Nonlinear System Identification of a Multi-story Building with Geometrical Nonlinearity Using a Deterministic Output-Only-Data Approach

Nonlinear system identification based on output-only data is challenging since the stochastic approaches require the structure to be excited by random input with a uniform Gaussian distribution. This chapter applies a deterministic output-only approach to the parameter estimation of a linear multi-story specimen with an amplitude-dependent geometrical nonlinearity. The approach is independent of the input type, value, and number but requires the excitation to be applied away from the nonlinearity. The vibration responses to high-amplitude excitations are taken into a subspace-based identification algorithm that simultaneously yields both nonlinear and underlying linear parameters. The process is verified by comparing the underlying linear parameters with the linear modal parameters of the structure under low-amplitude excitation. The results indicate a superior accuracy of the estimated parameters in the simulation and an acceptable confidence range for the experimental test

Accelerometer configuration assessment of Milad Tower utilizing operational modal analysis

In operational modal analysis, the observability and quality of the extracted modal parameters, apart from the characteristics of structures, loads, and noise, largely depend on the data acquisition system. Especially in large-scale structures, because of the low frequency- and low amplitude of vibration, the configuration and arrangement of the sensors are of particular interest. Milad Tower, with a height of 435m in Tehran, is the sixth tallest telecommunication tower in the world. The authors have previously identified the tower’s modal parameters by developing an automated data-driven subspace approach and investigated the long-term variations of modal parameters for structural health monitoring purposes. This paper adopts this identification approach to a Milad Tower finite element model subjected to random and seismic loadings. The identified modal parameters are compared with those obtained based on real acceleration records. The MEMS accelerometers’ performance is investigated by stabilizing identified natural frequencies for different setups and scenarios. Also, the performance of various accelerometers temporarily mounted on the tower is compared, and a response signal measured under the most recent earthquake in Tehran is presented. The qualitative and quantitative assessments of the sensors are discussed, and required improvements are recommended

Damage Identification of Offshore Jacket Structure via a Kalman Filter Based Nonlinear State Estimation

Offshore structures, functioning in harsh and extreme environmental conditions, may experience structural damages that can be challenging to reliably identify with conventional methods, especially if the damages do not correspond to a permanent change in the modal properties. In this study, a state-of-the-art Kalman Filter (KF) framework aided by a Finite Element (FE) analysis is employed for nonlinear state estimation and, consequently, structural damage identification of an offshore steel jacket structure subjected to unknown extreme wave loads. The outcome of this approach enables the estimation of the nonlinear structural response that, in turn, allows the localization and quantification of the damage

Force Identification and Response Prediction of an Offshore Platform Using Admittance Function and Incomplete Response Measurements

In several existing structures and infrastructure systems, the loads and responses are hard to be accurately measured especially when the structural systems are partly or wholly inaccessible or located in harsh conditions (e.g., offshore structures). The chapter proposes a frequency-domain technique to estimate the force and response of vibrating structures using limited response signals. The frequency response function of a structure can be reconstructed either based on an updated finite element model’s properties or identified modal parameters by a data-driven approach. Next, the components of the frequency response function corresponding to a few degrees of freedom, whose response measurements are available, can be selected to create the admittance function and, consequently, to estimate the applied loads and predict the response of any degree of freedom. The technique is implemented on the finite element model of an offshore platform exposed to wave loads. The estimated wave loads and response of the submerged elements indicate the proposed technique’s competence in simplicity and efficiency compared with other methods