Development of adaptive modeling techniques for non-linear hysteretic systems

Analysis and modification of Volterra/Wiener neural networks for the adaptive identification of non-linear hysteretic dynamic systems

An extended model-based observer for state estimation in nonlinear hysteretic structural systems

SUMMARYVibration isolation is well recognized as an effective mitigation strategy for acceleration‐sensitive equipment subjected to earthquake. In the present paper, an equipment isolation system with nonlinear hysteretic behaviour is proposed and a methodology for the optimal design is developed. An integrable constitutive model, derived from the mathematical Duhem hysteresis operator, is adopted for the isolation system. The optimization procedure is defined through a dual‐criteria approach that involves a transmissibility criterion combined with an energy performance criterion: the former consists in limiting the absolute acceleration of the isolated equipment below an allowable threshold value; the latter, in maximizing the ratio between the energy dissipation due to hysteresis and the input energy to reduce the isolator displacements. The seismic effectiveness of the nonlinear hysteretic isolation system is numerically investigated under natural accelerograms with different frequency content and increasing levels of excitation. Both ground‐mounted and floor‐mounted equipment items are considered in the analyses; in the second case, the dynamic interaction between the equipment and its supporting structure is taken into account in the design of the isolation system, and its effects on the isolation performance and the structural response are discussed. Comparisons in terms of effectiveness and robustness with a linear isolation system with viscoelastic behaviour are eventually provided. Copyright © 2013 John Wiley & Sons, Ltd.

As newer and more reliable ways of construction were developed, civilization began to spread out further and retain functional infrastructure for longer periods of time.[...]

Experimental system identification of the dynamics of a vibro-impact beam with a view towards structural health monitoring and damage detection

To avoid potential life and economic loss, structural health monitoring has become increasingly important for bridge systems. As a key component of structural health monitoring, structural system identification (SSI) tries to identify the structural parameters through the measured response. However, due to the ill-posedness of inverse analysis, the estimation of parameters is greatly affected by measurement errors. To alleviate the adverse effect of errors, the relations among the displacements in beam structures (for instance bridges), compatibility conditions, are derived analytically via a symbolical approach. Then a set of compatible displacements is obtained by an optimization procedure that finds the closest compatible deformation shape to the measured one. The final estimations of the parameters are obtained by providing the compatible displacements to SSI. The proposed method is validated by the numerical simulations on a simply supported bridge and on a continuous bridge.

Structural system identification is a critical task in resilience assessments, especially following a natural hazard. In this paper, we propose PIDynNet, a novel physicsinformed approach that produces an ordinary differential equation-constrained neural network model for structural system identification. PIDynNet improves the estimation of structural parameters of nonlinear structural systems by embedding an auxiliary physicsbased loss term into the overall loss function as well as a supervised data-driven loss term. The proposed framework has the generalization capability to predict nonlinear structural response given unseen ground excitations. Two nonlinear numerical experiments are conducted to demonstrate the advantage of PIDynNet over other identification methods in problems with or without latent variables.

Identification of nonlinear systems is one of the important problems in engineering. Chaotic systems are among those nonlinear systems that have been in the center of attention of many researchers because of their complex and unpredictable behaviors. This paper proposes an algorithm for optimally estimating the parameters of system by minimizing the mean of squared errors (MSE) index. In this paper, Teaching Learning Based Optimization (TLBO) algorithm is used for solving both offline and online parameter estimation problems for chaotic systems. The validity of this algorithm in terms of convergence speed and parameter accuracy in comparison to other popular optimization algorithms such as Standard PSO (PSO), Differential Evolution (DE), Adaptive Particle Swarm Optimization (APSO) and Genetic Algorithm (GA) is shown through an illustrative example for the modeling of chaotic systems. Furthermore, in order to demonstrate the feasibility of this algorithm, it is applied to the problem of parameter identification of a well-known nonlinear Lorenz chaotic system. According to simulation results, the proposed algorithm is a very suitable algorithm for online parameter identification for a class of nonlinear chaotic systems.

Predictive maintenance, as one of the core components of Industry 4.0, takes a proactive approach to maintain machines and systems in good order to keep downtime to a minimum and the airline maintenance industry is not an exception to this. To achieve this goal, practices in Structural Health Monitoring (SHM) complement the existing Non-Destructive-Testing (NDT) have been established in the last decades. Recently, the increasing computational capability such as utilization of a graphical processing unit (GPU) in combination with advanced machine learning techniques such as deep learning has been one of the main drivers in the advancement of predictive analytics in condition monitoring.In our previous work, we proposed a novel approach using deep learning for guided wave based structural health called DeepSHM. As a study case, we treated an ultrasonic signal from guided Lamb wave SHM with a convolutional neural network (CNN). In that work, we only considered a single central frequency excitation. This led to a single governing wavelength which is normally good for the detection of a single damage size.In classical signal processing, applying a broader excitation frequency poses an analysis and interpretation nightmare because it contains more complex information and thus is difficult to understand. This problem can be overcome with deep learning; however, it creates another problem: while deep learning typically results in a more accurate result prediction, it is specifically made for solving only certain types of tasks. While many papers have already introduced deep learning for diagnostics, many of these works are only proposing novel predictive techniques, however the mathematical formalization is lacking, and we are not informed about why we should treat acoustic signal with deep learning. So, the basis of ‘explainable AI’ for SHM and NDT is currently lacking.For this reason, in this paper, we would like to extend our previous work into a more generalized. Rather than focusing on a novel technique, we propose a plausible theoretical perspective inspired from neuroscience for signal representation of deep learning framework to model machine perception in structural health monitoring (SHM), especially because SHM typically involves multiple sensory input from different sensing locations. To do this, we created a set of artificial data from a finite element model (FEM) and represented DeepSHM in two different ways: 1). Perpetual representation of observation and 2). Hierarchical structure of entities that is decomposable in a smaller sub-entity. Consequently, we assume two plausible models for DeepSHM: 1). Either it behaves as a single deciding actor since the observation is regarded as perpetual, and 2). Or it acts as a multiple actor with independent outputs since multiple sensors can form different output probabilities. These artificial data were split into several different input representations, classified into several damage scenarios and then trained with commonly used deep learning training parameters. We compare the performance metrics of each perception model to describe the training behavior of both representations.

