is discussed. Observed sand boils and other signs of confined elevated water pressure reaching or exceeding total overburden pressure point to liquefaction at depth in Zones E and F during the Oso landslide. Strain localization likely occurred during Stage 2 of the failure, triggering the 300m length shearing of the failure surface.

Thanks to its meshfree feature, the smoothed particle hydrodynamics (SPH) has apparent advantages over the traditional finite element method for addressing large deformation problems in geotechnical engineering, e.g., landslides. This chapter provides the essentials of SPH theory and the key ingredients of SPH modeling. Especially, the stability-related issues are investigated and discussed by carrying out some benchmarking examples. Then, the effectiveness of SPH modeling is validated by applying it to a series of granular column collapse and earthquake-induced landslides. Finally, the most recent development of SPH is reviewed, and the Pros/Cons of SPH modeling relative to the conventional grid-based methods and other meshfree methods are explored in detail. Some latest advice on the use of SPH in geotechnical engineering is also involved in this chapter.

The inclusion of reinforcements within the soil mass increases the bearing capacity and reduces the settlement of soil foundation. To achieve optimum bearing capacity with a minimum cost of foundation is a conflicting optimization problem. In this chapter, nondominated sorting genetic algorithm (NSGA-II), a multi-objective optimization algorithm is used to find out the maximum bearing capacity factor with a minimum cost of the foundation. A design methodology considering both the pull out and rupture failure of geosynthetic reinforcement available in the literature is used. The results of the conflicting objectives are presented in terms of the Pareto-optimal set. The NSGA-II is found to be very efficient in identifying the Pareto-optimal set of the reinforced soil foundation. The implementation issue of NSGA-II parameters is discussed. A parametric study is also made to identify the important soil and reinforcement parameters in achieving the Pareto-optimal solution.

Soils are unconsolidated and heterogeneous materials that induce numerous geotechnical challenges due to poor bearing capacity, uniform and differential settlement of building, seepage, scouring, and erosion. Recently developed microbially induced calcite precipitation (MICP) technique is an environmentally friendly and sustainable technique that works on nature's concept of forming rocks and minerals. The natural mineral formation is a quiet slow process that can be expedited by adding a sufficient amount of nutrients to the soil including urea and calcium sources to form calcite crystals between soil grains. However, the application of MICP requires a proper understanding of the kinetics of biogeochemical reactions in the urea hydrolysis and biocementation process. The study includes numerical modeling of precipitation kinetics of calcium carbonate with time and distance in granular soil media, including the factors affecting the rate of urea hydrolysis (i.e., pH and electrical conductivity). The laboratory investigations were also carried out on sand using Sporosarcina pasteurii strain with two different cementation media concentrations, i.e., 0.25 and 0.50M. The experiments were conducted and analyzed after 10 and 20days of biotreatment for calcite precipitation using calcimeter. The presence of calcite between sand grains was identified by SEM images. The predicted results were found in concurrence with experimental results, which proves the validation of the numerical model.

This chapter shows that hard rocks (highly metamorphic and volcanic) under highly confined compression (corresponding to seismic depths) are unexplored experimentally today due to uncontrolled and violent failure even on modern stiff and servo-controlled testing machines. Due to this testing limitation, it is generally accepted that hard rock behavior at these conditions should correspond to the behavior of the conventional Class I and Class II. This approach is widely used for the analysis of dynamic instability associated with earthquakes and shear rupture rockbursts in deep mines. This chapter proposes new experimental results that demonstrate that hard rocks under highly confined compression (σ1>σ2=σ3) exhibit fundamentally different postpeak behavior compared to Class I and Class II. The new results allow proposing some new principles of dynamic instability in the Earth's crust.

Incorporating realistic soil-structure interaction (SSI) in built environment and major infrastructure projects provides significant beneficial role in terms of economics (i.e., better cost benefit), thereby reducing carbon footprints and enhancing green credentials. Generally, SSI is ignored, and often engineers tend to use Winkler's springs to decouple geotechnical analysis and structural analysis. The use of structural springs to mimic soil behavior (both linear and nonlinear) will have limited applications as in many cases we find that the springs do not replicate the actual soil behavior. Use of continuum stiffness somewhat deviates from 2D springs and brings better distribution of forces in 3D analysis. This chapter highlights how SSI can be applied to massive foundations, including nuclear reactor buildings. It also gives advice on how to verify the results using simple models. It is also important to understand the construction sequence and construction process before the results of the SSI can be effectively used in designing the reinforcement in concrete foundations. The current commercial packages that model nonlinear soil constitutive models although have beam and plate elements for modeling, the structural elements do not give satisfactory results for design. Therefore, this article focuses on how designers can overcome this issue and determine the forces in the structure from SSI. Structural engineers need forces to determine the steel and geotechnical engineers need to understand how to model the structural elements in SSI packages. This work gives some examples of major projects and gives guidance to the designers.

