Ting-Yu Hsu

Application of advanced statistical methods for extracting long-term trends in static monitoring data from an arch dam

The objective of this article is to develop methods for extracting trends from long-term structural health monitoring data and try to set an early warning threshold level based on the results of analyses. The long-term monitoring data in this study is the continuous monitoring of the dam static deformation. Two different approaches were applied to extract features of the long-term structural health monitoring data of the static deformation of the Fei-Tsui Arch Dam (Taiwan). The methods include the singular spectrum analysis with auto regressive model (SSA-AR) and the nonlinear principal component analysis (NPCA) using auto-associative neural network method (AANN). The singular spectrum analysis is a novel nonparametric technique based on principles of multi-variance statistics. An AR model is optimized for each of the principal components obtained from SSA, and the multi step predicted values are recombined to make the time series. Different from SSA method the NPCA-AANN method is also used to extract the underlying features of static deformation of the dam. By using these two different methods, the residual deformation between the estimated and the recorded data was generated, through statistical analysis, the threshold level of the dam static deformation can be determined. Discussion on the two proposed methods to the static deformation monitoring data of Fei-Tsui Arch Dam (Taiwan) is discussed.

On-line structural damage localization and quantification using wireless sensors

In this paper, a wireless sensing system is designed to realize on-line damage localization and quantification of a structure using a frequency response function change method (FRFCM). Data interrogation algorithms are embedded in the computational core of the wireless sensing units to extract the necessary structural features, i.e. the frequency spectrum segments around eigenfrequencies, automatically from measured structural response for the FRFCM. Instead of the raw time history of the structural response, the extracted compact structural features are transmitted to the host computer. As a result, with less data transmitted from the wireless sensors, the energy consumed by the wireless transmission is reduced. To validate the performance of the proposed wireless sensing system, a six-story steel building with replaceable bracings in each story is instrumented with the wireless sensors for on-line damage detection during shaking table tests. The accuracy of the damage detection results using the wireless sensing system is verified through comparison with the results calculated from data recorded of a traditional wired monitoring system. The results demonstrate that, by taking advantage of collocated computing resources in wireless sensors, the proposed wireless sensing system can locate and quantify damage with acceptable accuracy and moderate energy efficiency.

A fractional derivative model to include effect of ambient temperature on HDR bearings

Based on a previous application of the fractional derivative Kelvin model to the seismic response prediction of high damping rubber (HDR) bearings, the effect of ambient temperature is incorporated into the formulation of the model in this study, considering the variation of ambient temperature may significantly influence the mechanical characteristics of the bearings. Shaking table tests are conducted for a test structure composed of a steel deck isolated by four HDR bearings. The bearings were tested at various temperatures ranging from 0 to 28°C. Identified from the results of sinusoidal tests, the effective shear modulus is expressed in terms of maximum shear strain and temperature of the bearing. The numerical constants involved in the fractional derivative Kelvin model are represented by functions of ambient temperature. The extended model is validated by comparing the predicted seismic responses of the test structure with those measured from the earthquake tests conducted at various temperatures.

Performance of the NCREE's on‐site warning system during the 5 February 2016 Mw 6.53 Meinong earthquake

The National Center for Research on Earthquake Engineering in Taiwan has developed an on-site earthquake early warning system (NEEWS). The Meinong earthquake with a moment magnitude of 6.53 and a focal depth of 14.6 km occurred on 5 February 2016 in southern Taiwan. It caused 117 deaths, injured 551, caused the collapse of six buildings, and serious damage to 247 buildings. During the Meinong earthquake, the system performance of 16 NEEWS stations was recorded. Based on a preassigned peak ground acceleration (PGA) threshold to issue alarms at different stations, no false alarms or missed alarms were issued during the earthquake. About 4 s to 33 s of lead time were provided by the NEEWS depending on the epicenter distance. In addition, the directivity of the earthquake source characteristic and also possibly the site effects were observed in the diagram of the distribution of PGA difference between the predicted PGA and the measured PGA.

A damage detection algorithm integrated with a wireless sensing system

In this study, the authors propose a frequency response function change method (FRFCM) which can be integrated with a wireless sensing system to detect damage of a building structure. The FRFCM was derived based on motion equations under a ground excitation both before and after a structure is damaged. The advantage of FRFCM is that only the frequency response functions of some frequency ranges around natural frequencies of a structure are needed to detect the location and extent of a damage. On the other hand, the wireless sensing units have the calculation ability to transform the measured time series to the frequency spectrum using the fast Fourier transform (FFT) algorithm. Therefore, only a few frequency bands of the frequency spectrum in the wireless sensing units are necessary to be delivered to the wireless server, instead of the whole measured time series. By doing so, the transmit power consumption of a wireless sensing unit is greatly reduced, hence increasing the feasibility of on-line damage detection using wireless sensing system based on structural vibration signals. The proposed idea was validated in a shaking table test of a 6-story steel building structure in a laboratory. In order to detect damage on-line automatically via a wireless sensing system, a FFT algorithm and a automatic peak-peaking algorithm for selecting natural frequencies of a structure were imbedded into the wireless sensing units. The damage extent of each story of the structure was displayed on the screen of the host computer automatically after the transmit of fragments of Fourier spectrum from wireless sensing units was done.

