Structural Safety最新文献

Seismic resilience of critical infrastructure, such as nuclear power plants, is paramount in ensuring nuclear safety. This study presents a comprehensive analysis of the seismic fragility of nuclear power plants under sequential earthquakes, employing the innovative vine-copula theory. The methodology integrates advanced modeling techniques, including layered shell elements and plastic damage softening constitutive modeling, to capture the intricate behavior of nuclear power plants under seismic loading. The seismic sequence is derived from the Wenchuan earthquake data, considering both mainshocks and aftershocks. A set of random seismic peak ground accelerations (PGAs) is generated based on the distribution of giant earthquake PGAs. Utilizing seismic attenuation theory, corresponding random aftershock PGAs are generated. The resulting mainshock-aftershock sequence, modulated within the real seismic sequence, serves as the input for numerical simulations. The vine-copula theory is employed for multi-dimensional fragility analysis, providing a flexible framework to model the complex nonlinear dependencies among structural response parameters. The vine-copula model is applied to fit seismic response data, allowing the construction of fragility surfaces under sequential earthquakes. This approach, rooted in performance-based earthquake engineering (PBEE), enables a more realistic representation of the seismic risk profile. The findings demonstrate that seismic fragility trends for nuclear power plants increase with higher mainshock and aftershock intensity measures (IMs). The impact of aftershocks on the structural performance, often overlooked in traditional studies, is elucidated through the proposed methodology. The study contributes valuable insights into nuclear safety assessments by quantifying the influence of sequential earthquakes on the fragility of nuclear power plants.

The design of an offshore wind turbine to resist fatigue damage during its whole service life requires to estimate an expectation over the pluri-annual joint statistics of wind and wave variables. Using a full factorial-based integration for the estimation of the cumulative fatigue damage represents a tremendous computational cost with aero-servo-hydro-elastic solvers which is generally not affordable by industrial designers. To overcome this limitation, strong approximations with lumping of environmental discretized joint probability (scatter diagram) are generally employed. We present in this paper a new method, called MAKSUR, involving the iterative enrichment of a design of experiments tailored to provide a good approximation of the long term mean damage. This method relies on a Kriging response surface with a learning criterion defined as the variance of the mean damage integral. It is compared to another previous similar approach called AK-DA, also dedicated to damage prediction, but is shown to converge more efficiently and with less numerical parameters to define by the user. The potential of the method for offshore wind turbine is demonstrated by a realistic 6D floating wind turbine case study with six wind and wave input variables.

Time-variant reliability problems are frequently encountered in engineering due to factors like material degradation or random loading modeled as random processes. The PHI2 method, which employs the First Order Reliability Method (FORM), is commonly used to solve such problems. However, it requires repeated searches for Most Probable Points (MPPs), making it computationally expensive. To improve efficiency with little sacrifice of accuracy, this study proposes a First Order Time-variant Reliability Expansion (FOTRE) method, which provides an efficient explicit formulation for MPP regarding time, in contrast to the expensive optimization approach of the PHI2 method. It requires only a single accurate search for the so-called “worst MPP” over the whole lifespan and offers the “adaptive accuracy of outcrossing rate”, which avoids the repeated search for MPPs ensuring computational accuracy. The inspiration behind the FOTRE method stems from the observation that the outcrossing rate tends to be small at time points with relatively large reliability indexes compared to the minimum reliability index β_min, which has a negligible impact on the subsequent structural failure probability over the entire lifespan. This innovative approach significantly improves the efficiency of solving time-variant reliability problems without compromising much of the numerical accuracy. The effectiveness and accuracy of the FOTRE method are demonstrated through several numerical examples.

The idea of coupling economics and safety into one optimization exercise, and hence deriving target reliabilities explicitly from socio-economic consequences of limit state violation, appeared in the JCSS Probabilistic Model Code around the turn of the century; the effort culminated in the form of a Life Quality Index (LQI)-based marginal lifesaving costs (MLSC) methodology in ISO 2394:2015 for determining maximum acceptable failure probabilities, p_T, in life safety limit states for civil engineering structures across all nations. Unfortunately, while the methodology does yield adequate levels of safety when applied to structures in the developed countries, the recommended values of p_T turn out to be one to two orders of magnitude higher, and unacceptable to every known standard in the world including its own earlier edition (ISO 2394:1998) when applied to structures in the developing world (exemplified by India) with identical functions and expected fatalities. This arises from two shortcomings: (1) an MLSC approach is generally unable to provide an independent constraint on monetary optimization, and (2) the LQI-based measure of MLSC constraint is strongly dependent on a country’s per capita GDP (g) and the resultant p_T is effectively governed by the reciprocal of g. This paper recommends continuing with the absolute − not marginal − consequences of violating life safety limit states, and setting constraints on monetary optimization that are consistent with fatality risks from engineering activities more universally accepted to be safe.

