In geotechnical engineering, geological uncertainty and the inherent spatial variability of soil properties are two primary sources of uncertainty, significantly impacting embedded geo-structures. ...While the influence of the soil spatial variability and geological uncertainty has been separately investigated, the coupling effect of these two types of uncertainties on tunnel is still limited. Meanwhile, little effort has been devoted to quantifying the degree of importance of these two types of uncertainties. This study quantitatively evaluates the effects of geological uncertainty and soil spatial variability on tunnels, and first assess the importance of these two uncertainties based on a novel approach. Improved coupled Markov chain model was utilized to simulate the geological uncertainty based on sparse and limited boreholes. The coefficient of variation (COV) was adopted to characterize the different levels of soil spatial variability in this study. The results illuminated the role of geological uncertainty and spatial variability in tunnel probabilistic analysis by comparing different boreholes schemes and COV. In addition, the geological uncertainty index (GUI) was presented to quantitatively estimate the degree of uncertainty in the simulated strata. A novel approach for evaluating the significance of geological uncertainty and soil spatial variability on tunnel was proposed. Based on the Kendall correlation coefficient, the classification was also given, divided into high, strong, moderate, weak and no correlation. The geological uncertainty should be considered in high and strong correlation groups. Both two types of uncertainties should be simultaneously taken into account in moderate and weak correlation divisions.
In an urbanization process, infrastructure elements such as tunnels and deep excavations are widely used to service the development of cities. Owing to the lengthy geological processes of ...geomaterials and the limited availability of site-specific test data, soil and rock properties exhibiting spatial variability are frequently encountered in geological and geotechnical engineering. This paper presents a comprehensive review of the application of spatial variability in tunneling and deep excavation over the past 20 years. It is found that the spatial variability is generally modeled as a random field (RF) in finite element software, based on random field theory (RFT). This model has been widely used in the design, stability evaluation, and probabilistic analysis of tunnels and excavations. Previous works have proven that the performance of tunnels and deep excavations can be better captured by considering the spatial variability, as compared with conventional deterministic analysis methods. Nonetheless, current research still faces many factual scientific problems. Therefore, this paper also identifies some research gaps, as well as recommendations for further investigations.
The probabilistic design of complex structure usually involves the features of numerous components, multiple disciplines, nonlinearity, and transients and, thus, requires lots of simulations as well. ...To enhance the modeling efficiency and simulation performance for the dynamic probabilistic analysis of the multicomponent structure, we propose an improved decomposed-coordinated Kriging modeling strategy (IDCKMS), by integrating decomposed-coordinated (DC) strategy, extremum response surface method (ERSM), genetic algorithm (GA), and Kriging surrogate model. The GA is used to resolve the maximum-likelihood equation and achieve the optimal values of the Kriging hyperparameter θ . The ERSM is utilized to resolve the response process of outputs in surrogate modeling by extracting the extremum values. The DC strategy is used to coordinate the output responses of analytical objectives. The probabilistic analysis of an aeroengine high-pressure turbine blisk with blade and disk is conducted to validate the effectiveness and feasibility of this developed method, by considering the fluid-thermal-structural interaction. In respect of this investigation, we see that the reliability of turbine blisk is 0.9976 as the allowable value of radial deformation is 2.319 × 10 −3 m. In terms of the sensitivity analysis, the highest impact on turbine blisk radial deformation is of gas temperature, followed by angular speed, inlet velocity, material density, outlet pressure, and inlet pressure. By the comparison of methods, including the DC surrogate modeling method (DCSMM) with quadratic polynomial, the DCSMM with Kriging, and the direct simulation with finite-element model, from the model-fitting features and simulation performance perspectives, we discover that the developed IDCKMS is superior to the other three methods in the precision and efficiency of modeling and simulation. The efforts of this article provide a highly efficient and highly accurate technique for the dynamic probabilistic analysis of complex structure and enrich reliability theory.
•Probabilistic analysis framework is proposed for structural creep-fatigue failure.•The eDSCA-driven neural network is built for damage and lifetime predictions.•Reliability-based creep-fatigue ...evaluation diagrams are constructed.•Novel data classification scheme is developed with enhanced data correlation.
To achieve a high-reliability design of high-temperature structures with a feasible balance between accuracy and efficiency, the physics-based probabilistic assessment for creep-fatigue failure is proposed under the probabilistic Linear Matching Method (pLMM) framework. At the physical level, the structural failure mechanism is reflected in the prepared training database, which is generated by the direct method procedures. And to efficiently express the relationship between design parameters and structural responses implicitly, the direct method-driven artificial neural network is built with the superior fitting quality of damage and lifetime. With the benchmarks provided, the applicability of the proposed probabilistic analysis approach for risk management of critical infrastructures is demonstrated, where the reliability-based creep-fatigue evaluation diagram is established according to different requirements. Furthermore, a novel data classification scheme is proposed to deal with the randomness in creep damage-dominated probabilistic assessment.
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It is challenging to conduct groundwater contamination risk assessment in fractured aquifers containing a large number of complex fractures, especially in a situation where the uncertainty of massive ...fractures and fluid-rock interactions is inevitable. In this study, a novel probabilistic assessment framework based on discrete fracture network (DFN) modeling is proposed to assess the uncertainty of groundwater contamination in fractured aquifers. The Monte Carlo simulation technique is employed to quantify the uncertainty of fracture geometry, and the environmental and health risks of the contaminated site are probabilistically analyzed in conjunction with the water quality index (WQI) and hazard index (HI). The results show that the contaminant transport behavior in fractured aquifers can be strongly affected by the distribution of the fracture network. The proposed framework of groundwater contamination risk assessment is capable of practically accounting for the uncertainties involved in the mass transport process and effectively assessing the contamination risk of fractured aquifers.
