WEBVTT Kind: captions Language: en 00:00:02.000 --> 00:00:05.260 [ Silence ] 00:00:06.700 --> 00:00:08.760 Okay, good morning, folks. 00:00:08.760 --> 00:00:12.310 Welcome to the Earthquake Science Center seminar. 00:00:12.310 --> 00:00:14.910 So today, it’s my pleasure to introduce John McCartney. 00:00:14.910 --> 00:00:20.070 Before we get started, I’ll just put in a quick plug for next week’s talk. 00:00:20.070 --> 00:00:24.160 We have Ashley Streig from Portland State University. 00:00:24.160 --> 00:00:27.540 And the title of her talk is going to be, Paleoseismology 00:00:27.540 --> 00:00:30.880 and Earthquake Chronology for the Santa Cruz Mountains. 00:00:30.880 --> 00:00:33.200 So that should be pretty interesting. 00:00:33.219 --> 00:00:35.910 Like I said, today we have professor John McCartney 00:00:35.910 --> 00:00:40.340 visiting us from the University of California-San Diego. 00:00:40.340 --> 00:00:43.180 I’ve had the pleasure of knowing John for quite a few years now. 00:00:43.180 --> 00:00:45.570 He was actually my undergraduate adviser 00:00:45.570 --> 00:00:48.960 when I was studying civil engineering back at Boulder. 00:00:50.200 --> 00:00:57.920 John’s a geotechnical engineer with a specialty in unsaturated soil mechanics. 00:00:57.920 --> 00:00:59.900 And that’s what he’ll be talking about today. 00:00:59.900 --> 00:01:04.960 He received his undergraduate and master’s degree in civil engineering 00:01:04.960 --> 00:01:08.020 from the University of Colorado and then went on to get 00:01:08.020 --> 00:01:11.160 his Ph.D. from UT-Austin. 00:01:11.160 --> 00:01:13.880 With that, John, I’ll let you take it away. Thanks. 00:01:13.880 --> 00:01:16.540 - All right. Thanks, Jack. 00:01:16.540 --> 00:01:20.780 So I kind of had a little bit of a challenge in deciding what topic 00:01:20.790 --> 00:01:24.700 to present today when Jack invited me to come up here. 00:01:24.700 --> 00:01:27.900 But over the past 10 years or so, I’ve been doing, you know, 00:01:27.900 --> 00:01:33.319 a little bit of work – mostly I’m funded focusing on the behavior of unsaturated 00:01:33.320 --> 00:01:38.560 soils, either during just dynamic loading or during earthquake response. 00:01:39.520 --> 00:01:42.000 And recently, we’ve had the opportunity to be able to apply 00:01:42.000 --> 00:01:49.299 some of this to the behavior of some mechanically stabilized Earth walls used 00:01:49.299 --> 00:01:53.920 as bridge abutments, and I’ll show that at the very end of the presentation today. 00:01:54.820 --> 00:01:57.079 But I also work on some other topics, so I wanted to give you a 00:01:57.080 --> 00:02:01.660 quick overview of some of the different work things that I work on. 00:02:01.660 --> 00:02:06.819 And I also decided to give a quick overview of some of 00:02:06.820 --> 00:02:10.580 the things that we’re working on as a group down at UCSD. 00:02:10.580 --> 00:02:13.740 Our department there is the Department of Structural Engineering, 00:02:13.740 --> 00:02:20.849 and I’m a geotechnical engineer. So the outside perception of 00:02:20.849 --> 00:02:26.360 the department maybe doesn’t – isn’t well-described. 00:02:26.360 --> 00:02:30.040 So I just wanted to give you some – a few ideas about some of the things 00:02:30.040 --> 00:02:33.569 that we work on in the geotech group there. 00:02:33.569 --> 00:02:36.299 Then we’ll go through the research motivation for why we’re trying to 00:02:36.299 --> 00:02:40.120 look at the behavior of unsaturated soils during earthquakes. 00:02:40.120 --> 00:02:43.730 And then there’s going to be two kind of main topics that I’ll talk about. 00:02:43.730 --> 00:02:48.140 The first is just characterizing the properties of unsaturated soils. 00:02:49.060 --> 00:02:52.780 There’s an interesting behavior of unsaturated soils. 00:02:52.780 --> 00:02:56.690 The amount of water in the soil is going to affect the stress state in the soil, 00:02:56.690 --> 00:03:01.220 and that’s going to be able to change your shear modulus and your damping. 