Search

Bridges are an important type of transportation infrastructures. Once more than one bridge in a transportation network is extensively or completely damaged in a severe earthquake, the traffic capacity of the network may degrade significantly, resulting in considerable social and economic consequences to the community served. The principles of build back better (BBB) require that the performance level of a critical infrastructure facility should be properly determined in the post-hazard recovery process to reduce the future risk. This paper proposes a continuous-time Markov decision process framework based on life-cycle cost (LCC) for determining the optimum earthquake-resistant levels of the rebuilt bridges. Compared to other optimisation methodologies, the proposed one owns two distinctive features: (1) network-level LCC analyses are performed in the context of BBB principles; (2) optimum decisions are made sequentially at random time points of earthquake occurrence, and the optimum earthquake-resistant level of a rebuilt bridge depends on the performance levels of other bridges in the network. A hypothetical transportation network is investigated to demonstrate the application of the proposed methodology. In particular, the sensitivity of the optimum policy on network topology is studied. [ABSTRACT FROM AUTHOR]

Summary: We propose a computationally efficient stochastic framework and an associated mathematical formulation to assess the improvement in the vibration response of wind turbines with active structural controllers. Uncertainties in the turbulent wind speed as well as in the structural parameters are considered in the formulation for analyzing the 2 important (rated and cut‐out) wind speed conditions. A multimodal wind turbine model based on a NREL 5‐MW wind turbine model is adopted for analysis, and an active tendon controller is used to develop the stochastic framework. Dynamic interaction between the rotor blades and the supporting tower, as well as the coupling induced by the pretwist of the blades, and a collective pitch controller are considered. A time‐evolving phase spectrum method together with a phase delay spectrum model is used to simulate the stochastic wind field, which is computationally less expensive. The probability density contours and the extreme value distributions of the blade tip displacements with and without the active controller are obtained by the wavelet‐based probability density evolution method, followed by the computation of the corresponding failure probabilities. It is observed that the improvement in reliability and performance of the rotor blades with the controller becomes statistically more effective as the wind speed increases (e.g., significant beneficial effects are noticed from the probability distributions in terms of the spread at the cut‐out wind speed). [ABSTRACT FROM AUTHOR]

The probability density evolution method (PDEM) provides a feasible approach for the dynamic response analysis of nonlinear stochastic structures. The key step in this regard is to solve a generalized density evolution equation (GDEE) in order to establish the probability density function (PDF). Previously, a finite difference method (FDM) has often been resorted to solve the GDEE. However, one may encounter the problem of mesh sensitivity in the application of FDM to the PDEM. To this end, a novel difference-wavelet method that can improve the finite difference result by means of a nonlinear wavelet density estimation method is proposed in the present paper. By exploiting the multi-resolution property of wavelet functions and by choosing the optimal scale at each instant, it is expected that the bothering mesh sensitivity issue in finite difference method can be overcome to some extent and a better probability density result can be obtained. In order to verify the proposed method, a single-degree-of-freedom (SDOF) oscillator and an 8-story frame structure are investigated in detail. The results show the notable superiority of the proposed method to finite difference method. [ABSTRACT FROM AUTHOR]

The key issue of the probability density evolution method (PDEM) is to solve a generalized density evolution equation (GDEE). Previously, the GDEE was solved in the framework of the point evolution method which is essentially a zero-order ensemble evolution method. In this paper, a first-order ensemble evolution method is proposed aiming at increasing the accuracy and robustness of the PDEM. The main idea of the proposed method is to incorporate information of standard deviation of each probability subdomain into the probability density evolution equation (PDEE) by introducing an ensemble velocity term. Compared with the point evolution method, the proposed method can truly reflect the fluctuation of a stochastic dynamic system. In order to estimate the ensemble velocity term accurately, a piecewise quadratic polynomial fitting method is also proposed. In addition, a GF-discrepancy based point selection method and a finite difference scheme that is total variation diminishing are adopted to solve the new PDEE. A single-degree-of-freedom oscillator, a Riccati equation and a 2-span 8-storey frame structure are investigated in detail to demonstrate the advantage of the proposed method over the original one. [ABSTRACT FROM AUTHOR]

• A hybrid Markov decision process model is derived; • A life-cycle maintenance decision framework for ageing bridges in seismic areas is developed; • A ternary state space that describes the damage state of a bridge is defined; • A gamma process is adopted to model stochastic corrosion processes; • The maintenance strategy, inspection interval, and performance level are optimized successively. Highway bridges are a class of important infrastructure components in transportation networks. Highway bridges will inevitably deteriorate over time due to various external impacts, such as traffic loads and corrosion. In seismic-prone areas, seismic hazard is another critical factor that threatens the normal operation of ageing highway bridges. To ensure adequate safety of ageing highway bridges under potential earthquake shocks, it is imperative to perform proper maintenances during their service life. This paper aims at optimizing the life-cycle maintenance strategies for highway bridges under the combinatorial threats of progressive deterioration and sudden earthquakes. Driven by the distinct characteristics of the two threats, we have proposed a novel hybrid Markov decision process model that integrates a discrete-time Markov decision process model for dealing with progressive deterioration and a continuous-time Markov decision process model for dealing with sudden earthquakes into a unified framework. The proposed model is further reduced into the form of a discrete-time Markov decision process model with an equivalent state transition matrix and an equivalent discount factor, which can be easily solved by dynamic programming. A ternary state space that collectively describes corrosion initiation, corrosion damage cumulation, and seismic damage cumulation is defined. A gamma process and a Poisson process are adopted to model bridge corrosion and earthquake occurrences, respectively. To demonstrate the application of the proposed framework, a numerical example is conducted, in which the optimizations with respect to the maintenance strategy, inspection interval, and design performance level of a hypothetical highway bridge are carried out successively. [ABSTRACT FROM AUTHOR]

