Bedrock-soil layer slopes (BSLSs) are widely distributed in nature. The existence of the interface between bedrock and soil layer (IBSL) affects the failure modes of the BSLSs, and the seismic action makes the failure modes more complex. In order to accurately evaluate the safety and its corresponding main failure modes of BSLSs under seismic action, a system reliability method combined with the upper bound limit analysis method and Monte Carlo simulation (MCS) is proposed. Four types of failure modes and their corresponding factors of safety (Fs) were calculated by MATLAB program coding and validated with case in existing literature. The results show that overburden layer soil's strength, the IBSL's strength and geometric characteristic, and seismic action have significant effects on BSLSs' system reliability, failure modes and failure ranges. In addition, as the cohesion of the inclination angle of the IBSL and the horizontal seismic action increase, the failure range of the BSLS gradually approaches the IBSL, which means that the damage range becomes larger. However, with the increase of overburden layer soil's friction angle, IBSL's depth and strength, and vertical seismic actions, the failure range gradually approaches the surface of the BSLS, which means that the failure range becomes smaller.

The frequent occurrence of extreme rainfall events often triggers levee slope failure (LSF), which, due to the levee effect, significantly damages the roads behind the levee. This paper presents a novel framework for the quantitative risk assessment of roads posed by LSF. Within the framework, the innovative integration of Monte Carlo simulation (MCS) and Material point method (MPM) provides a unique solution for simulating the complicated dynamic relationship between LSF and road destruction. MCS generates precise failure scenarios for MPM simulations, overcoming the limitations of traditional approaches in addressing uncertainty in complex scenario systems. With its technical superiority in capturing post-failure deformations, MPM offers critical insights for assessing road exposure and vulnerability. The framework also accounts for indirect losses from road disruptions, which have long been overlooked. The application of the framework to the risk assessment of the road behind the Shijiao Levee in the Pearl River Basin fully demonstrates its practicality and robustness. Compared to traditional risk assessment methods, the proposed framework provides a more refined dynamic evaluation, facilitating the formulation of more effective disaster mitigation strategies.

Ice and water coexist in frozen soil, and their respective contents (ice content, theta i; unfrozen water content, theta u) are critical factors influencing the mechanical properties of frozen soil. Currently, these two parameters are measured separately. Existing measurement methods require specialized equipment, are time-consuming. To improve measurement efficiency, this paper proposes an inverse analysis surrogate model, which can simultaneously predict both theta i and theta u within one minute. The method process is as follows: 1. A three-dimensional numerical model is established to simulate the transient heat conduction in frozen soil under heat pulse. 2. Six parameters (theta i, theta u, rho s, lambda s, Cs, Gs) need to be determined for each simulation. Through Monte Carlo sampling of six parameters, thousands of numerical simulations are performed. Then, a dataset comprising thermal response curves (TRC) labeled with (theta i, theta u, rho s, lambda s, Cs, Gs) is established. 3. A machine learning algorithm is used, where TRC and soil property parameters serve as inputs, and (theta i, theta u) as outputs. 4. In the laboratory, soil property parameters are measured, and in the field, TRC within one minute of frozen soil is measured in real-time. By inputting soil property parameters and TRC into the machine learning model, (theta i, theta u) can be obtained in real-time.The method was validated through laboratory experiments. The results show that with TRC and rho s, lambda s, Cs as inputs, mean absolute errors (MAE) for theta i and theta u were 2.3 % and 3.1 %, respectively. The proposed method significantly improves measurement efficiency, allowing for the simultaneous measurement of theta i and theta u within one minute.

