Structural Safety最新文献

Probabilistic life-cycle consequence (LCCon) analysis (e.g., assessment of repair costs, downtime, or casualties over an asset’s service life) can enable optimal life-cycle management of critical assets under uncertainties. This can lead to effective risk-informed decision-making for future disaster management (i.e., risk mitigation and/or resilience-enhancing strategies/policies) implementation. Nevertheless, despite recent advances in understanding, modeling, and quantifying multiple-hazard (or multi-hazard) interactions, most available LCCon analytical formulations fail to accurately compute the exacerbated consequences which may stem from incomplete or absent repair actions between different interacting hazard events. This paper introduces a discrete-time, discrete-state Markovian framework for efficient multi-hazard LCCon analysis of deteriorating engineering systems (e.g., buildings, infrastructure components) that appropriately accounts for complex interactions between natural hazard events and their effects on a system’s performance. The Markovian assumption is used to model the probability of a system being in any performance level (i.e., limit state) after multiple hazard events inducing either instantaneous and/or gradual deterioration and after potential repair actions through implementing stochastic (transition) matrices. LCCon estimates are then obtained by combining limit state probabilties with suitable system-level consequence models in a computationally efficient manner. The proposed framework is illustrated for two case studies subject to earthquake and flood events as well as environment-induced corrosion during their service life. The first is a reinforced concrete building and the second is a simple transportation road network with a reinforced concrete bridge.

This paper proposes a reliability analysis framework that accounts for the error caused by characterizing a data set as a probabilistic model. To this end we model the uncertain parameters as a probability box (p-box) of Sliced-Normal (SN) distributions. This class of distributions enables the analyst to characterize complex parameter dependencies with minimal modeling effort. The p-box, which spans the maximum likelihood and the moment-bounded maximum entropy estimates, yields a range of failure probability values. This range shrinks as the amount of data available increases. In addition, we leverage the semi-algebraic nature of the SNs to identify the most likely points of failure (MLPs). Such points allow the efficient estimation of failure probabilities using importance sampling. When the limit state functions are also semi-algebraic, semidefinite programming is used to guarantee that the computed MLPs are correct and complete, therefore ensuring that the resulting reliability analysis is accurate. This framework is applied to the reliability analysis of a truss structure subject to deflection and weight requirements.

Earthquakes have a long history of causing catastrophic damage to communities, resulting in structural collapses, loss of life, and economic turmoil. To enable informed decision-making and reduce the impact of these events in earthquake-prone regions, seismic risk studies provide relevant information to support stakeholders in the implementation of effective risk-based policies. The present work addresses the seismic reliability of the pre-code masonry building stock in the Metropolitan Area of Lisbon, which is the region of Portugal that faces the highest seismic risk due to the coexistence of moderate to high seismic hazard and highest demographic-economic exposure. The adopted general framework combines several hazard studies developed for the region under investigation and a synthetic database of masonry buildings representative of the pre-code building stock in Lisbon. Through analytical–numerical probabilistic approaches, new second-order hazard solutions with structural dependency are derived for the mean annual frequency of limit-state exceedance, which can be integrated into national application documents for Eurocode 8. In light of these results, the reliability assessment of the building stock is conducted in several Local Administrative Units by means of an improved SAC/FEMA formulation. The study represents the first comprehensive investigation of its kind in this region, providing essential information to define appropriate target safety level for code calibration and support future risk studies.

The road network consists of bridges of various lengths and configurations, all of which require accurate prediction of traffic load within their lifetime. However, current prediction methods are limited to modeling and predicting traffic load for a handful of individual bridges only; no method can simultaneously model and predict the traffic load of all bridges within an entire road network. Further, conventional models neglect the information that exists in the traffic load effect data established for different bridges, leading to large estimation uncertainties for each bridge and load effect examined. This study proposes a hierarchical Bayesian model that can estimate the traffic load effect of multiple bridges simultaneously, and subsequently create predictions for the remaining (unexamined) bridges within the road network. The proposed model is demonstrated using the traffic load data and influence lines used in the background study for the Eurocode 1 Load Model 1. The results show significant reductions in prediction uncertainties, better fits as measured by leave-one-out statistics, more robust fits against extremes, and the emergence of intuitive correlation structures between different bridges’ traffic loads that are absent in conventional models. This paper also presents a potential new strategy to reduce estimation uncertainty, and a method to predict parameters and return levels for bridges across an entire network made possible by the proposed hierarchical Bayesian model.

The probabilistic design of offshore wind turbines aims to ensure structural safety in a cost-effective way. This involves conducting structural reliability assessments for different design options and considering different structural responses. There are several structural reliability methods, and this paper will apply and compare different approaches in some simplified case studies. In particular, the well known environmental contour method will be compared to a more novel approach based on sequential sampling and Gaussian processes regression for an ultimate limit state case study on the maximum flapwise blade root bending moment. For one of the case studies, results will also be compared to results from a brute force simulation approach. Interestingly, the comparison is very different from the two case studies. In one of the cases the environmental contours method agrees well with the sequential sampling method but in the other, results vary considerably. Probably, this can be explained by the violation of some of the assumptions associated with the environmental contour approach, i.e. that the short-term variability of the response is large compared to the long-term variability of the environmental conditions. Results from this simple comparison study suggests that the sequential sampling method can be a robust and computationally effective approach for structural reliability assessment.