The time finite element method (TFM) is employed for parametric identification of nonlinear structural dynamic systems. An advantage of TFM is the ease with which one can calculate the sensitivity of the transient response with respect to various design parameters, a key requirement for gradient-based parameter identification schemes. The method is simple, because one obtains the sensitivities of the response to system parameters by differentiating the algebraic equations, not original differential equations. These sensitivities are used in the Levenberg-Marquardt iterative direct method to identify parameters for nonlinear single- and two-degree-of-freedom systems. The measured response was simulated by integrating the example nonlinear systems using the given values of the system parameters. The accuracy and the efficiency of the present method are compared to a previously available approach that employs a multistep method to integrate nonlinear differential equations. It is seen, for the same accuracy, that the present approach requires fewer data points.

Identification of nonlinear structural system is an important but challenging task for structural health monitoring. Due to the complexities of structural nonlinearities, it is hard to establish proper mathematical models for some structural nonlinear behaviors. Moreover, only partial structural responses can be measured in practice; it is essential to conduct identification of nonlinear structural systems using only partial measurements of structural responses. To cope with these issues, an algorithm is proposed in this article for the identification of some model-free structural nonlinear restoring forces using only partial measurements of structural responses. First, an equivalent linear structural system is introduced for the identification of the locations of structural nonlinearities. Then, a model-free structural nonlinear restoring force is approximated by a power series polynomial. The unknown coefficients of the power series polynomials together with other structural parameters are identified by the extended Kalman filter so that the characteristics of the behaviors of the model-free of nonlinear restoring forces can be identified. Some numerical examples including the identification of two nonlinear multi-story shear frames and a planar nonlinear truss with different structural nonlinear restoring forces are used to validate the proposed algorithm.

This article presents a review of selected articles about structural engineering applications of machine learning (ML) in the past few years. It is divided into the following areas: structural system identification, structural health monitoring, structural vibration control, structural design, and prediction applications. Deepneural networkalgorithms have beenthe subject of a large number of articles in civil and structural engineering.There are, however, otherML algorithms with great potential in civil and structural engineering that are worth exploring. Four novel supervised ML algorithms developed recently by the senior author and his associates with potential applications in civil/structural engineering are reviewed in this paper. They are the Enhanced Probabilistic Neural Network (EPNN), the Neural Dynamic Classification (NDC) algorithm, the Finite Element Machine (FEMa), and the Dynamic Ensemble Learning (DEL) algorithm

The authors have been developing a new tiny (~12 mm cube and <4 g) Bluetooth wireless accelerometer unit based upon the technology that is prevalent in modern Smart Phones and Smart Watches. The primary intended application of this unit is for training/teaching purposes in vibration-based Experimental Modal Analysis (EMA) approaches for Structural System Identification (SSI) and Structural Health Monitoring (SHM). Digitised tri-axial acceleration data from a single unit can be streamed using purpose-specific software on a notebook or tablet at up to 400 Hz where the longest clear distance of this unit from the streaming device, (smart phone, tablet, laptop computer), is less than approx. 15 m. It is anticipated that tri-axial data synchronously captured from up to 8 accelerometer units at a sweep rate of 100 Hz per set, can be achieved on completion of the second stage of development of this system. This will certainly be suitable for use on model structures for teaching purposes, but also for industrial use (machinery; on-board vehicles; smaller structures) where the clear distance from the computer (or tablet) to these units is less than approx. 15 m. This paper focuses on the performance testing of a collection of commercially available accelerometers and compares these against the capabilities being aimed for in our new miniature accelerometer units.

Structural system identification using Least Mean Square (LMS) adaptive technique

Estimating the governing equation parameter values is essential for integrating experimental data with scientific theory to understand, validate, and predict the dynamics of complex systems. In this work, we propose a new method for structural system identification (SI), uncertainty quantification, and validation directly from data. Inspired by generative modeling frameworks, a neural network maps random noise to physically meaningful parameters. These parameters are then used in the known equation of motion to obtain fake accelerations, which are compared to real training data via a mean square error loss. To simultaneously validate the learned parameters, we use independent validation datasets. The generated accelerations from these datasets are evaluated by a discriminator network, which determines whether the output is real or fake, and guides the parameter-generator network. The uncertainty in each parameter is quantified by fitting a Gaussian distribution to each parameter, then computing the 95% confidence bounds. Analytical and real experiments show the parameter estimation accuracy and model validation for different nonlinear structural systems.