00:03:01.220 --> 00:03:03.340 And there’s going to be some interesting behavior if you have 00:03:03.340 --> 00:03:08.140 wetting and drying of soils that we’ll see in the results there. 00:03:08.760 --> 00:03:11.860 And once you have a good idea of the dynamic properties, the next thing is to 00:03:11.860 --> 00:03:16.780 try to predict the settlements that we’re going to have during earthquakes. 00:03:16.780 --> 00:03:20.859 And we lump the deformation response of unsaturated soils during 00:03:20.859 --> 00:03:24.180 earthquakes into the term of “seismic compression.” 00:03:24.180 --> 00:03:28.189 And I’ll present today two different models that we’ve been working on. 00:03:28.189 --> 00:03:32.470 The first is an empirical model where we’ve taken some trends 00:03:32.470 --> 00:03:36.000 from the literature to put together into a workable model, 00:03:36.000 --> 00:03:39.090 but it’s kind of a Frankenstein approach. 00:03:39.090 --> 00:03:43.230 And it works, and we validated that with some centrifuge tests. 00:03:43.230 --> 00:03:48.810 But it maybe isn’t the final model that we can use in design. 00:03:48.810 --> 00:03:51.739 So we’ve also done a second model where we tried to 00:03:51.739 --> 00:03:56.030 extend the UBCSand model to unsaturated soils. 00:03:56.030 --> 00:03:58.540 And there’s still a lot of things to do there, but I’ll show you some of the 00:03:58.540 --> 00:04:02.100 things that we worked on so far and then how we’ve tried to calibrate that. 00:04:02.100 --> 00:04:06.439 And then finally, as I mentioned, recently I’ve been working on 00:04:06.439 --> 00:04:11.010 a Caltrans project focusing on the seismic response of mechanically 00:04:11.010 --> 00:04:14.900 stabilized Earth bridge abutments. So I’ll show some pictures of that and 00:04:14.900 --> 00:04:18.850 show how some of the different research we’ve worked on seismic compression 00:04:18.850 --> 00:04:22.840 could apply to the understanding of the behavior of those systems. 00:04:23.960 --> 00:04:28.380 So, as Jack mentioned, I did my undergrad and master’s in Colorado, 00:04:28.380 --> 00:04:31.230 went to University of Texas-Austin for my Ph.D., 00:04:31.230 --> 00:04:34.070 then I moved to University of Arkansas for a year, 00:04:34.070 --> 00:04:38.400 then came back to Colorado and taught at University of Colorado. 00:04:38.400 --> 00:04:40.080 And that’s where I met Jack. 00:04:40.080 --> 00:04:43.090 He was in my undergraduate foundations class. 00:04:43.090 --> 00:04:47.390 And then in 2014, I moved down to University of California-San Diego. 00:04:47.390 --> 00:04:51.670 So this is a nice – this is a geological map of the country, but it’s also interesting to 00:04:51.670 --> 00:04:58.380 see the – this is my first university with a non-large-land-animal mascot. 00:04:58.380 --> 00:05:02.120 So they’re the Tritons down in UC-San Diego. 00:05:02.670 --> 00:05:05.780 So the things that I work – on mainly I focus on material 00:05:05.790 --> 00:05:10.730 characterization on unsaturated soil mechanics, looking at the effective stress 00:05:10.730 --> 00:05:15.480 and different variables that will change the yield stress of these soils. 00:05:16.260 --> 00:05:20.300 And I’ve been working a lot recently on the thermal behavior of soils. 00:05:20.310 --> 00:05:23.160 My other main research area is on shallow geothermal heat exchange 00:05:23.160 --> 00:05:27.070 and putting that into civil engineering infrastructure. 00:05:27.070 --> 00:05:29.410 But I also work with foundation engineering, 00:05:29.410 --> 00:05:33.340 mainly focusing on full-scale testing and centrifuge testing. 00:05:33.340 --> 00:05:37.710 And the topic that I’m going to focus on today – I decided to 00:05:37.710 --> 00:05:40.520 choose an earthquake engineering topic – 00:05:40.520 --> 00:05:46.520 this seismic response of unsaturated soils and geosynthetic reinforced structures. 00:05:48.560 --> 00:05:52.500 These are the geotechnical faculty at University of California-San Diego. 00:05:53.820 --> 00:05:55.720 Professor Elgamal, Professor Hutchinson, 00:05:55.720 --> 00:05:59.280 and Professor Luco were all there before I came. 00:05:59.280 --> 00:06:05.160 And then myself and Professor Tomac are the two junior faculty in the group. 