A deterministic model for the factor of safety of rock mass against planar failure is developed based on Patton's shear strength criteria considering inclined upper slope. Two geometric cases are considered for the analysis depending upon the location of tension crack with practically occurring conditions such as partially water filled up tension crack, influence of external forces such as seismic accelerations, and rock bolt stabilizing force. Another deterministic model for the factor of safety (FS) of rock mass against plane failure is developed by modifying the Hoek's model for the considered rock slope geometry and forces. A benchmark example has been reanalyzed for a comparative study between the developed models. Based on the results, it appears that the model using Patton's theory might be better to analyze the stability of rock slopes as it accounts undulated failure surface. Parametric studies have also been made to study the effect of the upper slope face, the water level in tension crack, and the seismic coefficients on the FS. It is observed that the FS values decrease with the increase in the upper slope face angle and with the change of water level in tension crack from fully filled with water to dry state, the amount of reduction in the FS values is observed as 28%.

Accurate assessment of undrained shear strength (USS) for soft sensitive clays is a great concern in geotechnical engineering practice. This study applies novel data-driven extreme gradient boosting (XGBoost) and random forest (RF) ensemble learning methods for capturing the relationships between the USS and various basic soil parameters. Based on the soil data sets from TC304 database, a general approach is developed to predict the USS of soft clays using the two machine learning methods above, where five feature variables including the preconsolidation stress (PS), vertical effective stress (VES), liquid limit (LL), plastic limit (PL), and natural water content (W) are adopted. To reduce the dependence on the rule of thumb and inefficient brute-force search, the Bayesian optimization method is applied to determine the appropriate model hyperparameters of both XGBoost and RF. The developed models are comprehensively compared with two comparison machine learning methods and two transformation models with respect to predictive accuracy and robustness under 5-fold cross-validation (CV). It is shown that XGBoost-based and RF-based methods outperform these approaches. Besides, the XGBoost-based model provides feature importance ranks, which makes it a promising tool in the prediction of geotechnical parameters and enhances the interpretability of model.

An accurate numerical simulation of a mechanized tunneling process in an urban area must consider the complex interactions between subsoil, the tunnel boring machine, and ground constructions. Of course, due to the natural origin of the geomaterials, their characteristics deal with randomness. The considerably high level of associated uncertainty inherent in geomaterials may lead to notable deviations in the prognosis of geotechnical structures. To address this issue, a model adaptation framework is presented, which intends to minimize the involved uncertainties in the simulation of mechanized tunneling. In this framework, first parameter identification techniques are developed to reach an adequate soil model for numerical simulations based on measurements. Accordingly, the concept for an optimal measurement campaign is introduced. The optimum observation design is supposed to identify a sensor arrangement, which provides the least uncertainty in the parameter identification process. The optimized measurement concept is developed employing sensitivity indices and other probabilistic tools including Bayesian updating methods. The concept of model adaptation is further developed by involving the field data during an intermediate boring phase in the reliability assessments of the other advancement phases. To do this, a Bayesian updating concept is combined with a Markov chain Monte-Carlo method to evaluate the updated reliability measures, considering the ground settlement as the limit state. Nevertheless, due to the infeasibility of performing an extensive geotechnical site characterization in a spacious project as tunneling, some geological alternation might be overlooked. Here, the adaptation framework proposes a supervised machine learning methodology to predict the geological changes ahead of the TBM. The classification is performed based on different supervised learning algorithms, which assign the obtained characteristics to the predefined geological conditions.

Pile foundations are used for jacket-type offshore structures, which are subjected to large magnitudes of lateral loads due to waves and winds. The ultimate failure of a pile-soil system is catastrophic and excessive lateral deflection of the pile creates operational difficulties. In view of this, the bending moment in the pile is required to be predicted in a rational manner. The p-y concept developed by Reese and Matlock (1956) is popularly being used to estimate pile top deflection and bending moments.