Structural Safety最新文献

System-Reliability-Based Design Optimization (S-RBDO) of structures considering progressive collapse is a complex problem, as the number of potential failure sequences increases geometrically with the degree of static indeterminacy of the structure. Existing methods for identifying failure sequences in structural systems are computationally expensive and prone to missing some critical failure sequences, especially within an optimization framework. In this context, identifying the most critical failure sequences to simplify the problem is fundamental. Herein, we propose a novel system-reliability-based framework for sizing and shape optimization of trusses. The procedure identifies minimal cut sets using the recently developed null space method, which has been proven more efficient than traditional failure path-based methods. The most probable failure sequence is selected from each identified minimal cut set. System reliability is estimated using the dominant failure sequences for the whole structure, selected based on their correlations using the Probabilistic Network Evaluation Technique (PNET). Craziness-Based Particle Swarm Optimization (CRPSO) is employed as the optimization algorithm. Numerical examples involving hundreds to millions of failure sequences demonstrate applicability and efficiency of the proposed framework on truss optimization problems with different material post-failure behaviors. Results suggest that, in a system-reliability analysis considering progressive collapse, the most critical failure sequences are those obtained from minimal cut sets. Furthermore, results show that the procedure proposed herein can outperform other frameworks based on traditional failure path-based methods. Simple truss sizing and shape optimization is considered herein, but the conclusions have immediate relevance to the optimal design of realistic structures considering progressive collapse.

In recent years, performance-based design (PBD) has gained attention and is sought to be the benchmark approach in the field of wind engineering. While the concept of performance-based design is well-accepted in earthquake engineering, it is yet to be embraced for the design of buildings to resist severe wind loads. This paper introduces a framework for the performance-based wind design (PBWD) of tall steel buildings using a time domain analysis that keeps the process of wind effects and the structural design process integrated, transparent, and fully auditable. From the perspective of PBWD, the main objective is to achieve a desirable performance level for a given hazard level, i.e., mean recurrence interval of extreme wind. The wind effects are directly related to the mean annual return of wind through a well-accepted simulation approach. A 180 m tall standard CAARC building is used for the case study to illustrate the proposed methodology. The wind load time histories are determined using the pressure tap data on exterior faces of the building measured in the wind tunnel. With the calculated wind loads, nonlinear dynamic analysis is conducted with various wind directions and mean wind speeds based on database-assisted design (DAD) approach. The key performance measures such as demand-to-capacity indices, inter-story drift, damage deformation index, and floor accelerations are calculated as a function of wind directions and mean wind speed. The obtained responses are used in conjunction with a local wind climatological database to determine the extreme wind effects for any specific mean recurrence interval. The performance of the steel building is evaluated for three performance criteria, including occupant comfort, operational, and continuous occupancy. The conducted performance assessment reveals that building fails to satisfy the serviceability requirement of drifts. However, the building satisfies the requirements for the occupant comfort and operational performance levels for strength design, while it also satisfies the continuous occupancy, limited interruption in Risk category II. The result reveals that the proposed framework provides realistic assessment of performance of the building incorporating the wind directionality and return period of the wind speeds.

This paper proposes a multitask learning framework for probabilistic model updating by jointly using strain and acceleration measurements. This framework can enhance the structural damage assessment and response prediction of existing steel frame structures with quantified uncertainty. Multitask learning may be used to address multiple similar inference tasks simultaneously to achieve a more robust prediction performance by transferring useful knowledge from one task to another, even in situations of data scarcity. In the proposed model-updating procedure, a spatial frame is decomposed into multiple planar frames that are viewed as multiple tasks and jointly analyzed based on the hierarchical Bayesian model, leading to robust estimation results. The procedure uses a displacement–stress relationship in the modal space because it directly reflects the elemental stiffness and requires no prior knowledge concerning the mass, unlike most existing model-updating techniques. Validation of the proposed framework by using a full-scale vibration test on a one-story, one-bay by one-bay moment resisting steel frame, wherein structural damage to the column bases is simulated by loosening the anchor bolts, is presented. The experimental results suggest that the displacement–stress relationship has sufficient sensitivity toward localized damage, and the Bayesian multitask learning approach may result in the efficient use of measurements such that the uncertainty involved in model parameter estimation is reduced. The proposed framework facilitates more robust and informative model updating.