The structural characteristics of soil-rock mixture (SRM) slopes, including the content, shape, size, and spatial distribution of rock blocks, can significantly influence their failure mechanisms and factor of safety (FOS). Defining the structural characteristics of SRM slopes for stability analysis remains challenging. This study proposes a method for establishing random models and evaluating the statistical properties of the FOS values of SRM slopes. Accordingly, the SRM slope models were constructed by considering the random properties of the shape, size, and spatial distribution of rock blocks in the slope domain. A slope failure criterion based on energy changes and the combined subroutines of USDFLD and URDFIL was implemented in the ABAQUS finite element software to determine the FOS values of the SRM slopes. Monte Carlo simulations were performed to assess the statistical properties of the FOS for random SRM slopes varying rock block properties. The results indicated that when the rock block content was greater than 30%, the stability of SRM slopes considerably increased. For a rock block content of 40%, the effect of rock block size on the SRM slope stability followed two different trends: the mean FOS value tended to decline and subsequently increased as rock block size increased. However, this trend was not observed on SRM slopes with a 30% rock block content. Besides, the dispersion of the FOS values gradually increased with increasing rock content and rock block size. Furthermore, the soil-rock interface strength affected the stability and failure mechanism of SRM slopes. These findings enhance comprehension of the SRM slope stability assessment and demonstrate improved accuracy in predicting and mitigating damage.

The inclusion of calcite precipitates (CaCO3) in soft soil can improve the mechanical properties. Understanding the variability in sand stiffness due to heterogeneous precipitates is crucial for stiffness evaluation and prediction. A novel discrete element-Monte Carlo (DE-MC) method was proposed to quantify the sand stiffness variability induced by stochastic distributions of calcite precipitates, specifically focusing on shear wave velocity (Vs) as an indicator of soil stiffness. A total of 1972 samples were constructed to simulate stochastic spatial distributions of calcite precipitates. Through joint stochastic analysis, the preferential paths formed by calcite clusters were identified as significant contributors to Vs variability. The normalized connectivity per unity distance contact weight (Cd,n) exhibited the most correlated relation with Vs. Two weight selection methods were applicable for using Cd,n to characterize and predict Vs. The results suggest that the DE-MC method has the potential to assess the variability in sand stiffness quantitatively.

This paper presents a reliability analysis of circular footings on unsaturated soils. Two methods were used to capture the unsaturated soil behavior: implementing two elastoplastic constitutive models, including the Barcelona Basic Model (BBM) and the Sun Model (SM), which are explicitly proposed for unsaturated soils, and incorporating the apparent cohesion in the Mohr-Coulomb Model (MCM). The effect of soil suction on the bearing capacity of circular footings was investigated. It was shown that for low values of suction, the bearing capacities obtained from MCM were higher than those obtained from BBM and SM. However, as suction increased, MCM tended to predict lower bearing capacities. In practice, geotechnical engineers are still concerned with the measurement and determination of suction as a key stress state variable of unsaturated soils. In this context, the Monte Carlo simulation technique has been incorporated into numerical modeling for the investigation of the effect of suction uncertainties on the bearing capacity. Uncertainties associated with the suction value were modeled as normally and log-normally distributed random variables. It was shown that assuming a normal distribution for suction resulted in slightly lower probabilistic bearing capacity values (i.e., more conservative design) compared to the log-normal distributions. The results emphasized the important role of the coefficient of variation (COV) of suction in determining the probabilistic bearing capacity. A negative linear correlation was observed between the COV of suction and the probabilistic bearing capacity. Finally, a simple relationship was proposed to estimate the probabilistic bearing capacity of the circular footing in an unsaturated soil when its deterministic value and the COV of suction are known.

Among the techniques used to control or mitigate the structural vibrations induced by dynamic input, such as wind and earthquake, the dissipative coupling is one of the most applied, especially for its ease of implementation. Indeed, for example in large urban areas, it is common to find adjacent structures where the space between the buildings becomes smaller. To optimally select the visco-elastic features of the dissipative device to be used, the paper retraces the path followed by the previous scientific works proposing new design criteria. Such criteria are based on the nonlinear stochastic response of two simple oscillators linked by a damper whose hysteretic behavior is represented by a Bouc-Wen model. A state-space formulation of the equations of motion has been adopted to facilitate the analysis of the dynamic response. At the same time, the loading is hypothesized as a zero-mean Gaussian excitation. Consequently, the nonlinear response has been approximately evaluated by the equivalent linearized standard deviations for both displacements and accelerations. Subsequently, formulations of objective functions, based on the Minimax and Total Energy of the equivalent linearized stochastic response, have been applied to determine the optimal configurations of the coupled system. The influence of both noise power amplitude and soil typology on the designed systems has been also investigated. Suggestions related to the path to achieve pre-fixed targets (as balancing of displacements and accelerations) are provided.

