The Effect of Soil Grain Size on Ground Surface Acceleration During a Shaking Event

Don Wason Dover High School (d.wason@dover.k12.nh.us)

Abstract: Earthquakes occur worldwide and can often be related to global tectonic processes. Ground shaking during an earthquake, which is the primary cause of property damage and the resulting loss of life, is controlled by the earthquake’s magnitude, the distance to the epicenter and the characteristics of the bedrock or soil at the surface. Understanding how different types of soil respond to ground shaking is essential for scientists and engineers seeking to mitigate potential earthquake damage at specific locations. Ground surface acceleration was measured on four dry soils of different grain size using a shake table moving through a sine wave frequency sweep from 0.01 to 5 Hz with a magnitude of 0.3 inches. When considering the complete frequency scan, root mean square (RMS) acceleration normalized to the shake table indicates that each soil experienced more overall acceleration than the shake table itself, and that the finer grained soils showed greater acceleration than the coarser soils. This general pattern, however, was not consistent at all frequencies. At the highest frequencies, the finer-grained soils lost their ability to support the surface accelerometer which began to move independently and sink below the surface. Ground surface motion during shaking is clearly complex and can not be predicted by soil grain size alone.

Introduction: Earthquakes have occurred on every continent and regularly occur in specific seismic belts (Figure 1) associated with tectonic activity at the lithospheric plate boundaries ( Tarbuck and Lutgens, 2006). Hundreds

Results and Discussion: Figure 5 is an acceleration vs. time plot for the sine wave frequency sweep showing the surface acceleration for each of the four soils and the shake table (the two shake table traces record shake table acceleration for each shake run). Root mean square (RMS) acceleration values for each soil type normalized to the shake table for that run (Table 1) indicate that the Top Soil showed the greatest overall surface motion, followed by the Fine Sand, the Coarse Sand, with the Sand-Gravel Mix showing the least surface motion. All four soils showed greater RMS acceleration than the shake table itself.

Results and Discussion (continued):

Conclusions: The results of this investigation suggests several conclusions: 1. When considering the complete frequency scan acceleration data, all soils tested showed higher RMS acceleration than the shake table, with acceleration inversely proportional to grain size. At specific frequencies, however, certain soils showed acceleration that was lower than that of the shake table. 2. At the very highest frequencies the finer grained soil’s surfaces became unstable. 3. The dynamics of soil surface acceleration during shaking is complex and is influenced by soil grain size as well as other factors including motion frequency. It is these “other factors” that warrant further investigation.

Works Cited:Kramer, S. L., 1996, Geotechnical Earthquake Engineering, Upper Saddle River: Pearson Prentice-Hall, Inc., 653p.

Plafker, G., and Galloway, J. P., eds., 1989, Lessons Learned from the Loma Prieta, California, Earthquake of October 17, 1989. U.S. Geological Survey Circular 1045, Washington: United States Government Printing Office, 48p.

Tarbuck, E.J., and Lutgens, F.K., 2006, Earth Science, 11th. Edition, Upper Saddle River: Pearson Prentice-Hall, Inc., 726p.Acknowledgements: This research was supported with funding from the National Science Foundation Research Experience for Teachers in Engineering grant (ENG-1132648). Thanks goes to: Dr. Tat Fu, who guided this research experience, coached me as I grappled with the data, and provided access to the UNH shake table and other resources in the Department of Civil Engineering at UNH. Rui Zhang, who worked with me day to day, advised me on the design of this investigation, ran the shake table and the associated computer equipment, taught me how to analyze and refine the data, and graciously put up with this strange geologist and high school teacher invading his research time and space. Dr. Majid Ghayoomi, who provided welcome advice and access to references and equipment.Special thanks goes to: Dr. Stephen Hale and the Leitzel Center Staff, who provided funding and support for this research experience.

Figure 1: Distribution of earthquake epicenters with magnitudes of greater than or equal to 5.0 over a ten year period. (Data from National Geophysical Data Center/NOAA) (From figure 7.11 of Tarbuck and Lutgens, 2006)

of millions of people, worldwide, live in these regions where their property and their very lives are at risk from earthquakes. When an earthquake occurs, stored energy is released in the form of seismic waves that radiate outward from the focus and travel through the earth, eventually reaching the surface. These seismic waves cause the earth’s surface to shake for seconds or even minutes. The extent of this shaking, in strength and duration, at any specific location, is controlled by the magnitude of the earthquake, the distance from the epicenter, and the surface characteristics of this location. It has been recognized for almost two centuries that one of the

most important site characteristics controlling seismic shaking is the type of bedrock or soil that makes up the surface. Soil will change the effect of the seismic waves, in general, by attenuating motion at certain frequencies and amplifying motion at other frequencies (Kramer, 1996). For Example, damage occurring during the 1989 Loma Prieta Earthquake in California’s Bay Area was greatest (MM IX) in regions of San Francisco and Oakland that were underlain by bay mud, alluvium, and especially loose sediment dumped into the Bay to create “buildable” land as these cities grew. These cities are more that sixty miles north of the epicenter which was located just east of Santa Cruz, a city which experienced less damage (MM VIII) because of ground characterized by more seismically resistant soils and bedrock (Plafker and Galloway, 1989). This relationship between earthquake damage and the type of soil or bedrock at a specific location provides the impetus for the following investigation. The purpose of this investigation, therefore, is to measure the surface acceleration of soils with different grain size distributions while being shaken by the Shake Table housed in the Civil Engineering Department at the University of New Hampshire.

