Nonstationary Excitation Research Articles

Multi-Performance Economical Optimization of Base Isolation System with Tuned Inerter Negative Stiffness Damper Subjected to Non-Stationary Seismic Excitation

Base isolation proves to be an effective vibration control strategy for buildings. However, the base-isolation floor (BIF) may undergo substantial displacements. To improve the seismic performance of base-isolation system, this paper proposes a multi-performance economical optimization procedure (MPEOP) for exploring the optimal parameters of base isolation system with tuned inerter negative stiffness damper (BIS-TINSD) subjected to non-stationary seismic excitation. The simplified analysis model for BIS-TINSD is developed, followed by the formulation of the state-space representation. The non-stationary seismic excitation is represented as a stationary Gaussian process with a time-modulating function based on the Clough–Penzien spectrum, and combining the equations of motion with seismic excitation results in the augmented state-space representation of structure-damper-excitation, followed by the formulation of differential Lyapunov equation. The multi-objective performance economical index is proposed, in which the performance optimization objective is defined as a weighted combination of base isolation floor displacement and superstructure acceleration, and the economic optimization objective is set as the support stiffness. The Pareto optimal fronts (POF) are adopted to deal with optimal objectives. The examination of optimal design parameters is extended under real earthquake records. The results demonstrate the effectiveness of the MPEOP, and indicate the advantages of the BIS-TINSD compared with the inerter amplify damper (IAD) and negative stiffness inerter damper (NSID).

Sequential simulated annealing for life-cycle optimization of nonlinear stochastic systems via arbitrary polynomial chaos expansion

Quantifying the uncertainties of engineering systems modeled as nonlinear oscillators subject to random excitation is a theoretically complex and computationally demanding task. Consequently, finding an optimal design of such systems considering their life-cycle performance is prohibitive with Monte Carlo simulation methods. In this paper, an efficient performance-based design optimization approach is developed for finding the optimal parameters of engineering systems modeled as nonlinear/hysteretic oscillators subject to stationary and non-stationary excitation. This novel approach utilizes an arbitrary polynomial chaos expansion to estimate the expected cost of failure without performing a computationally expensive integration over the hazard levels. Moreover, we introduce a novel sequential heuristic optimization scheme based on simulated annealing to minimize the total expected cost over the structure life-cycle. Three examples are included in the paper to assess the developed optimization scheme. First, we use the developed framework to optimize a linear single-degree-of-freedom oscillator subject to broadband excitation. Second, a multi-degree-of-freedom oscillator with cubic nonlinearity in damping and stiffness, subject to stationary broadband excitation, is optimized to show the influence of the problem dimensionality in the optimization process. In the last example, a multi-story reinforced concrete shear building modeled as a multi-degree-of-freedom Bouc-Wen oscillator with stiffness and strength degradation and subject to multi-hazards modeled as stationary (wind excitation) and non-stationary (earthquake) stochastic processes, is optimized.

Nonstationary time-frequency characteristics of vehicle suspension systems based on evolutionary power spectral density

Improving the ride comfort for vehicles that travel at varying speeds is a nonstationary problem and has been an increasingly important topic for suspension system research. Accurate identification of frequencies associated with the resonant responses is needed for effective design of active suspension control strategies. However, traditional time-frequency (TF) methods have resolution limitations in either the time domain or the frequency domain. This paper proposes a new methodology to employ the evolutionary power spectral density (EPSD) to describe the responses of vehicles in the time-frequency domain. The key to calculate the responses in nonstationary cases is to derive the nonuniform modulation function which is obtained by the pseudo excitation method. To illustrate the applicability of the proposed method, an example of a seven-degree-of-freedom vehicle model is investigated in which the nonstationary excitations acting on the wheels are computed by the nonuniform modulation function and a precise integration method. Results show that resolutions obtained by the EPSD are more precise than the traditional TF methods with less than 0.2 Hz of bandwidth in both the lower frequency range at the first natural frequency of 1 Hz and in the higher frequency around 10 Hz. Moreover, the changing of the resonant frequency with varying speeds provides the insights, from the perspective of human body’s sensitivity to frequencies of vibration, to improve the ride comfort for vehicles under nonstationary driving conditions.