00:06:07.400 --> 00:06:09.500 So University of California-San Diego 00:06:09.500 --> 00:06:12.640 is mostly famous for this outdoor shaking table. 00:06:12.640 --> 00:06:16.740 It’s a great facility for testing the earthquake response 00:06:16.740 --> 00:06:19.630 of structures and soil structures. 00:06:19.630 --> 00:06:22.990 We have many different containers that we can use to test soils on this. 00:06:22.990 --> 00:06:25.120 This is one laminar container. 00:06:25.120 --> 00:06:29.860 It’s good for testing both piles and buried structures like tunnels. 00:06:29.870 --> 00:06:33.460 We also have a rigid container if you’re wanting to test a plane strain structure 00:06:33.460 --> 00:06:38.800 like a geosynthetic reinforced wall, which is being shown up here. 00:06:40.700 --> 00:06:44.140 And one of the good side effects of when they installed the system 00:06:44.150 --> 00:06:47.650 is they had to make a very large excavation, so we now have 00:06:47.650 --> 00:06:52.550 a pit where we can test – we can fill this with different types of soils, and we 00:06:52.550 --> 00:06:57.760 have a reaction wall, and we can do different tests on piles from the surface. 00:06:58.660 --> 00:07:02.320 And most recently, I built a heat storage system. 00:07:02.330 --> 00:07:04.830 So this is the shaking table in the background there, 00:07:04.830 --> 00:07:06.880 and we’re collecting heat from solar thermal panels 00:07:06.880 --> 00:07:12.550 and injecting that into the ground as modes of heat storage. 00:07:12.550 --> 00:07:16.180 And then, on campus, we have another smaller shaking table, 00:07:16.180 --> 00:07:20.080 but it’s still big enough to do a lot of different types of testing. 00:07:21.040 --> 00:07:23.880 We have another laminar container that can go on there. 00:07:23.880 --> 00:07:27.280 The tests that I’ll show at the very end on the mechanically stabilized 00:07:27.280 --> 00:07:30.960 Earth walls we also performed on this smaller shaking table. 00:07:30.960 --> 00:07:33.480 We have a nice centrifuge. 00:07:33.490 --> 00:07:37.140 So this – in this case, we can test small-scale models of geotechnical 00:07:37.140 --> 00:07:42.460 structures under elevated gravity loads and apply some scaling. 00:07:42.460 --> 00:07:47.390 We have some different containers to test clay soils and rigid containers. 00:07:47.390 --> 00:07:50.640 We also have a small shaking table that we can put on there as well. 00:07:50.640 --> 00:07:55.680 So we’ve all updated this in the past three years. 00:07:55.690 --> 00:07:58.060 And then we have just a standard geotechnical testing laboratory 00:07:58.060 --> 00:08:03.000 with some triaxial tests and some different pullout tests 00:08:03.000 --> 00:08:05.950 and direct shear tests for different types of materials. 00:08:05.950 --> 00:08:10.900 This is a project we’re working on for the shear strength of tire chips. 00:08:10.900 --> 00:08:14.460 And I’ll talk about this testing cell later. 00:08:14.460 --> 00:08:18.620 It’s a cyclic simple shear test that we’ve adapted to test on saturated soils. 00:08:19.280 --> 00:08:23.560 Okay, so now to focus on the main topic of the presentation. 00:08:23.560 --> 00:08:26.000 I wanted to acknowledge the three students that have worked on 00:08:26.000 --> 00:08:29.810 this topic – two previous students from University of Colorado – 00:08:29.810 --> 00:08:33.800 Ali and Majid – worked on dynamic properties of unsaturated soils 00:08:33.800 --> 00:08:37.320 and then this empirical model for the seismic compression. 00:08:37.320 --> 00:08:42.760 And my student, Wenyong, is continuing their work at UCSD. 00:08:42.760 --> 00:08:49.220 So the first step before we go into this topic – most people think 00:08:49.220 --> 00:08:56.740 when you’re worrying about the response of soils to an earthquake, 00:08:56.740 --> 00:08:58.220 we want to look at the worst-case scenario. 