A novel sampling approach, called adaptive importance sampling (AIS), is proposed to efficiently perform time-variant reliability analysis. In practice, structures are generally subject to time-variant deterioration processes and external loads, and the Time-variant Failure Probability Function (TFPF), which is the failure probability as a function of time, is a critical quantity of interest in engineering applications. The proposed approach leverages an adaptive strategy and an optimal combination algorithm to further improve the accuracy and efficiency of TFPF estimation using the importance sampling approach. The adaptive strategy is to seek for the best setting of importance sampling components to iteratively obtain estimator components of the TFPF. The optimal combination algorithm is to collect all these adaptive estimator components to form an overall estimator by its coefficient of variation (C.o.V.). The proposed approach outperforms traditional importance sampling methods in the sense that it ensures the convergence with minimal computational cost, specifically the C.o.V. of the TFPF estimator is below a predetermined threshold over the entire time domain. Therefore, the proposed approach offers an extension to traditional importance sampling methods for time-variant reliability assessment. Numerical examples are provided to demonstrate the effectiveness of the proposed approach in accurately estimating the TFPF of structures subjected to time-variant loads and deterioration processes.

Traditional reliability analysis aims to compute the failure probability based on probability distribution functions, which are constructed using the moments of random parameters. In practice, however, appropriate samples may be insufficient to obtain deterministic values of the moments of all random variables and the exact value of failure probability cannot be obtained. To be consistent with the reality, the uncertainties in moments can be measured as interval variables, and then the bounds of failure probability should be evaluated. In this study, an idealized case is considered, where there is at most one imprecise moment associated with any given input random variable. A third moment method is proposed with uncertain moments measured as interval variables, and is named as TMI method. The proposed TMI method is straightforward including only four steps. Firstly, the derivative of performance function to random variables having uncertain moments is calculated, with the random variables set to be their mean values. Secondly, the values of uncertain moments for computing the bounds of failure probability are determined. Then, with inverse normal transformation defined based on the moments, the performance function at the bounds in Gaussian space is directly constructed. Finally, bounds of failure probability can be evaluated by two times of classical reliability analysis corresponding to the constructed performance functions. The application of TMI method is validated by numerical examples, including high-dimensional and strong nonlinear problems.

Characterization of the response of systems with governing parameters that exhibit both uncertainties and spatial dependencies can become quite challenging. In these cases, the accuracy of conventional probabilistic methods to quantify the uncertainty may be strongly affected by the availability of data. In such a scenario, fuzzy fields become an efficient tool for solving problems that exhibit uncertainty with a spatial component. Nevertheless, the propagation of the uncertainty associated with input parameters characterized as fuzzy fields towards the output response of a model can be quite demanding from a numerical point of view. Therefore, this paper proposes an efficient numerical strategy for forward uncertainty quantification under fuzzy fields. This strategy is geared towards the analysis of steady-state, linear systems. To reduce the numerical cost associated with uncertainty propagation, full system analyses are replaced by a reduced-order model. This reduced-order model projects the equilibrium equations into a small-dimensional space constructed from a single analysis of the system plus sensitivity analysis. The associated basis is enriched to ensure the quality of the approximate response and numerical cost reduction. Case studies of heat transfer and seepage analysis show that with the presented strategy, it is possible to accurately estimate the fuzzy responses with reduced numerical effort.

Past Italian earthquakes revealed the high seismic vulnerability of steel pallet racks designed for gravity loads only, which are still the most widespread industrial storage system. This study aims to derive the seismic fragility of these structures to enable more refined estimates of enterprise risk and the definition of effective retrofit solutions. For this purpose, 3D non-linear models of 27 unbraced pallet racks, representative of the Italian context, were analysed in Time-History under 268 bidirectional events, representative of Italian seismicity. Multiple fragility models were then derived, based on various engineering demand parameters and seismic intensity measures, through a cloud approach.

Long-term exposure to coastal and marine environments accelerates the aging of reinforced concrete (RC) structures, impacting their structural safety and society impact. Traditional assessments of long-term performance deterioration in RC structures involve complex, nonlinear, and time-intensive studies of physical mechanisms. While existing machine learning (ML) methods can assess the lifetime of these structures, they often prioritize data regression over mechanistic interpretation. To enhance the efficiency and interpretability of predicting the life-cycle performance of RC structures, this study introduces a generic framework based on interpretable ensemble learning (EL) methods. The framework predicts life-cycle performance efficiently and accurately, with optimal hyperparameters automatically tuned through Bayesian optimization. Interpretability algorithms clarify the influence of environmental, durability, and mechanical parameters on structural durability and mechanical predictions. Validation employs real-world cases of RC hollow beams in the coastal area of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). The comprehensive model for RC structures integrates actual data on temperature, humidity, and surface chloride content in the GBA, considering diffusion, convection, and binding effects of chloride ions, corrosion non-uniformity, and crack impact on durability estimation. Comparative analysis with existing ML methods underscores the effectiveness of the framework. The findings highlight the dynamic evolution of feature importance rankings throughout the service life, shedding light on the continuous changes in the significance of different factors when predicting mechanical resistance.