Analytical Study on Response Power Spectral Density of Structures Subjected to Nonstationary Excitation Considering Transient Responses

Analytical Study on Response Power Spectral Density of Structures Subjected to Nonstationary Excitation Considering Transient Responses

Research on robust fault-tolerant control of the controllable suspension based on knowledge-data fusion driven

For the robust fault-tolerant control of the controllable suspension system, a control strategy driven by knowledge-data fusion is proposed. Firstly, the boundary fuzziness between perturbation type uncertainty and gain type fault is analyzed, and then a data-driven method is introduced to avoid the state estimation of system uncertainty and fault. The proximal policy optimization algorithm in reinforcement learning is selected to construct a “data control law”, to deal with uncertainty and fault. On the other hand, based on the classical sky-hook control, the “knowledge control law” for system performance optimization is designed, taking into account the nonlinear and non-stationary characteristics of the system. Furthermore, the dependency between robust fault tolerance and performance optimization control is revealed, and the two control laws are fused by numerical multiplication, to realize the performance matching optimization control of robust fault tolerance of controllable suspension system driven by knowledge-data fusion. Finally, the effectiveness and feasibility of the proposed method are verified by the simulation and real-time experiment of non-stationary excitation and near-stationary excitation under the combination of uncertainty and fault.

Active learning guided automated cable force monitoring based on modified S-transform

This study introduces an active learning-guided online cable force monitoring system based on a modified S-transform approach, addressing key challenges in real-time system identification: complex non-stationary excitations, computational efficiency, and robustness. The framework identifies cable tension during non-stationary wind loads, significantly improving accuracy and efficiency via an extended active learning Kriging method. It effectively detects potential outliers, identifies the cable's fundamental frequency using a data fusion technique, and calculates real-time cable force and tensile stress with empirical formulae. A comprehensive analysis, including numerical and sensitivity studies, shows an error rate of less than 4 % in all cases, proving the proposed framework's superior accuracy, efficiency, and robustness compared to traditional methods. Laboratory validations using cable test data and Jiu Zhou Bridge data demonstrate the system's stability, even under extreme conditions, such as during Super Typhoon Mangkhut, providing a reliable solution for real-world cable force online monitoring.

Reliability-based design optimization of tuned mass-damper-inerter for mitigating structural vibration

The tuned mass-damper-inerter (TMDI), comprising mass blocks, springs, dampers, and two-terminal inertial elements, has garnered significant attention as an advanced iteration of the tuned mass damper (TMD) in recent years. However, previous studies of TMDI's design are primarily conducted according to the theoretical formulas based on deterministic analysis assuming harmonic excitations or stochastic analysis assuming Gaussian white noise type excitations. This treatment falls short of capturing the non-stationary and non-Gaussian characteristics exhibited by engineering excitations and thus cannot ensure the safety of controlled structure. A few studies considering non-stationary excitations and based on reliability metrics, in conjunction with stochastic simulation methods, often confront excessive computational overhead. Aiming at such a challenge, the reliability-based design optimization (RBDO) of TMDI system, combining the probability density evolution method (PDEM) and genetic algorithm (GA), for mitigating structural vibration under non-stationary excitations is performed in this study. For the purposes of illustration, a single-degree-of-freedom system attached the TMDI and subjected to stochastic ground motions is studied. Considering the cases of different levels of inertance-to-mass ratios, the RBDO of the TMDI is carried out. The solutions by the theoretical formulas and by the variance-based design optimization are compared to verify the superiority of the proposed method. For assessing the seismic mitigation performance of the optimized TMDI system, probability density evolution analysis of structure-TMDI system with three levels of inertance-to-mass ratios is performed. Comparison between the RBDO of TMDI and that of TMD under consistent mass ratio is further addressed. Numerical results reveal the technical advantages of TMDI in a probabilistic sense, i.e., high vibration mitigation performance, considerable mass reduction, and less stroke demand.

