Patrick T. Brewick

Data-Based Nonlinear Identification and Constitutive Modeling of Hysteresis in NiTiNOL and Steel Strands

AbstractSeveral different data-driven strategies for nonlinear identification are applied to experimental data exhibiting various types of hysteretic behavior. The experimental data contain displacement and restoring force information for several tests conducted using different configurations of a rheological testing device with various assemblies of nickel titanium-Naval Ordnance Laboratory (NiTiNOL) and steel wire strands. Among the different configurations, the response of the wire strands shows three distinct forms of nonlinear behavior: classical quasi-linear softening hysteresis; strongly pinched, hardening hysteresis; and slightly pinched, hardening hysteresis. The data-driven methods applied for nonlinear identification include polynomial basis functions and neural networks. The polynomial basis nonlinear identification methods are used for the construction and characterization of reduced-order models to gain insight into the physical modeling of the hysteretic phenomena. The neural network method...

Vibration Mitigation and Monitoring: A Case Study of Construction in a Museum

Vibration from demolition and construction activities poses a serious risk to museum objects. This case study presents preventive conservation and vibration monitoring strategies developed in response to a large-scale renovation project on the floor directly below the Egyptian Art galleries of the Metropolitan Museum of Art in order to safeguard this fragile, ancient art collection. The paper discusses the methods and procedures that were developed not only to protect the art but also to allow visitors continued access to as much of the collection as possible during the work period. In advance of the construction, pilot testing was performed to determine the levels of vibrations caused by different tools, as well as to gain a better understanding of vibration propagation within the museum and to specific objects through their mounts, pedestals, and display shelves. Vibration prone installations were modified with isolation and/or dampening approaches to mitigate vibration, or when possible, selected objects were deinstalled. A variety of mitigation solutions were shown to be effective through testing. During the demolition and construction phase, continuous wireless vibration monitoring was provided from within the galleries, and sometimes from sensors directly on objects or their shelves to provide near real-time alerts to museum staff and construction personnel. Alert levels were based on frequency independent velocity levels.

Exploration of the Impacts of Driving Frequencies on Damping Estimates

AbstractAccurate estimation of the damping in a structure has remained an important but challenging problem for the structural engineering community. The relative difficulty of damping estimation can be compounded when the excitation is not uniformly broadband or ambient in nature, such as when car traffic or large trains travel over a bridge. A bridge model that consisted of a series of simply supported (SS) stringers resting atop a larger girder was constructed using finite elements, and several simulations were conducted in which cars and trains crossed the bridge model. The presence of the cars and trains led to the appearance of driving forcing frequencies in the response. Driving frequencies are inherent to moving loads and proportional to the velocity of the moving loads and the length of the beam or bridge being crossed. The moving loads produced a pulse-like response in the SS stringers on the bridge model, and the resulting power spectral density (PSD) of the stringers showed that its power was ...

Efficient Data Fusion and Practical Considerations for Structural Identification

This chapter represents a partial summary of several presentations given as part of the associated short course at CISM. The primary topic is on the use of data fusion techniques in structural system identification. Here the data fusion concept means the bringing together of sensor measurements from different kinds of sensors measuring different dynamic response quantities to provide a more accurate estimate of the dynamic states as well as the improved identification of model parameters. Data fusion scenarios discussed include the situation when different sensors are either collocated or not. In the last section, some separate work related to practical challenges of damping estimation is examined using operational modal analysis in the context of driving frequencies caused by traffic on multi-span bridge structures.

A Data-based Probabilistic Approach for the Generation of Spectra-Compatible Time-History Records

ABSTRACT A data-based procedure is presented to develop spectra-compatible time-history records that are based on the dominant probabilistic features of an ensemble of records corresponding to the non-stationary stochastic phenomena of interest (e.g., earthquakes, wind loads, etc). The method requires a statistically significant collection of time-history records that are used to construct the associated covariance kernel of the random process. Subsequently, orthogonal decomposition approaches are used to determine the dominant eigenvectors of the covariance matrix, and these vectors are then linearly combined, with an adjustable amplitude-scale and phase-shift, to determine, via a nonlinear optimization scheme (employing a combination of stochastic and deterministic approaches), a time-history record that matches the target spectrum within a specified error bound. The utility of this approach is demonstrated with several collections of earthquake records from different regions of the world (Japan, Los Angeles, and San Francisco) that are then used to match various spectra widely used in seismic design applications. Issues that impact the selection of the bases vectors to construct the optimum spectra-matching record are discussed, and guidelines are provided for successful implementation of the proposed methodology.

