Shock and Vibration

Volume 2017 (2017), Article ID 5830124, 7 pages

https://doi.org/10.1155/2017/5830124

## Source Parameter Estimation Method for Assessment of Structural Resiliencies

School of Civil Engineering, Beijing Jiaotong University, Beijing, China

Correspondence should be addressed to Boming Zhao

Received 21 July 2017; Accepted 1 October 2017; Published 30 October 2017

Academic Editor: Xing Ma

Copyright © 2017 Zijun Wang and Boming Zhao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Assessing structural integrity and sustainability during natural hazards, for instance, strong earthquakes, is an effective way to reduce or even avoid large losses of life and property damage. With the established vulnerability relationships between source parameter and building damage, the seismic resilience of structures can be obtained after the source parameter is estimated in the early stage of an earthquake. For this purpose, we propose a method that employs the wave displacement parameter to estimate earthquake magnitude in real time to quick assess the structural resiliencies. By selecting period and amplitude parameter as comparisons, the magnitude estimation formulas are derived, respectively, where the proposed P wave displacement parameter method is of the highest precision. Through the evolutionary estimation of the P wave displacement parameter as a function of the time window used, we show that the existing regression relationships can be extended to the large earthquake. Therefore, this paper provides a quick earthquake magnitude estimation method for the establishment of a more reasonable and accurate resilience assessment system for structures.

#### 1. Introduction

Assessing the structural integrity and sustainability during natural hazards, for instance, strong earthquakes, is an effective way to reduce or even avoid large losses of life and property damage. With the established vulnerability relationships between source parameter and building damage, if we can estimate the earthquake’s magnitude before the high-intensity shaking occurs, the seismic resilience of structures can be obtained. Earthquake early warning systems [1–4] can provide alerts of impending ground motions within a few seconds to tens of seconds after an earthquake occurs so that appropriate measures can be immediately taken to mitigate seismic risks.

Strong earthquakes occur frequently in Mainland China, where more than 500 earthquakes with magnitude above 6.0 have occurred within 28 provinces from 1900 to the present day. Moreover, the region of earthquake occurrence has increased in size, and events occurred with greater intensity and frequency, meaning that the period of active earthquakes has arrived. These earthquakes destroy buildings and cause severe losses of life and property. For example, more than eighty thousand deaths or disappearances occurred in the 2008 8.0 Wenchuan earthquake, which was a major tragedy for the Chinese people. Therefore, it is of great significance to seek an approach through scientific research to mitigate the potential threats associated with earthquake hazards.

In this paper, the P wave displacement parameter method, which uses data from a single station, is then proposed. We additionally select the representative period and the amplitude parameters, and we compare the relationships between each of these 3 parameters and the magnitude of the aftershocks of the 2008 8.0 Wenchuan earthquake. In addition, magnitude estimation formulas are derived and the accuracy of these formulas is explored by comparing the estimated magnitude with the catalog magnitude. Furthermore, we investigate to what degree the initial parameters indicate the Wenchuan mainshock magnitude and analyze the relationships among the expanding time window, the proposed parameter, and the estimated magnitude. With the established vulnerability relationships between magnitude and building damage, by quickly predicting the early structural antiseismic capacity from the estimated magnitude, this paper can provide a scientific basis for the establishment of a more reasonable and accurate resilience assessment system for structures.

#### 2. Dataset

A robust input database is essential in identifying reliable regression functions for magnitude estimation using a statistical approach. According to the China Strong Motion Net Centre (CSMNC), after the 8.0 Wenchuan earthquake on May 12, 2008, a total of 383 aftershocks were recorded by the end of 30 September 2008. These events were generated over a rupture length of approximately 300 km with focal depths ranging from 2 to 20 km, and the records were obtained by strong motion seismographs with a dynamic range of ±2 g, which were mainly installed at free-field sites. The sampling rate was 200 sps.

In this study, we select the mainshock and 43 aftershocks of the 2008 8.0 Wenchuan earthquake. As criteria, we specify that the selected earthquakes should have magnitude greater than and hypocentral distances less than 150 km. Furthermore, each selected event is required to have at least three records to ensure good station coverage and avoid the path effects. Thus, we use a total of 306 acceleration waveforms from the selected events, which demonstrate a large range of focal depths and mechanisms varying from thrust to strike slip.

Since correct picking of P arrivals and ensuring the exclusion of S waves from the analysis are prerequisites for accurately calculating the characteristic parameters, we use the three-step P phase detection method proposed by Wang and Zhao [5], and we double-check the arrival time via manual inspection for each waveform. After performing baseline error correction for the acceleration records, the signals are integrated to velocity records. Moreover, the velocity records are integrated to displacement records, which are required to calculate the parameter values. Then, for real-time applications, a high-pass recursive Butterworth filter with a cutoff frequency of 0.075 Hz is applied to the vertical components to remove the long-period drift that occurs after integration [6].

#### 3. Source Parameter Estimation

##### 3.1. Magnitude Estimation Method

The proposed P wave displacement parameter is defined as the integral of the squared high-pass filtered displacement of the vertical component ground motion, which ranges from 0 to 3 seconds of the initial P wave. The definition of shows that it is an integral of the squared displacement within the selected time window, which can reflect information with different periods carried by an advancing rupture on a fault plane. As a magnitude estimator, is a physically fundamental and source-dependent property. Nielsen [7] concluded that the flow rate of elastic energy controls earthquake fracture development and propagation. Fractures with higher initial energy are more likely to continue propagating over long distances and grow into earthquakes with large magnitude.

To verify the validity of the proposed method, we select the two characteristic parameters that have so far proved to be the most robust, namely, and [8, 9], for comparison. To correct the calculated and values for the effects of distance, we normalize them to a reference distance of 80 km, which is the average of the hypocentral distances from the analyzed data set, as the way followed by Zollo et al. [10] and Festa et al. [11]. The final values of and are referred to as and , and they can be obtained after preliminary locations are available. Such locations can be determined using real-time procedures and data from a single station; for example, see the methods proposed by Odaka et al. [12] and Horiuchi et al. [13]. We average the characteristic parameters , , and from the multiple observation records associated with each event and assume a linear regression model between the catalog magnitude and the averaged parameters as , where* A* and* B* are constants that are to be determined from the regression analysis.

##### 3.2. Relationships between Magnitude and , , and

Using the current analytical form, the resulting best-fitting regression relationship between and the magnitude is given by

The individual data points and the average values of as a function of magnitude are shown in Figure 1(a), where SDV means the standard deviation and* R* stands for the correlation coefficient (similarly hereinafter). The logarithm of the P wave displacement parameter shows a striking linear correlation with earthquake magnitude within the magnitude range considered .