The combined influence of bond–slip and joint-shear in the seismic upgrading via externally-attached BFRP-bar reinforced precast sub-frames

Earthquake causes severe damage to building structures, and seismic upgrading is an important solution to enhance the capacity of existing buildings. In recent years, the externally-attached substructure technique has received broad attention, which upgrades the existing buildings on the system level and proves to be prominently effective in the overall performance. In this paper, the authors develop an externally-attached BFRP-bar reinforced precast concrete sub-frame for seismic upgrading. In the Section 2, the working principles of the novel externally-attached technique are introduced. In the Section 3, the critical issues and simulation strategies of the novel externally-attached technique are proposed (i.e., bond–slip and joint-shear effects). In the Section 4, the generation approach of stochastic nonstationary excitations is given, and in the Section 5, the application examples via three concrete frames are analyzed. In general, the research verifies the effectiveness of the proposed externally-attached BFRP-bar reinforced precast concrete sub-frame for upgrading. The individual influence of bond–slip or joint-shear will apparently increase the drift ratio and lower the upgrading performance. Meanwhile, the combined influence of bond–slip and joint-shear is important in consideration, which will further affect the upgrading performance and enlarge the structural demands. Ignoring the combined influence of the two critical issues in analysis or incorporating only one factor in design will overestimate the structural behaviors and underestimate the potential risks after the externally-attached upgrading. In a sense, the research provides an important reference for the further investigation of the combined influence of bond–slip and joint-shear in the seismic upgrading.

A KDE-based non-parametric cloud approach for efficient seismic fragility estimation of structures under non-stationary excitation

With the development of performance-based earthquake engineering, the risk-informed assessment framework has received broad recognition over the world, of which the probability seismic fragility analysis is an important step. The classic seismic fragility adopts the lognormal assumption and forms a parametric derivation. With the development of fragility theory, researchers are hoping to seek out non-parametric approaches to express the intrinsic fragility in a pure analytical form without any distribution assumptions. Besides, how to keep the calculation efficiency (e.g., combining with cloud approach) and how to consider the non-stationary stochastic responses (e.g., combining with non-stationary stochastic excitation model) are critical aspects in fragility that deserve further attention of researchers. In this paper, a kernel density estimation (KDE) based non-parametric cloud approach is proposed for efficient seismic fragility estimation of structures under non-stationary excitation. First, the methodology framework of the efficient approach is illustrated. Then, the procedures of non-stationary stochastic seismic response of structures and KDE-based non-parametric cloud approach for efficient seismic fragility are demonstrated. After that, an application example via a three-span-six-story reinforced concrete frame is given for implementation, followed with a parametric analysis of critical factors. During the process, the classic parametric linear-regression based cloud approach (cloud-LR) and benchmark Monte-Carlo-simulation based cloud approach (cloud-MCS) are also incorporated for validation. In general, the analysis verifies the effectiveness of the non-parametric cloud-KDE approach without requiring more computation work (i.e., same as the parametric cloud-LR approach and much less than the benchmark cloud-MCS approach). Meanwhile, the non-parametric cloud-KDE approach indicates a comparable accuracy with the classic fragility approaches (i.e., less deviation than the parametric cloud-LR approach and much closer to the benchmark cloud-MCS approach), and with the increase of stochastic cloud-point number, the corresponding fitting degree of cloud-KDE approach is growing better. The research provides a new sight for the development of non-parametric seismic fragility approach, and the corresponding findings can be further combined with the probabilistic hazard and risk analysis for a non-parametric assessment procedure in performance-based earthquake engineering.