00:08:58.220 --> 00:09:02.430 And the worst-case scenario is typically liquefiable soils, where you’re going to 00:09:02.430 --> 00:09:08.360 get a large amount of movement, and potentially settlement, potentially 00:09:08.360 --> 00:09:12.500 bearing capacity problems of your structures that are on top of that soil. 00:09:12.500 --> 00:09:16.930 Unsaturated soils, by definition, have air and water in the pores. 00:09:16.930 --> 00:09:21.400 So they’re going to be a little bit more challenging to understand. 00:09:21.400 --> 00:09:25.390 But if you look at the climate map of California, most of the state 00:09:25.390 --> 00:09:29.300 is probably going to have unsaturated soil layers 00:09:29.300 --> 00:09:33.520 unless you’re somewhere near the coast or up in the northern area. 00:09:33.520 --> 00:09:35.180 So this means that the soils that are going to be there are 00:09:35.180 --> 00:09:41.060 probably going to be unsaturated. So if we do the designs and the site 00:09:41.060 --> 00:09:44.960 response analyses based on saturated conditions or dry conditions, 00:09:44.960 --> 00:09:48.880 we may not be able to accurately capture the behavior of these materials. 00:09:50.180 --> 00:09:55.280 There are other conditions where we design a geotechnical system on the 00:09:55.280 --> 00:09:59.380 ground surface and we want the soil to remain in unsaturated conditions. 00:09:59.380 --> 00:10:05.790 So that’s another case where we’d want to apply this sort of research. 00:10:05.790 --> 00:10:07.760 And then there’s other – some interesting issues that we’re 00:10:07.760 --> 00:10:12.590 going to see in this because dry soils typically collapse 00:10:12.590 --> 00:10:16.560 during earthquake shaking, and saturated soils will experience liquefaction, 00:10:16.560 --> 00:10:20.940 and unsaturated soils are going to be somewhere in between those two states. 00:10:23.170 --> 00:10:26.660 So these are just some examples of geotechnical systems that we design 00:10:26.660 --> 00:10:31.410 with backfill that’s meant to be in unsaturated conditions. 00:10:31.410 --> 00:10:34.280 So mechanically stabilized Earth walls – these are layers of soil 00:10:34.280 --> 00:10:40.029 with reinforcements to add – to create a composite material. 00:10:40.029 --> 00:10:44.640 But also just road embankments and rail embankments – 00:10:44.640 --> 00:10:48.200 they’re designing the new high-speed rail through southern California 00:10:48.210 --> 00:10:51.930 right now that are all going to be on embankments of compacted soil. 00:10:51.930 --> 00:10:55.820 And small movements of the soil could have big impacts on the 00:10:55.820 --> 00:10:59.800 overlying structures, so we want to have an accurate understanding 00:10:59.800 --> 00:11:03.540 of the compression that we could have during earthquakes. 00:11:06.230 --> 00:11:09.300 So there’s been many studies on the seismic-induced settlement of 00:11:09.300 --> 00:11:15.340 free-field soil layers since the 1970s. However most of these methodologies 00:11:15.340 --> 00:11:19.550 focused on either dry soil or water-saturated soil because these 00:11:19.550 --> 00:11:21.990 were believed to be the two worst-case scenarios 00:11:21.990 --> 00:11:26.570 from the perspectives of the void collapse or liquefaction. 00:11:26.570 --> 00:11:29.990 But that left a gap in the understanding of soils in that 00:11:29.990 --> 00:11:34.340 intermediate degree of saturation range where there may be a mix 00:11:34.340 --> 00:11:39.100 of behavior between dry and water-saturated soil behavior. 00:11:40.740 --> 00:11:46.560 So from a basic perspective, you could think that seismic loading 00:11:46.560 --> 00:11:51.180 of unsaturated soils is going to lead to a partial drainage condition. 00:11:51.180 --> 00:11:55.040 So if you have a porous material down here where there are 00:11:55.040 --> 00:11:58.360 water-filled voids and air-filled voids, and the water is being held by 00:11:58.360 --> 00:12:04.150 capillarity between the different pores, shaking of that system is going to lead 00:12:04.150 --> 00:12:07.800 to both a partial drainage condition – 00:12:07.800 --> 00:12:11.110 so we could have some drained mechanisms where those 00:12:11.110 --> 00:12:15.380 air-filled voids – when you have shaking, they’re going to collapse, potentially, 00:12:15.380 --> 00:12:19.260 and the air is going to escape depending on the degree of saturation. 