In this work, a novel Fréchet-derivative-based global sensitivity analysis is incorporated into the probability density evolution method for the simultaneous reliability and sensitivity analysis of structures. The two methods are synthesized and realized under a two-stage uncertainty quantification framework. Fundamental and numerical algorithms of the method are described, which is validated by a benchmark problem with theoretical solutions. Then it is applied on studying stochastic seismic responses of a real-world reinforced concrete structure of 288 m high. Results demonstrate that the response characteristics of the structure will be greatly changed when both uncertainties from the ground motions and structural parameters are considered, e.g., the failure probability will be enlarged by 1 ∼ 2 orders of magnitude. Besides, contrary to the popular opinion that the uncertainty of structural parameters is trivial compared to that of parameters in the ground-motion model, the importance measures of the structural parameters can be greater than those of the parameters in the ground-motion model by a factor of 2. when the coupling effect of randomness and nonlinearity of structural materials becomes dramatic. Some issues to be further outlined are also discussed. • A two-stage UQ framework for reliability and sensitivity analysis of complex structures is proposed. • Fréchet-derivative-based global sensitivity analysis for detecting importance measures of basic input random variables. • Efficient propagation of uncertainty by synthesizing the probability density evolution method and the change of probability measure. • Simultaneous reliability and sensitivity analysis of a real-world RC structure of 288 m high. [ABSTRACT FROM AUTHOR]

The probability density evolution method (PDEM) synthesizing the change of probability measure (COM) provides a compatible framework for quantifying simultaneous aleatory and epistemic uncertainties. Nonetheless, when the effective support for the change of measure becomes narrow, the quantification error of the PDEM-COM may increase, and the PDEM-COM may even be invalid if the effective support is an empty set. To address this issue, in the present paper, the error analysis of the PDEM-COM is firstly explored, and the generalized F-discrepancy via the COM is utilized to measure the similarity of two different distributions. Then, an augmenting approach is proposed to improve the performance of the PDEM-COM by adding a number of computational simulations. The results indicate that, the PDEM-COM can still be relatively accurate as long as the effective support is sufficiently large, even when there is a significant shift in the probability measure. However, if the effective support turns to be extremely narrow, the error of hybrid uncertainty quantification via the PDEM-COM may be unsatisfied, but it can be significantly reduced by the suggested augmenting approach. Numerical examples are investigated to demonstrate the accuracy and efficiency of the proposed method. Some issues to be further studied are also outlined. • Error analysis of the PDEM-COM is studied. • Numerical illustration of the PDEM-COM for different changes of probability measure. • An augmenting approach is proposed to improve the PDEM-COM. • The accuracy and efficiency of the proposed method is validated by three engineering applications. [ABSTRACT FROM AUTHOR]

This paper deals with the issue on metamodelling (a.k.a. surrogate modelling) of nonlinear stochastic dynamical systems, which are often with multiple input uncertainties Θ ∈ R n , viz., the dimension n may range from low to high (e.g., n ≥ 10). In this paper, to circumvent the problem of "curse of dimensionality" of high-dimensional input uncertainties, the feature spaces of outputs and inputs are firstly extracted from the original output and input spaces, and thus a feature mapping strategy is proposed. To form the feature output space, the nonlinear autoregressive with exogenous inputs (NARX) and the proper orthogonal decomposition (POD) are adopted, while the feature input space is detected by the active subspace method (ASM). It is found that the dimension of feature input (output) space may be much less than the one of original input (output) space, thus the applicability of many metamodelling methods can be naturally enhanced. On the constructed input–output feature space, the procedure of metamodelling is completed by the polynomial chaos expansion (PCE) combined with Kriging, which can capture global behaviours as well as local characteristics of the computational model. Two techniques are introduced to accelerate the proposed feature mapping strategy, consisting of the GF-discrepancy minimization algorithm for the design of experiments (DoEs), and the manifold optimization technique for the parameter identification of ASM. Four benchmarks, including a mathematical function (n = 2), a dynamical quarter car model (n = 10), a Bouc–Wen nonlinear oscillator subjected to earthquake ground motions (n = 30), and the first sub-system (as a black box) of the NASA UQ Challenge 2019 (n = 100), are studied to demonstrate the accuracy and efficiency of the proposed method. Some problems to be further studied are also outlined. [ABSTRACT FROM AUTHOR]

Twenty novel norcantharidin derivatives, which were substituted by thiazole ring, were synthesized in a single step by the [3+2] 1,3-dipolar cycloaddition reaction with oxime or hydrazone in the presence of chloramine-T when compared with the conventional method. J. Heterocyclic Chem., (2011). [ABSTRACT FROM AUTHOR]

Abstract: In order to improve biological behavior of the 1H-1,2,4-triazole derivatives, a series of new ferrocene-analogues of commercial triadimefon were synthesized and their antifungal and plant growth regulatory activities evaluated. These organometallic analogues showed lower antifungal activity than parent triadimefon, but exhibited promising plant growth regulatory activity. [Copyright &y& Elsevier]