Structural Safety最新文献

Hazard-induced service interruption of interdependent infrastructure systems (IISs) (e.g., electricity, water, gas, etc.) can lead to significant disruptions of social and economic functions of a modern society. An effective post-event restoration of the IISs is therefore of paramount importance to the overall recovery of a hazard-stricken community as a whole. As opposed to approaches with pure engineering perspectives, this study proposes an IISs restoration planning methodology aimed at balancing tradeoffs between the loss of social services (e.g., health care, food supply, etc.) and that of economic productions (e.g., construction, manufacturing, trade, etc.) throughout the IISs restoration process. The methodology is distinguished from previous researches with the following contributions: i) quantitatively relates the losses of various social services and economic productions to the service disruptions of IISs through the functionality loss of buildings; ii) the IISs disruption-induced overall losses of social services and economic productions accumulated throughout the whole recovery process is set as the bi-objective in formulating IISs restoration plans, and the Pareto optimal solutions are given to satisfy different decision preferences; iii) physics-based models capturing operational mechanisms of the IISs are embedded to provide realistic estimations of commodity supplies at each time step of the restoration optimization. The optimization is coupled with Monte Carlo simulation to uncover the impact of decision preference on community recovery from a statistical point of view. Testbed illustration shows that the decision preference makes significant impact on the recovery of the community as a whole and of different areas in the community with different socioeconomic characteristics.

A broad review of the existing literature concerning Human and Organizational Factors (HOFs) and human errors influencing structural safety is presented in this study. Publications on this research topic were collected from the Scopus database. Two research focal points of this topic, namely modelling and evaluating the human error effects on structural reliability, and identifying causal factors for structural defects and failures, have been recognized and discussed with an in-depth literature review. The review of studies with a model focus summarizes the models and methods that have been developed to evaluate structural reliability considering human error effects. Besides, the review of publications on the factor subject outlines the most acknowledged HOFs that influence structural safety. Moreover, an additional spotlight was given to the studies from the offshore industry for the advanced development in HOFs and contributing the first complete Human Reliability Analysis (HRA) method for structural reliability analysis. In conclusion, this study provides a holistic overview of the knowledge developed in existing research on the topic of HOFs and human error influencing structural safety. Furthermore, current developments and challenges are reflected, and future research directions are explored for academics entering and working in this field. Additionally, the insights into HOFs generated from this review can assist engineers with better hazard identification and quality assurance in practice.

Both maximum and residual deformations are essential for seismic safety during and following strong earthquakes, especially in the near field. These parameters are often calculated per unidirectional analysis, albeit bidirectional analysis may be carried out for important systems with the availability of growing computational power. However, a pressing challenge in earthquake engineering that continues to exist in the face of several uncertainties is to represent these response statistics – essentially with wide dispersion - in an expressive and effective format. The present paper aims to represent the maximum and residual deformation to unidirectional and bidirectional shaking in a rational format, even when uncertainties in ground motion and structural characteristics are prevalent. To this end, a bridge pier with uncertain material parameters is subjected to a wide range of stochastically simulated near-field motions with forward-directive signature, and the responses are computed to unidirectional and bidirectional shaking. In an attempt to develop predictive models, the responses are regressed first in terms of primary parameters characterizing structural material and ground motions. Next, by standard principles of mechanics, the response is recast in a dimensionless and orientationally consistent format in terms of derived parameters. This reduces the number of independent variables yet connotes a sound basis of mechanics. The predictive model in terms of derived dimensionless parameters is further extended in the Bayesian framework to improve the predictive model in a probabilistic sense.

The unavoidable heterogeneity in the mechanical characteristics of concrete is widely acknowledged. Although it is widely considered as either perfectly correlated or entirely independent random variables in engineering practice; however, such treatment is illogical, and the outcomes may be deceptive. In high-rise buildings comprised of multiple structural components, it is crucial to consider the material properties’ spatial variability (MPSV) to obtain a reliable structural response and avoid damage to these structures. To this end, three main components, including column, rectangular shear wall, and U-shaped shear wall, are considered herein to investigate their stochastic response. The MPSV is represented by a covariance matrix decomposition-based random field generator combined with a GF-discrepancy-based point selection strategy to generate samples efficiently. A simplified strategy is developed to represent the random field for the U-shaped wall. Moreover, the probability density evolution method combined with the extreme value event is employed to obtain the failure probability of the studied components, where failure probabilities of 18%, 23%, and 32% are recorded for the studied RC column, rectangular shear wall, and U-shaped shear wall, respectively. Furthermore, different failure modes were identified and could not be determined through the deterministic analysis, highlighting the importance of accounting for material uncertainty. The proposed framework proved that the stochastic response and non-linear behavior of the considered components could be well captured and provide full perspective about the uncertainty quantification and reliability assessment and can be further implemented to capture the stochastic response and safety assessment of high-rise buildings.

For stochastic dynamical systems with multiple uncertain parameters, it is often of interest to detect which parameters are dominant, in which the global sensitivity analysis may be one of the common means. To measure the global sensitivity in both qualitative and quantitative terms, it is of significant importance to adopt a global sensitivity index with sufficient quantification information. The Fréchet-derivative-based global sensitivity index (Fre-GSI) proposed by Chen et al. (2020) is appropriate to this goal. The present paper aims to provide new aspects of the Fre-GSI, including: (1) The numerical solution of the Fre-GSI given by Chen et al. (2020) is investigated in both analytical and numerical aspects; (2) A novel global sensitivity evolution equation is derived from the generalized density evolution equation, thus the Fre-GSI can be estimated by directly solving the global sensitivity evolution equation, rather than repeatedly solving the generalized density evolution equation as suggested in Chen et al. (2020). Numerical examples are studied to illustrate the efficiency and accuracy of the proposed approach. Some problems to be further studied are also outlined.