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•A highly heterogeneous discontinuity exists in the fractured aquifer.•Numerical simulation based on discrete fracture network (DFN) is a feasible method to study fractured aquifers.•Fracture uncertainty should be taken into account in contamination prediction and risk assessment.•Monte Carlo simulation is used to establish a probabilistic assessment framework.•Risk assessment is performed using assessment index and probability of contamination occurrence.
•Safety and resilience are the fundamental issues in urban gas pipeline network (UGPN) reliability management.•A methodology for evaluating the safety and resilience of UGPN was developed.•The safety ...of the UGPN can be improved by implementing the planned resilience management.•Two different case studies are implemented and discussed.•System resilience and risk simulation extended beyond the original time range are reference.
Urban gas pipeline networks (UGPN) provide important support for high-quality urbanization. Therefore, it is imperative to analyze and assess the potential failures of UGPN. A novel probabilistic analysis method is proposed for assessing the safety and resilience of UGPN. Firstly, Bow-Tie analysis is used to identify faults. Then, a four-dimensional resilience assessment network is developed. Bayesian network is utilized to model the relationship between the variables, while dynamic Bayesian network is used to consider the dynamic nature of the system. The results of the study show that the proposed model can accurately estimate faults, consequences, and influence paths. Furthermore, the resilience analysis shows that monitoring the objective conditions is crucial and that the initial failure probability of the UGPN decreases from 0.03767 % to 0.01435 % when connected to a resilience network, indicating that considering resilience can effectively improve the reliability and safety of the UGPN. Two application examples are presented in the paper to validate the functionality of the proposed model. The proposed model can be used to set the state of UGPN to predict the probability of occurrence of a specific event and its consequences and to simulate the improvement trend of UGPN based on the direction of focus of future work.
•Comprehensive physical modelling of corroded cast iron pipelines.•Statistical analysis of pipe failure data.•Use of hybrid reliability method for probabilistic analysis.•Comparison between physical ...modelling and statistical analysis of observed data.
Cast iron was the dominant material for buried pipes for water networks prior to the 1970s in Australia and overseas. At present, many water utilities still have a significant amount of ageing cast iron pipes. Cast iron is a brittle material and when large diameter cast iron pipes (diameters above 300mm) further deteriorate, the consequences of failure can be substantial. Focusing on the likelihood of failure to assist risk assessment, this paper examines the performance of large-diameter cast iron pipes using probabilistic analysis, incorporating uncertainties of governing variables. Finite element analysis is first conducted to study the physical mechanism of buried pipes subjected to complex environmental conditions. The deterioration of cast iron pipes due to corrosion is considered on the basis of recent research. The uncertainties of governing variables, such as the physical properties of soil, cast iron, water pressure and corrosion patterns, in pipe failure risk assessment are considered. Using probabilistic physical modelling, the lifetime probability of failure is derived and a time-dependent sensitivity analysis is presented. The results of this probabilistic physical modelling are compared with cohorts of failure data from two Australian water utilities to examine the underlying trends from both physical modelling and statistical analysis.
•Voltage harmonics and unbalance in network with EVs, non-linear loads, and PVs are studied.•Probabilistic methodology is proposed to study power quality in residential network.•Different EV charging ...strategies and locations are compared.•Interaction of residential loads and EVs can improve quality of power indices.•Compliance with power quality limits is established probabilistically.
This paper presents a probabilistic methodology to assess the power quality impact (harmonics and voltage unbalance levels) in low voltage residential networks with increased simultaneous integration of nonlinear loads and electric vehicles. The uncertain behavior of these loads is considered in the model by applying a probabilistic approach that accounts for their stochastic allocation and performance. In particular, different electric vehicle charging locations and modes of charge are assumed for the analysis of the electric car impact and weekly demand profiles are considered for the nonlinear loads. A Monte Carlo simulation is performed in order to obtain a probabilistic assessment of power quality with penetration levels of disturbing loads estimated for year 2030 for different penetration levels of electric vehicles in the system and including the interaction with photovoltaic generation. The methodology proposed is applied to the IEEE European Low Voltage network and to a larger 471-buses residential network and results are compared to EN50160 standard limits as the reference framework for low voltage power quality indices.
The stiffness prediction of textile composites has been studied intensively over the last 20years. It is the complex yarn architecture that adds exceptional properties but also requires ...computationally expensive methods for the accurate solution of the homogenization problem. Braided composites are of special interest for the aerospace and automotive industry and have thus drawn the attention of many researchers, studying and developing analytical and numerical methods for the extraction of the effective elastic properties. This paper intends to study the effect of uncertainties caused by the automated manufacturing procedure, to the elastic behavior of braided composites. In this direction, a fast FEM-based multiscale algorithm is proposed, allowing for uncertainty introduction and response variability calculation of the macro-scale properties of 3D braided composites, within a Monte Carlo framework. Artificial neural networks are used to reduce the computational effort even more, since they allow for rapid generation of large samples when trained. With this approach it is feasible to apply a variance-based global sensitivity analysis in order to identify the most crucial uncertain parameters through the costly Sobol indices. The proposed method is straightforward, quite accurate and highlights the importance of realistic uncertainty quantification.
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