This paper proposes a probabilistic framework to calibrate burst strength models of intact and corroded pipelines based on the hierarchical Bayesian method. The approach uses burst test data of intact and corroded pipelines of different steel grades compiled from the literature and accounts for the variations among the data sources. First, the most appropriate burst strength models for corrosion-free and corroded pipelines are adopted. The burst pressure prediction models are categorised under low, medium and high-grade steel classes. Using the hierarchical Bayesian approach model uncertainty factors are derived to calibrate the burst strength models. The mean values and uncertainty of posterior probabilities of the model uncertainty factors are estimated for intact and corroded pipelines in three material categories. This study further investigates the uncertainty propagated by calibrated and non-calibrated models and draws important observations regarding the uncertainty associated with the calibration. The prediction uncertainties follow a non-linear increasing trend as corrosion defect increases. This study's importance is demonstrated with a case study that shows the differences in the uncertainty resulting from the use of the proposed approach compared to the conventional method. Additionally, for corroded pipes, model uncertainty factors are described as a function of defect depth with regression parameters estimated from hierarchical Bayesian-based regression analysis. Finally, a comparison between calibrated and non-calibrated models indicates that the calibrated models provide non-conservative predictions.

Estimation of second-order statistics allows characterizing the uncertainty associated with the response of stochastic finite element models. Two common approaches for estimating these statistics are Monte Carlo simulation and perturbation. The purpose of this paper is to present a framework to aggregate the results obtained by means of these two approaches under the umbrella of Control Variates with Splitting. This allows to produce estimates of the second-order statistics of the system’s response with improved precision and accuracy. More specifically, Control Variates is implemented in such a way that the variance of the estimates of second-order statistics is minimized. In addition, the application of intervening variables for enhancing perturbation is considered as well, showing substantial advantages by increasing the accuracy of the estimates of second-order statistics. The application of the proposed framework is illustrated by means of an example involving the estimation of second-order statistics of a model involving confined seepage flow.

The probability measure of low-quality concrete is essential for the reliability analysis of concrete structures. However, this problem is usually neglected, and the normal distribution or lognormal distribution is often selected as the probability distribution of concrete strength. In this study, a better solution for this problem is given by theoretically deriving of the Burr distribution based on the stochastic damage model. A large amount of in-situ test data in engineering structures is applied to perform a K-S test of different distribution types and to fit the distribution parameters. As a result, the advantage of Burr distribution in representing the tail probability is explained by both theoretical derivation and fitting results. And the Burr distribution is accepted by the K-S test in every strength grade while the other distribution types are all partly rejected.

Often the tropical cyclone (TC) wind hazard assessment requires the use of the TC wind field model. While theoretical models typically predict the observed wind field well, there can be a spatially varying residual correlation that impacts the damage assessment of communities or groups of structures. In this study, we focus on developing spatial correlation models and assessing the intraevent and interevent variability of the TC wind field using the H*Wind dataset and two widely used wind field models - the vertically averaged boundary layer slab model and the gradient wind field-based model. Our models and the statistics of the interevent and intraevent variability are integrated into a framework for evaluating the wind-induced damage of a portfolio of structures. The framework includes simulating TC tracks and wind fields, considering interevent and intraevent variabilities, and assessing peak linear elastic and nonlinear responses. Numerical examples illustrating the use of this framework are provided, indicating that realistic spatial correlation of the TC wind field needs to be considered to assess the correlation coefficient of the damage factor of a pair of spatially distributed structures and the probability distribution of the damage cost of a portfolio of structures.

This paper delves into cumulative damage on aluminium cladding panels attributed to hailstorms throughout the lifespan of the installations. 40 gas gun tests subjecting the cladding panel to repeated impact were undertaken for the purpose of studying cumulative damage behaviour. Insights from these tests were integrated into a hail size distribution model to characterise the probabilistic distribution of permanent indentation resulted from multiple hailstorm events. A life-cycle analysis framework was subsequently introduced, incorporating the natural variability of hailstone sizes and dynamic response of claddings to repeated ice impact. Intervention criterion can be established based on knowledge of the accumulation of permanent indentation into the cladding panels. Proactive actions are recommended should the indentations become visible to prevent worsening damage. Randomness of hailstorm occurrences was considered using hazard function which can be inferred from historical observations. Practical application of the proposed model is illustrated through case studies of two Australian states, coupled with comparative analyses highlighting key factors influencing cladding performance. The ability to account for stochasticity distinguishes the presented framework from existing deterministic approaches.