The use of probabilistic analysis (PA) of slopes as an effective method for evaluating the uncertainty that is so pervasive in variables has become increasingly common in recent years. This study presents a case study which was conducted to demonstrate the efficiency of an embankment which consists of an 11.693 m-high soil slope, placing emphasis on PA and reliability evaluation. The investigation employs Monte Carlo simulation (MCS) and subset simulation (SS) techniques, considering seismic coefficients (kh) of 0.12 for Zone-III and 0.14 for Zone-IV, along with varying pore water pressure ratios (ru=0.0, r u = 0 . 05 , and r u = 0 . 10 ). MCS with 10,000 samples was used to test the probabilistic response of the proposed embankment. This work also discusses the results of SS, in which 1,400 samples are generated from UPSS 3.0 Excel add-ins, which permits rapid PA in such a way that they progressively shift toward the failure zone in successive stages. The study delves into the impact of uncertainty on the probability of failure ( p f ) . Findings reveal an increased p f with rising coefficients of variation, r u , and kh values, underscoring the sensitivity to soil parameter variations. SS outperforms MCS in simulating low probabilities, demanding smaller sample sizes and less computational time. Furthermore, the machine learning technique has been used to optimize the worst p f condition. In this current research, three neural network-based models, namely recurrent neural network, long short-term memory (LSTM), and Bayesian neural network, have been used. Based on the performance of the models, the three neural network-based models were compared in the testing phase, and the proposed LSTM outperformed the other neural networks (R2 = 0.9962 and root mean square error = 0.0051).

The structural integrity of buried pipelines is threatened by the effects of Permanent Ground Deformation (PGD), resulting from seismic-induced landslides and lateral spreading due to liquefaction, requiring accurate analysis of the system performance. Analytical fragility functions allow us to estimate the likelihood of seismic damage along the pipeline, supporting design engineers and network operators in prioritizing resource allocation for mitigative or remedial measures in spatially distributed lifeline systems. To efficiently and accurately evaluate the seismic fragility of a buried operating steel pipeline under longitudinal PGD, this study develops a new analytical model, accounting for the asymmetric pipeline behavior in tension and compression under varying operational loads. This validated model is further implemented within a fragility function calculation framework based on the Monte Carlo Simulation (MCS), allowing us to efficiently assess the probability of the pipeline exceeding the performance limit states, conditioned to the PGD demand. The evaluated fragility surfaces showed that the probability of the pipeline exceeding the performance criteria increases for larger soil displacements and lengths, as well as cover depths, because of the greater mobilized soil reaction counteracting the pipeline deformation. The performed Global Sensitivity Analysis (GSA) highlighted the influence of the PGD and soil-pipeline interaction parameters, as well as the effect of the service loads on structural performance, requiring proper consideration in pipeline system modeling and design. Overall, the proposed analytical fragility function calculation framework provides a useful methodology for effectively assessing the performance of operating pipelines under longitudinal PGD, quantifying the effect of the uncertain parameters impacting system response.

The objective of this study is to present a method for developing fragility curves for soil liquefaction that align with seismic hazards using Monte Carlo simulation. This approach can incorporate all uncertainties and variabilities in the input parameters. The seismic parameters, including earthquake magnitude (M) and associated peak ground acceleration (PGA), are jointly considered for the liquefaction assessment. The liquefaction potential and the resulting damages obtained by this method are more realistic. A case study is conducted using data from a sand-boil site in Yuanlin, Changhua County, where liquefaction occurred during the 1999 Chi-Chi earthquake in Taiwan. The findings indicate that the liquefaction potential index, IL, the post-liquefaction settlement, St, and the liquefaction probability index, PW, are all appropriate parameters for assessing liquefaction damages. The fragility curves for soil liquefaction developed through this method can support the performance-based earthquake engineering (PBEE) approach, provide guidance for liquefaction evaluation to the Taiwan Earthquake Loss Estimation System (TELES), and serve as a foundation for scenario simulation and an earthquake early warning system for liquefaction damages.