Resilience has emerged as a crucial concept for evaluating structural performance under disasters because of its ability to extend beyond traditional risk assessments, accounting for a system’s ability to minimize disruptions and maintain functionality during recovery. To facilitate the holistic understanding of resilience performance in structural systems, a system-reliability-based disaster resilience analysis framework was developed. The framework describes resilience using three criteria: reliability (β), redundancy (π), and recoverability (γ), and the system’s internal resilience is evaluated by inspecting the characteristics of reliability and redundancy for different possible progressive failure modes. However, the practical application of this framework has been limited to complex structures with numerous sub-components, as it becomes intractable to evaluate the performances for all possible initial disruption scenarios. To bridge the gap between the theory and practical use, especially for evaluating reliability and redundancy, this study centers on the idea that the computational burden can be substantially alleviated by focusing on initial disruption scenarios that are practically significant. To achieve this research goal, we propose three methods to efficiently eliminate insignificant scenarios: the sequential search method, the n-ball sampling method, and the surrogate model-based adaptive sampling algorithm. Three numerical examples, including buildings and a bridge, are introduced to prove the applicability and efficiency of the proposed approaches. The findings of this study are expected to offer practical solutions to the challenges of assessing resilience performance in complex structural systems.

Transmission towers and overhead transmission lines are designed and constructed by considering the combined ice load and wind-on-ice load if the ice accretion hazard is not negligible. The structural design codes provide clauses with a range of values to evaluate such a combined load. However, it is unclear which of the values suggested in the codes one should use for specified regions, and the reliability-based calibration of such a combination is unavailable. To fill this gap, in the present study, we carried out the reliability-based calibration of the companion load combination factors by using statistics of the ice accretion thickness and concurrent wind speed available from more than 250 meteorological stations in Canada. For the calibration, a nonlinear combination problem needs to be considered since the wind-on-ice load depends on the accreted ice thickness, making this calibration task differ from those commonly reported in the literature, which is focused on the linear load combination problem. A parametric investigation was also carried out to assess the effect of using different return periods and the correlation between ice accretion and concurrent wind speed on the companion load combination factors. The calibration results were used to recommend the load combination format, the companion load combination factors, and the ratio of the square equivalent concurrent wind speed to the return period value of the annual maximum wind speed, which is commonly implemented in design codes.

This paper presents an environment-driven tropical cyclone (TC) model for the Western North Pacific basin, which comprises a revised Poisson regression genesis model, a tailored beta-advection track model, and a fast intensity model. The TC model reproduces the temporal and spatial distributions of genesis events, the motion pattern of tracks, as well as the intensity evolutions along tracks. Risk analyses for Hong Kong and along the southeast coastline of mainland China demonstrate that this model can simulate extreme TC events with high fidelity. And the Gaussian mixture model outperforms the Frank Copula in approximating the joint distributions of the annual maximum wind speeds and the corresponding wind directions. This model is driven by a set of environmental variables including relative vorticity, relative humidity, sea surface temperature, vertical wind shear, potential intensity, sub mixed layer depth stratification, mixture layer depth and so on. This enables the model to not only reproduce historical records, but also make predictions for future TC behaviors under climate change with combination of global climate models. Besides, the computational efficiency of the TC model is comparable to traditional purely statistical models. The proposed model can also be coupled with other natural hazard models to conduct multi-hazard analysis.

Riverine floods have become increasingly prevalent on a global scale, posing significant risks to infrastructure systems and communities. The escalating impacts of climate change associated with the increase in rainfall intensities and frequencies necessitate the improvement of the existing design methodologies to account for the non-stationary climate change effects to ensure that the reliability is above the target level and mitigate future flood disasters. This paper presents a novel LRFD approach for river embankments subjected to extreme rainfall under non-stationary climate change effects. This approach introduces an additional partial factor to account for the effects of climate change. Precipitation and temperature projections are collected from various climate models considering several cases of emission scenarios. An integrated hydrological and hydraulic modeling of the analyzed river is carried out to estimate the associated time-variant river discharge and water surface elevation. The non-stationary extreme value associated with the maximum flood level is leveraged using the peak-over-threshold approach. The embankment reliability and the corresponding most probable points are evaluated using limit states associated with overtopping and slope failures. Based on the estimated and target reliability indexes, the design point for each random variable is assessed considering the cases with and without climate change effects. Finally, the partial factors associated with climate change effects are determined. As an illustrative example, the proposed framework is applied to the Ashida River in Fukuyama city of Japan.