The appropriate assignment of gravity live loads is of fundamental importance in establishing a design base for safe and economical structures. This paper presents a critical review of the probabilistic modelling of live loads in buildings, providing a historical overview of the theoretical models found in the literature, as well as the load surveys that provided the empirical evidence for their development. As a general rule, these models divide the live load into sustained and extraordinary components, represented as a Poisson square wave and spike processes, respectively, with the differences lying in the underlying hypotheses and process parameters. A comparison of different models is presented, and it is shown that the model parameters currently in use in background documents on the reliability of the Eurocodes do not agree well with survey data, leading to an overestimation of extreme loads. While the Eurocodes lack clear specification regarding the exceedance probability for design loads, this study demonstrates, using a model with modified parameters, that the office design load corresponds to an approximate 27% exceedance probability over 50 years for a reference area of 20 m${}^2$. The impact of employing this model on the reliability-based calibration of the Eurocode partial safety factors for loads is also examined, and it is found that the average reliability falls somewhat below the prescribed 50-year target reliability index of 3.8.

Bayesian active learning methods have emerged for structural reliability analysis and shown more attractive features than existing active learning methods. However, it remains a challenge to actively learn the failure probability by fully exploiting its posterior statistics. In this study, a novel Bayesian active learning method termed ‘Parallel Bayesian Probabilistic Integration’ (PBPI) is proposed for structural reliability analysis, especially when involving small failure probabilities. A pseudo posterior variance of the failure probability is first heuristically proposed for providing a pragmatic uncertainty measure over the failure probability. The variance amplified importance sampling is modified in a sequential manner to allow the estimations of posterior mean and pseudo posterior variance with a large sample population. A learning function derived from the pseudo posterior variance and a stopping criterion associated with the pseudo posterior coefficient of variance of the failure probability are then presented to enable active learning. In addition, a new adaptive multi-point selection method is developed to identify multiple sample points at each iteration without the need to predefine the number, thereby allowing parallel computing. The effectiveness of the proposed PBPI method is verified by investigating four numerical examples, including a turbine blade structural model and a transmission tower structure. Results indicate that the proposed method is capable of estimating small failure probabilities with superior accuracy and efficiency over several other existing active learning reliability methods.

Buffeting of long-span bridges caused by the wind turbulence could result in problems of large deformation, fatigue, traffic safety and user comfort. The calculation of buffeting responses is greatly affected by multiple uncertainties, especially the randomness of flutter derivatives and structural damping. In buffeting analysis, these uncertainties are typically propagated using the brute-force Monte Carlo (MC) method, which requires enormous computational resources for a complicated structure involving multiple uncertainties. This study develops an efficient framework based on surrogate models to account for these uncertainties in buffeting responses and the assessment of structural fragility in a mixed climate. Two surrogate models, Kriging and polynomial chaos expansions (PCE), are applied in this framework. Comparison with the direct MC method shows that the Kriging model rather than the PCE model is the proper surrogate model, and the surrogate model contributes significantly to saving computing time from 17 h to 1 min for MC simulations. It is also observed that uncertainties propagated from structural parameters to responses will be more notable as the wind speed increase. Buffeting fragility curves of this bridge show that it’s easier for responses in acceleration to achieve and exceed thresholds, indicating that performance related to user comfort might not be satisfied. By introducing the probability distributions of non-typhoon and typhoon winds at the site of the bridge, it is found that considering single climate may underestimate structural risk. The framework based on surrogate models in this paper can be further generalized to additional PBWE frameworks addressing different wind and structural engineering issues.

It is well established that various sources of uncertainties play a critical role in the safety assessment of engineering structures. Some widely used frameworks, such as performance-based earthquake engineering (PBEE), explicitly consider the ground motion record-to-record randomness, while the material and modeling uncertainty remain to be primarily based on judgments or limited analysis. This paper presents the results of a comprehensive uncertainty quantification and sensitivity analysis of a reinforced concrete structural component. First, different modeling strategies are adopted to develop several parent models. Next, various sources of uncertainty are propagated through the parent models to generate thousands of children models. The children models are further combined with material uncertainty to produce grandchildren models, and nonlinear transient simulations are conducted using an innovative artificial acceleration at different seismic intensity levels. The results are post-processed using a range of probabilistic, statistical, and machine learning methods. The study finds that the modeling strategy and its associated variability can cause significant bias and dispersion in the drift response, while material uncertainty has a relatively minor effect. The study quantifies the importance of modeling uncertainty, which is often overlooked in engineering practice.

To address the challenges of reliability analysis in high-dimensional probability spaces, this paper proposes a new metamodeling method that couples active subspace, heteroscedastic Gaussian process, and active learning. The active subspace is leveraged to identify low-dimensional salient features of a high-dimensional computational model. A surrogate computational model is built in the low-dimensional feature space by a heteroscedastic Gaussian process. Active learning adaptively guides the surrogate model training toward the critical region that significantly contributes to the failure probability. A critical trait of the proposed method is that the three main ingredients–active subspace, heteroscedastic Gaussian process, and active learning–are coupled to adaptively optimize the feature space mapping in conjunction with the surrogate modeling. This coupling empowers the proposed method to accurately solve nontrivial high-dimensional reliability problems via low-dimensional surrogate modeling. Finally, numerical examples of a high-dimensional nonlinear function and structural engineering applications are investigated to verify the performance of the proposed method.