Application of the low-cost MEMS-type seismometer for structural health monitoring: A pre-study

The Earthquake Early Warning (EEW) research group at National Taiwan University (NTU) and a technology company have been developing a Micro Electro Mechanical Systems (MEMS) type of accelerometer named palert designed for EEW purpose. The main advantage of palert is that it is a relatively low-cost seismometer. On the other hand, due to the high price of commercial hardware of Structural Health Monitoring (SHM) systems, the application of SHM to buildings is limited. Therefore, the low price of palert devices makes it affordable to general purpose application and would lead to popularization of SHM for buildings. This study serves as a pre-study for this purpose and the feasibility for SHM application for palert is also verified. In order to monitor the health of the building, the method proposed by Nakata et al. is used to estimate fundamental normal-mode frequency of a steel building in the laboratory of the NCREE. The results show that the palert is reliable to measure the buildings response for the most of the normal buildings with less than ten stories. The fundamental normal-mode frequencies estimated using the Palert are quite comparable to the ones estimated using the high-performance accelerometers and data acquisition system. The Palert illustrates the possibility to be used to monitor the health of a building but further studies are still necessary.

Application of Bayesian statistical method in sensitivity-based seismic damage identification of structures: Numerical and experimental validation

Most of the developed sensitivity-based damage detection methods are based on the application of external excitations which could be prohibitive due to infeasible excitation of all structural degrees of freedom. In this regard, identification of damage properties using seismic structural response would be advantageous. In this research, sensitivity-based finite element model updating method is proposed to identify structural damage by earthquake response in the frequency domain and the transfer function of the structure due to ground excitation. The obtained sensitivity equation is solved by linear least square method through defining constraints on the design variables. Since the attainable measured data are restricted by limits on the instrumentations and preciseness of the measurements and due to the fact that only a few of the lower modes of a structure can generally be determined with confidence, a Bayesian statistical method is utilized to enhance the reliability of the predicted damage properties. The proposed technique is applied to a numerical frame model and an experimental six-story steel structure with various scenarios of story stiffness reduction. The results are indicative of the capability of the proposed method for identification of damage location and severity.

Evaluating Post-Earthquake Building Safety Using Economical MEMS Seismometers

The earthquake early warning (EEW)-research group at National Taiwan University has been developing a microelectromechanical system-based accelerometer called “P-Alert”, designed for issuing EEWs. The main advantage of P-Alert is that it is a relatively economical seismometer. However, because of the expensive nature of commercial hardware for structural health monitoring (SHM) systems, the application of SHM to buildings remains limited. To determine the performance of P-Alert for evaluating post-earthquake building safety, we conducted a series of steel-frame shaking table tests with incremental damage. We used the fragility curves of different damage levels and the interstory drift ratios (calculated by the measured acceleration of each story using double integration and a filter) to gauge the potential damage levels. We concluded that the acceptable detection of damage for an entire building is possible. With improvements to the synchronization of the P-Alert sensors, we also anticipate a damage localization feature for the stories of a building.

Strong Ground Motion and Pulse‐Like Velocity Observations in the Near‐Fault Region of the 2018 Mw 6.4 Hualien, Taiwan, Earthquake

The maximum observed peak ground acceleration (PGA) and peak ground velocity (PGV) at various stations during the 2018 Hualien,Taiwan earthquake were 594 Gal and 146 cm=s, respectively. Pulse-like velocities were observed at all stations within a distance of 4 km from the Milun fault. The horizontal spectral accelerations of the pulse-like records indicated two obvious amplifications at periods of roughly 1 and 2 s. Natural frequencies of 0.8–1.5 Hz were observed in the region near the Milun fault using microtremor measurements. The spectral acceleration peak at periods of roughly 2 s is mostly seen in the east–west direction, indicating a typical fault-normal seismic radiation from the fault rupture. Consequently, we contend that the amplifications of spectral acceleration at approximately 1 and 2 s were caused by site amplification and the rupture front, respectively. The site amplification at approximately 1 s may have been one reason for the collapse of medium-rise buildings during this earthquake. Evident soil nonlinearity resulted in smaller horizontal than vertical PGA at many stations in the near-fault region.

Damage Detection for Beam-like Structures Based on Local Flexibility Method with Rotary DOF Measurement

Many vibration-based global damage detection methods attempt to extract modal parameters from vibration signals as the main structural features to detect damage. The local flexibility method is one promising method that requires only the first few fundamental modes to detect not only the location but also the extent of damage. Generally, the mode shapes in the lateral degree of freedom are extracted from lateral vibration signals and then used to detect damage for a beam structure. In this study, a new approach which employs the mode shapes in the rotary degree of freedom obtained from the macro-strain vibration signals to detect damage of a beam structure is proposed. In order to facilitate the application of mode shapes in the rotary degree of freedom for beam structures, the local flexibility method is modified and utilized. The proposed rotary approach is verified by numerical and experimental studies of simply supported beams. The results illustrate potential feasibility of the proposed new idea. Compared to the method that uses lateral measurements, the proposed rotary approach seems more robust to noise in the numerical cases considered. The sensor configuration could also be more flexible and customized for a beam structure. Primarily, the proposed approach seems more sensitive to damage when the damage is close to the supports of simply supported beams. The proposed approach also shows its potential application to damage detection of wind turbine blades and plate structures.