Simulation of non-stationary ground motions and its applications in high concrete faced rockfill dams via direct probability integral method

Many high concrete face rockfill dams (CFRDs) have been constructed in earthquake-prone areas in recent years, and it is essential to evaluate the seismic safety performance of these dams considering the randomness of seismic excitation. In this paper, the relationship between the stochastic ground motion generation method and the direct probability integral method (DPIM) is established by using the number theory point selection method as the bridge. The DPIM is introduced into the field of stochastic dynamic analysis and multi-index reliability evaluation of high CFRD for the first time. First, a non-stationary stochastic excitation model at five sites is established based on the latest standard for the seismic design of hydraulic structures. Then, taking a practical project as an example, stochastic dynamic response analysis and reliability assessment based on dam crest settlement deformation and dam slope cumulative slippage are conducted by using DIPM. Finally, the influence of these two indexes on reliability is explored by drawing the equal failure probability curve. The results show that the stochastic dynamic analysis and reliability assessment framework established in this paper can provide probabilistic seismic safety assessments for high CFRDs. Sliding damage of the dam slope is more likely than the deformation damage of the rockfill from the perspective of probability.

Reliability-based topology optimization of fractionally-damped structures under nonstationary random excitation

This contribution proposes an effective reliability-based topology optimization (RBTO) framework for the layout of viscoelastic dampers of energy-dissipating structures under nonstationary seismic excitation, in which the constitutive behavior of viscoelastic damper is described by the novel fractional derivative models. To formulate the RBTO problem, the installation number of fractional viscoelastic dampers is minimized under the constraints of dynamic reliability, and the design variables are chosen as the damper parameters and the existence variables of the potential fractional viscoelastic dampers. The explicit responses are first established for the fractionally-damped structure, and the explicit response sensitivities with respect to the design variables are further derived through the adjoint variable method (AVM). On this basis, an explicit time-domain method (ETDM) is developed for explicit formulation of the statistical moments of responses and the moment sensitivities of the fractionally-damped structure, which are further employed to analytically derive the first-passage dynamic reliabilities and the reliability sensitivities of the structure based on the classical level-crossing process theory. Finally, the RBTO problem for the layout of fractional viscoelastic dampers is solved with the gradient-based method of moving asymptotes (MMA), in which the existence variables of fractional viscoelastic dampers can be driven to binary solutions by the solid isotropic material with penalization (SIMP) technique. An engineering application involving a building frame structure installed with fractional viscoelastic dampers is presented to validate the feasibility of the proposed ETDM-based RBTO framework.

A non-parametric algorithm for time-dependent modal analysis of civil structures and infrastructures

Vibration-based monitoring strategies have been demonstrated to be effective tools in providing – in nearly real-time – reliable information regarding the integrity of structures and infrastructure systems. However, commonly used methods for vibration analysis and modal identification are not able to capture the time variation of the modal properties during single acquisitions, hence they cannot perform dynamic identification in the presence of nonlinearities or non-stationary input excitations. To overcome this limitation, a novel non-parametric algorithm for automatic time-dependent modal analysis is hereby presented and discussed. This Enhanced Modal Identification for Long-term Integrity Assessment (EMILIA) algorithm can compute time-dependent estimations of the natural frequencies and mode shapes that can be critical to the early identification of hidden damage. The dynamic characterization of a beam-like structure in sound and damaged conditions is carried out for numerical validation purposes, allowing to evaluate the reliability of the proposed method over different scenarios and comparing its efficiency against traditional algorithms. Finally, further tests are conducted to analyse the sensitivity of the EMILIA algorithm to its main parameters and components.

Non-stationary non-Gaussian random vibration analysis of Duffing systems based on explicit time-domain method

Non-stationary non-Gaussian random vibration problems of structures are challenging and drawing increasing attention. In the present study, firstly, an explicit time-domain method (ETDM) is proposed to determine the higher-order response statistics of linear systems subjected to non-stationary non-Gaussian random excitations, in which the first four orders of cumulants of dynamic responses are directly formulated through the cumulant operation rule based on the explicit expressions of responses. Secondly, an equivalent linearization – explicit time-domain method (EL-ETDM) is further developed to solve the non-stationary non-Gaussian random vibration problems of Duffing systems, in which the equivalent linear system is derived discarding the assumption of Gaussian response, and the corresponding higher-order cumulant analyses of the linearized system are accomplished by the efficient ETDM. The present approach can account for non-Gaussian random excitations with arbitrary forms, and two specific applications to the Poisson white noise and the square form of Gaussian random process are investigated. Four numerical examples are presented to demonstrate the effectiveness of the proposed methods.