Hybrid Time/Frequency Domain Identification of Real Base-Isolated Structure

This paper presents a case study using hybrid time- and frequency-domain identifications in a synergistic manner to develop models of a full-scale experimental base-isolated structure. This four-story reinforced-concrete building on an isolation layer (of rubber bearings, elastic sliding bearings, passive metallic yielding dampers, and controllable oil dampers) was designed and constructed at the large-scale Japanese NIED E-Defense earthquake engineering laboratory. A variety of sensors, including accelerometers, were mounted within the structure to measure building response to shake table excitations. While the building was ultimately subjected to historical and synthetic ground motions, the recorded table and building accelerations during a number of random excitation tests are used to identify the structure’s natural frequencies, damping ratios and mode shapes. The substantial damping provided by the isolation layer necessitates adopting a hybrid time- and frequency-domain approach for identification. The modes of the structure are separated by frequency content wherein lower frequency modes are identified using time domain approaches from the subspace identification family of methods and higher frequency modes are identified using frequency response functions. Individually, neither approach is able to successfully identify all of the desired modes but, through their combination, the modal properties of the structure are successfully characterized.

Increasing the Efficiency of Blind Source Separation Methods for Improved Modal Parameter Estimation

One of the most challenging cases for performing parameter estimation on a structure is in the absence of input data. These systems are commonly referred to as “output-only” and the identification of their modal parameters is known as operational modal analysis (OMA). One of the newer arrivals to OMA is a family of methods referred to as blind source separation (BSS). These methods are powerful tools for OMA because they limit assumptions required of measured responses and do not involve transformations to another domain. Second-order blind identification (SOBI) is a popular method within the BSS family, and this method has shown great promise in identification of modal parameters for structural systems. However, SOBI methods operate using several output covariance matrices computed over a series of time lags, and larger systems or longer recordings quickly become cumbersome due to the size and number of matrices required for accurate identification. A new technique is proposed that increases the efficiency of SOBI methods by reducing the number of time-lagged covariance matrices required to produce highly accurate estimates of modal parameters. The technique is based on randomly selecting the time-lagged matrices as opposed to choosing them sequentially. The random selection of covariance matrices greatly reduces the correlation between matrices, which is an issue inherent to sequential selection. The proposed randomized approach is applied to a series of systems, ranging from simple sinusoids to a sample structural model. In each case the performance of the randomized approach is compared to the traditional sequential selection of time-lagged covariance matrices and the randomized approach consistently demonstrates superior performance both in terms of accuracy and efficiency of identified mode shapes. doi: 10.12783/SHM2015/164

Efficient Bayesian Model Selection for Identifying Locally Nonlinear Systems Incorporating Dynamic Measurements

In Bayesian model selection, suitable mathematical models are selected among a set of possible models using Bayes’ theorem. To simplify the analysis, linear structural models are often used, though they are not always adequate to accurately compute the structural response. Nonlinear models, which may be more accurate, significantly increase the required computation time for Bayesian model selection. A method is proposed in this paper to reduce the computational cost by incorporating into the model selection process the authors’ previously developed efficient dynamic response algorithm for response of locally nonlinear systems. The efficacy of the approach is demonstrated, using the numerical example of a base-isolated building on hysteretic lead rubber bearing (LRB) isolators, with different linear and nonlinear model classes as candidates to represent the LRBs. doi: 10.12783/SHM2015/288

Modal Analysis of a Full-scale Four-story Reinforced-concrete Base-isolated Building Subjected to Random and Simulated Earthquake Shake Table Excitations

Full-scale dynamic earthquake engineering experiments are costly, yet they are vital for the advancement of seismic protection for large structures. The main goal of such large-scale studies is to accurately replicate the response of complex structures. E-Defense, a part of Japan’s National Research Institute for Earth Science and Disaster Prevention (NIED), constructed and tested a four-story reinforced-concrete base-isolated building to study, among other objectives, how different energy dissipation devices in the building isolation layer improve the response subjected to strong impulsive and long period excitations. This structure was subjected in August 2013 to a series of tests. Analyzing the resulting acceleration responses from the random ground motion tests forms the first step in identifying the dynamic characteristics of this test structure. Specifically, enhanced canonical correlation analysis (ECCA) and the advanced numerical algorithm for subspace state-space system identification (N4SID), from the stochastic subspace identification (SSI) family of methods, were used to estimate the modal parameters. Additional identification was then performed using responses recorded during simulated earthquakes. The recovered modal parameters were compared against the nominal parameters to analyze the behavior of the building’s isolation system, as well as the effectiveness of the identification methods. doi: 10.12783/SHM2015/166