Figure 2. Soils with different grain size distributions used in this investigation. These include: A: Top Soil, B: Fine Sand, C: Coarse Sand, and D: Sand-Gravel Mix. (Pencil is 14.5 cm)

Four soil samples, with different grain size distributions, were created for shake table testing, these include Top Soil (Figure 2A), Fine Sand (Figure 2B), Coarse Sand (Figure 2C), and a Sand-Gravel Mix (Figure 2D). The grain size distribution of each of these soils was characterized through standard soil sieve analysis and is summarized by Figure 3. Although each soil contains a wide range of particle sizes, there is a general trend of increasing average grain size from the Top Soil (0.365 mm), to Fine Sand (0.455 mm), to Coarse Sand (0.761 mm), to the Sand-Gravel Mix (3.387 mm).

Figure 3: Grain size distribution for each of the four soils used in this investigation .

Experimental Design: Soil Surface Acceleration

Figure 4: Shake Table setup for measuring acceleration at the soil’s surface. Accelerometers are placed on the surface of each soil sample as well as taped to the outside of each soil container and directly to the shake table platform.

Approximately one hundred pounds of each variety of dry soil was placed in a large plastic storage container (16”W X 25”L X 11”H). Soil samples, in their containers, were clamped to the shake table with the container’s long axis parallel to the direction of shake table motion. Soils were tested two at a time due to shake table size constraints as shown in Figure 4. The shake table was set up initially to run a sine wave frequency sweep from 0.01 to 5.00 Hz with a magnitude of 0.3 inches. Five accelerometers were simultaneously measuring the resulting acceleration of the surface of the two soil samples, the two sample containers, and the shake table itself. Two runs were required to measure the surface acceleration of all four soil types.

Soil Type RMS Acceleration(normalized to shake table)

Top Soil 1.523

Fine Sand 1.164

Coarse Sand 1.148

Sand-Gravel Mix 1.103

Figure 5: Acceleration vs. time plot for the complete sine wave frequency sweep showing the surface acceleration for each of the four soils and the shake table.

Table 1: Root mean square acceleration for each of the four soil types normalized to the shake table for the complete frequency sweep in Figure 5 above.

Figure 6: This graph shows the overall effect of soil grain size on measured surface acceleration during the complete sine wave frequency sweep.

A consideration of the acceleration data produced over the complete frequency sweep (0.01 – 5.0 Hz) suggests the relationship illustrated by Figure 6, where finer-grained soils experience greater acceleration. The reality is however, morecomplex. Visual examination of Figure 5 shows portions of the frequency sweep (e.g. 50 to 55 sec.) where the shake table acceleration is greater than that of some or perhaps all of the soils except the Top Soil. Figure 7 shows an expanded view of the middle portion of the acceleration vs. time plot for the sine wave frequency sweep (Time = 30 to 45 sec).

Figure 7: Acceleration vs. time plot for the middle section of the sine wave frequency sweep showing the surface acceleration for each of the four soils and the shake table. This is a subset of Figure 5 above and represents the time range of 30 to 45 seconds of the complete sweep.

Soil Type RMS Acceleration(normalized to shake table)

Top Soil 1.470

Fine Sand 1.143

Coarse Sand 1.179

Sand-Gravel Mix 1.182Table 2: Root mean square acceleration for each of the four soil types normalized to the shake table for the 30-45 sec. region of the frequency sweep shown in Figure 5.

Figure 8: This graph shows the effect of soil grain size on measured surface acceleration during the middle section of the frequency sweep shown in Figure 5.

The middle section of the frequency sweep shows a different pattern in the measured acceleration at the soil surface. Allfour soils continue to show greater acceleration than the shake table itself, and the Top Soil continues to show the greatest acceleration of the four soils. The other three soil samples however, show increasing acceleration in reverse order compared to the complete scan (Table 2). Figure 8 shows the relationship between soil grain size and acceleration for the middle section of the frequency sweep. Although the trend line is similar to that shown in Figure 6, the normalized RMS acceleration has changed for the three coarser soils. Figure 9 shows an expanded view of the end portion of the acceleration vs. time plot for the sine wave frequency sweep (Time = 70 to 100 sec). This section, at the highest frequency motion, shows several irregularities. The Top Soil’s acceleration trace is no longer centered over zero on the acceleration axis. This is probably due to the Top Soil accelerometer beginning to move independently over the Top Soil surface. Figure 10 shows the final position of the accelerometer after the shaking stopped. Not only was it no longer parallel to the axis of shake table motion and the Top Soil container, but it was starting to sink below the Top Soil surface in what resembled liquefaction. This also happened with the Fine Sand to a smaller degree during the first run. Accelerometers placed on the containers indicated that during this last portion of the frequency sweep the containers themselves began moving independently of the shake table. In this situation the Top Soil surface accelerometer was perhaps recording a motion vector that had greater acceleration than it would have if it had not shifted. While this suggests that Top Soil acceleration indicated by the complete frequency sweep is skewed toward higher values, a qualitative look at the surface acceleration prior this region shows the Top Soil acceleration to be consistently greater than that of the other soils.

Figure 9: Acceleration vs. time plot for the end of the sine wave frequency sweep showing the surface acceleration for each of the four soils and the shake table. This is a subset of Figure 5 above and represents the time range of 70 to 100 seconds of the complete sweep.

Figure 10: Top Soil and sample container at the end of the sine wave frequency sweep showing the misaligned and partially buried accelerometer. Note initial orientation in Figure 4.

See Figure 7

See Figure 9

A B C D

Experimental Design: Soil