Response and EPSD of rotor-blade nonlinear system with non-stationary non-Gaussian stochastic excitation via PGHW method

Response and EPSD of rotor-blade nonlinear system with non-stationary non-Gaussian stochastic excitation via PGHW method

An efficient time-variant reliability analysis strategy embedding the NARX neural network of response characteristics prediction into probability density evolution method

Stochastic response analysis and first-passage reliability evaluation of multi-degree of freedom nonlinear systems subject to non-stationary seismic excitation have been a critical issue in the field of compound random vibrations, yet remain unsolved. This study extends the time-varying reliability analysis method based on the probability density evolution method, making it applicable to the problem of stochastic response analysis and first-passage reliability evaluation of complex engineering structures. To efficiently represent the response of representative samples, a nonlinear autoregressive with exogenous inputs neural network model is introduced in this study. After comparing three different optimization methods, the neural network model based on the Bayesian regularization algorithm is selected as the predictor for the response of complex nonlinear systems, enabling efficient stochastic response analysis and time-varying reliability evaluation. To this end, leveraging the high efficiency of the probability density evolution method, the analysis of time-varying reliability of large-scale structures has been successfully conducted. Furthermore, the proposed method can be easily expanded to dynamic reliability evaluation problems represented by extreme value theory. Three numerical examples are quoted to demonstrate the applicability and advantages of the proposed method in the field of first-passage and extreme-value problems of systems, which are also compared with PCE, SVR, and Kriging. The results from these numerical examples indicate that the proposed method can effectively reduce the required calculational cost for the reliability analysis of complex structures while maintaining high analytical accuracy.

Survival probability determination of nonlinear oscillators subject to combined deterministic periodic and non-stationary stochastic loads

This paper presents a technique based on Wiener path integrals (WPI) for computing the survival probability of nonlinear oscillators under combined nonstationary stochastic and periodic excitation. Specifically, the equation of motion is equivalently decomposed into a correlated combination of a nonlinear stochastic differential equation (SDE) and a nonlinear deterministic differential equation. The nonstationary response probability density function (PDF) of the stochastic component is obtained using the Wiener path integral concept in conjunction with stochastic averaging/linearization. The approach involves utilizing stochastic averaging/linearization techniques to transform the nonlinear SDE into a linear one with varying equivalent stiffness and damping. Afterwards, using the WPI method and the concept of the most probable path, a closed-form approximate analytical expression of the joint transition probability density function (PDF) is obtained for small intervals corresponding to the equivalent linear SDE. Additionally, the survival probability of the original nonlinear oscillator subject to combined excitation with fixed barriers can be approximated equivalently by using the survival probability with time-varying barriers of the equivalent linear system with time-varying stiffness and damping subject only to the nonstationary stochastic excitation. Finally, by using the analytical expression of the transition PDF of the equivalent linear system and a discrete version of the Chapman–Kolmogorov (C–K) equation, it is possible to determine the response and survival probability of the original oscillator under combined excitation step by step. Examples involving a Duffing oscillator and a vibro-impact oscillator are analyzed to showcase the precision and efficacy of the suggested technique in contrast to relevant Monte Carlo simulations.

Novel Girsanov correction based Milstein schemes for analysis of nonlinear multi-dimensional stochastic dynamical systems

This work proposes Girsanov corrected explicit, semi-implicit, and implicit Milstein approximations for the solution of nonlinear stochastic differential equations. The solution trajectories provided by the Milstein schemes are corrected by employing the change of measures, aimed at removing the error associated with the diffusion process incurred due to the transformation between two probability measures. The change of measures invoked in the Milstein schemes ensures that the solution from the mapping is measurable with respect to the filtration generated by the error process. The proposed scheme incorporates the error between the approximated mapping and the exact representation as an innovation that is accounted in the Milstein trajectories as an additive term. Numerical demonstrations using parametrically and non-parametrically excited stochastic oscillators, a practical problem involving ring type gyroscope, and a 7-degree-of-freedom nonlinear structural system subjected to both stationary and non-stationary excitation demonstrate the improvement in the solution accuracy for the proposed schemes with much coarser time steps when compared with the classical Milstein approximation with finer time steps.

