Structural Safety最新文献

In this work, the Theory of Plastic Mechanism Control (TPMC) is combined with a probabilistic method to account for the influence of random material variability. Reference is made to steel Moment Resisting Frames (MRFs) equipped with FREEDAM connections. FREEDAM connections are beam-to-column connections equipped with friction dampers to dissipate the seismic input energy. TPMC is used to guarantee that in case of destructive seismic events the structural members such as beams and columns remain undamaged. To this scope, the structure is designed to assure a collapse mechanism characterized by the activation of all the friction dampers of the beam ends and the formation of plastic hinges at the base of the first storey columns only. From the probabilistic point of view, the random uncertainties are given by the static friction coefficient of the contact surfaces and the preloading of the bolts of the friction dampers as well as the yielding resistance of the steel members. The failure domain is related to all the possible failure events, where the term “failure” concerns the development of an undesired mechanism different from the global one. Generally, the design conditions to prevent undesired collapse mechanisms are stochastic events within the framework of the kinematic theorem of plastic collapse. The limit state function corresponding to each event can be represented by a hyperplane in the space of random variables. Consequently, the failure domain is a surface resulting from the intersection of the hyperplanes corresponding to the limit states of each single failure event. Since dissipative zones (member ends or friction dampers) in the frame members are common to many different mechanisms, the single limit state functions are correlated. Therefore, the probability of failure can be evaluated by means of the Bimodal or Ditlevsen bounds by assuming that the failure events are located in series. The output of the work is a simple relationship which provides the overstrength factor of FREEDAM connections to be considered in the column design phase to account for random material variability thus assuring a given level of reliability in the application of TPMC.

This paper contains decision analytical approaches, conditions and models for the quantification of decision values for built environment systems. Specifically, (1) delta formulations of objective functions for decision value quantification are introduced, (2) conditions for decision value are identified and (3) action value analysis formulations are further developed. The delta objective functions are formulated with differences in utility, cost, and probabilities for consistent decision identification by expected utility and value. The delta formulations facilitate the direct calculation of action and information values and the explication of conditions for a positive decision value. Action value objective functions are derived in delta formulation for the action types of utility and system state actions and with action implementation states and action uncertainty models. The delta formulations are exemplified for predicted information and predicted actions values. The paper closes with a synthesis and discussion of decision values and with findings encompassing a computational effort reduction and the identification of predicted information value mechanisms.

Kriging has gained significant attention for reliability analysis primarily because of the analytical form of its uncertainty information, which facilitates adaptive training and establishing stopping criteria for the training process. Learning functions play a significant role in both selection of training points and stoppage of the training. For these functions, most existing learning functions evaluate candidate training points individually. However, lack of consideration for the global effects can lead to suboptimal training. In addition, the subjectivity of these stopping criteria may result in over or undertraining of surrogate models. To overcome these gaps, we propose Global Error-based Learning Function (GELF) for optimal refinement of Kriging surrogate models for the specific purpose of reliability analysis. Instead of prioritizing training points based on their uncertainty and proximity to the limit state like the existing learning functions, GELF for the first time directly and analytically associates the maximum error in the failure probability estimate to the global effect of choosing a candidate training point. This development subsequently facilitates an adaptive training scheme that minimizes the error in adaptive reliability estimation to the highest degree. For this purpose, GELF uses hypothetical future uncertainty information by treating the current construction of the surrogate model as a generative model. The proposed method is tested on three classic benchmark problems and one practical engineering problem. Results indicate that the proposed method has significantly better computational efficiency than the state-of-the-art methods while achieving high accuracy in all the numerical examples.

Assessing the seismic capacity of pier columns is a crucial element in the performance-based seismic design of bridges. Such assessment necessitates a probabilistic approach to accurately determine the marginal probability distributions of seismic capacities and to characterize the dependencies among these variables. In response to this need, this paper employs Multi-Output Gaussian Process Regression (MO-GPR), a probabilistic machine learning method, to jointly model the multiple seismic capacities of pier columns. We initially introduce a probabilistic seismic capacity model that utilizes MO-GPR for pier columns and validate its predictive accuracy in comparison to Bayesian linear regression and existing empirical methods. Subsequently, the methodology is augmented by the integration of hierarchical modeling within the MO-GPR framework, resulting in a Multi-Output Hierarchical Gaussian Process Regression (MO-HGPR) model that effectively estimates intraclass correlation coefficients for specific types of datasets. It is postulated that these correlation coefficients also reflect correlations associated with multiple components of the real structure. This study employs MO-HGPR and MO-GPR separately to investigate the potential correlations of seismic capacities among pier columns and distinct limit states. The results demonstrate that the MO-GPR model exhibits superior prediction accuracy and more effectively portrays uncertainty compared to existing empirical models. Moreover, the correlations of seismic capacities among piers and limit states are both robust and significantly impact the seismic fragility of bridges. This finding highlights the essential nature of considering capacities correlations in seismic fragility or risk assessment processes.

Eurocode 3 provides Geometrically and Materially Nonlinear Analysis with Imperfections-method (GMNIA) in which the entire structure can be designed in system-level. In GMNIA, reliability of structural system is verified by using a system safety factor which is obtained in Eurocode 3 based on numerical or experimental capacity test results. Unfortunately, such test results are scarcely published in the literature thus complicating the determination of the factor for a design engineer. In the so-called Direct Design Method, however, system safety factors are provided in advance for the design engineer by system-level reliability studies. This study determines the Eurocode-compliant GMNIA system safety factor for Warren truss portal frames by using the approach of the Direct Design Method. Advanced numerical models are utilized to perform Monte Carlo-simulations for entire structural systems. These simulations provide statistical distributions of system resistances which are employed in the First-Order Reliability Method to derive the system safety factors. Studied structures are made of S700 cold-formed hollow sections. Various system geometries, system configurations and load combinations consisting of snow and wind loads are investigated and a suitable system safety factor is proposed for the ultimate limit state design of Warren truss portal frames. The proposed system safety factor is applied in a practical comparison in which Warren system is designed both by the conventional Eurocode 3 method and GMNIA. This comparison reveals that GMNIA has a remarkable potential to offer reduced material consumption in design compared to the conventional method.

Polynomial dimensional decomposition (PDD) is a surrogate method originated from the ANOVA (analysis of variance) decomposition, and has shown powerful performance in uncertainty quantification (UQ) accuracy and convergence recently. However, complex high-dimensional problems result in a large number of polynomial basis functions, leading to heavy computational burden, and the probability distributions of the input random variables are indispensable for PDD modeling and UQ, which may be unavailable in practical engineering. This study establishes an adaptive data-driven subspace PDD (ADDSPDD) for high-dimensional UQ, which employs two types of data for modeling the PDD basis function and the low-dimensional subspace directly, namely, the data of input random variables and the input-response samples. Firstly, we propose a data-driven zero-entropy criterion-based maximum entropy method for reconstructing the probability density functions (PDF) of input variables. Then, with the aid of the established PDFs, a data-driven subspace PDD (DDSPDD) is proposed based on the whitening transformation. To recover the subspace of the function of interest accurately and efficiently, we put forward an approximate active subspace method (AAS) based on the Taylor expansion under some mild premises. Finally, we integrate an adaptive learning algorithm into the DDSPDD framework based on the sparse Bayesian learning theory, obtaining our ADDSPDD; thus, the real subspace and the significant PDD basis functions can be identified with limited computational budget. We validate the proposed method by using four examples, and systematically compare four existing dimension-reduction methods with the AAS. Results show that the proposed framework is effective and the AAS is a good choice when the corresponding assumptions are satisfied.