Design sensitivity analysis of structural systems with damping devices subjected to fully non-stationary stochastic seismic excitations

In the framework of dynamic excitations to be considered during the design phase of structures, the most crucial one is the ground motion acceleration. To increase the structural performances against seismic actions, one of the most effective design criteria is to introduce damping devices.An efficient approach to define the optimal parameters of the damping system is based on the design sensitivity analysis, which provides a quantitative estimate of desirable design change, by relating the available design variables.In this paper a method to evaluate the sensitivities of stochastic response characteristics of structural systems with damping devices subjected to seismic excitations, modelled as fully non-stationary Gaussian stochastic processes, is proposed. The main steps are: i) to define the time–frequency varying response (TFR) function for non-classically damped systems; ii) to evaluate closed form solutions for the first-order derivatives of the TFR function as well as of the one-sided evolutionary power spectral density function of the structural response, with respect to damping parameters of devices; iii) to perform a design sensitivity analysis selecting as performance measure function the non-geometric spectral moments of nodal displacements.A numerical application demonstrates how the proposed approach is suitable to cope with practical problems of engineering interest.

Dynamic reliability analysis of structures under nonstationary stochastic excitations using tail-modified extreme value distribution

In practical engineering, it is usually necessary to compute the small first-passage probability of dynamic systems exposed to nonstationary stochastic excitations. However, it is still a challenging problem to efficiently estimate the super-small failure probability using an approximate extreme value distribution (EVD) based on a small number of simulated samples. In this study, a novel EVD model is proposed to describe the probability distribution of the structural extreme response induced by nonstationary stochastic excitations. This EVD model consists of two parts: the main body is modeled by a truncated-shifted generalized lognormal distribution, and a monotonic exponential model is proposed to fit the tail region. Besides that, a criterion for determining the breakpoint between the main body and tail region is proposed, which ensures the non-negativity and normativity of the proposed EVD model and allows the EVD to be reconstructed accurately and efficiently, particularly in its tail region. In this regard, the corresponding first passage probabilities under different thresholds can be straightly evaluated. The precision and completeness of the proposed EVD model are investigated using two numerical examples that consider linear and nonlinear frame structures subjected to nonstationary ground motion accelerations. The results show that the proposed model is in excellent agreement with the results of Monte Carlo simulation, even when the failure probability is very small.

Bayes-informed mixture distribution for the EVD estimation and dynamic reliability analysis

In this paper, a Bayes-informed mixture distribution is proposed to capture the underlying implicit extreme value distribution of a nonlinear dynamic system subjected to stochastic seismic excitation. First, three possible candidate distributions considering the properties of the extreme value distribution are selected, i.e., Gumbel, inverse Gaussian and Gamma distributions. Then, the mixture of these possible candidate distributions is conducted from Bayesian perspective by using the proposed moment-generating function-guided likelihood function. The benchmark data for constructing the likelihood function is the function value of the moment-generating function related to the unknown extreme value distribution, which can be calculated by the Latinized partially stratified sampling technique. The predicted data can be calculated by the mixed moment-generating function of the mixture distribution. Finally, the mixture distribution’s unknown parameters can be calibrated via the Bayesian inference and transitional Markov chain Monte Carlo so that the calibrated mixture distribution can be a representation of the implicit extreme value distribution. Regarding the dynamic reliability analysis, the first passage failure probability can be readily obtained from the mixture distribution. Three nonlinear dynamic systems subjected to the fully non-stationary seismic excitations, i.e., a single-degree-of-freedom system, a 3-D reinforced concrete frame structure and a practical highway bridge are investigated to validate the efficiency and accuracy of the proposed method for extreme value distribution estimation.