00:12:19.260 --> 00:12:21.320 But you could also have an undrained mechanism where 00:12:21.320 --> 00:12:25.520 some of these water-filled pores – the water is going to be pressurized, 00:12:25.520 --> 00:12:30.080 and it’s going to lead to an increase in water pressure and flow, 00:12:30.080 --> 00:12:32.660 and the dissipation of those pore pressures may be a little bit 00:12:32.660 --> 00:12:36.630 different than in saturated soils as well. 00:12:36.630 --> 00:12:39.790 So we have a mix of drained and undrained behavior. 00:12:39.790 --> 00:12:45.670 And then also the presence of that water in – the capillary water 00:12:45.670 --> 00:12:49.210 between the pores is going to lead to a change in the stress state. 00:12:49.210 --> 00:12:53.160 So that matric suction, or capillary pressure, that’s going to be occurring, 00:12:53.160 --> 00:12:55.290 it’s going to change the inter-particle stresses, 00:12:55.290 --> 00:12:58.980 and that may resist the compression of the air-filled voids. 00:12:58.980 --> 00:13:01.380 So there’s a lot of other interesting things that are going to be happening 00:13:01.380 --> 00:13:06.440 in unsaturated soils that we don’t see in dry or saturated materials. 00:13:08.440 --> 00:13:10.190 So one thing that’s going to happen is that 00:13:10.190 --> 00:13:12.860 the dynamic properties are going to change. 00:13:12.860 --> 00:13:14.960 It’s well-known that, if you change the outside 00:13:14.960 --> 00:13:19.410 confining stress on unsaturated soils, that you’re going to increase the 00:13:19.410 --> 00:13:22.690 shear modulus of the soil – make it stiffer. 00:13:22.690 --> 00:13:27.230 But if you increase the suction inside of the soil, you actually also lead – 00:13:27.230 --> 00:13:30.010 so this is the role of the external confining stress. 00:13:30.010 --> 00:13:32.480 It’s going to lead to an increasing behavior. 00:13:32.480 --> 00:13:34.000 But when you have partially saturated, 00:13:34.000 --> 00:13:37.720 it also leads to an increase in the stiffness of the soil. 00:13:37.720 --> 00:13:40.080 So that’s a positive thing. That means that unsaturated soils 00:13:40.080 --> 00:13:44.580 are probably going to be stiffer than saturated soils or dry soils. 00:13:45.550 --> 00:13:49.920 The other interesting thing is that the yield stress is going to change in the soils 00:13:49.920 --> 00:13:54.820 independently from the effects of matric suction on the effective stress. 00:13:54.820 --> 00:13:56.810 And that’s going to have some important behavior if you’re trying to 00:13:56.810 --> 00:14:00.410 look at the effects of wetting and drying during seasonal interaction 00:14:00.410 --> 00:14:05.100 between the atmosphere and the soil on the shear modulus. 00:14:05.100 --> 00:14:07.530 Another interesting thing with unsaturated soils is that their 00:14:07.530 --> 00:14:11.160 hydraulic conductivity is much lower than saturated soils. 00:14:11.160 --> 00:14:15.110 So that means that, if you do have pore pressure generation, the rate of water 00:14:15.110 --> 00:14:21.020 dissipation may be slower in unsaturated soils than in saturated soils. 00:14:23.000 --> 00:14:26.860 One challenge, if you wanted to apply unsaturated conditions in design, 00:14:26.870 --> 00:14:31.440 is to figure out what the actual unsaturated conditions at your 00:14:31.440 --> 00:14:36.290 particular site are going to be for a given atmospheric condition. 00:14:36.290 --> 00:14:39.271 The simplest case would be to say that you have a water table 00:14:39.271 --> 00:14:43.190 that’s fixed at a certain depth, and you have no flow conditions, 00:14:43.190 --> 00:14:45.440 which are highlighted in red here. 00:14:45.440 --> 00:14:50.580 In this case, the suction head is going to increase linearly with height. 00:14:50.580 --> 00:14:53.620 The suction head is going to be equal to the elevation head. 00:14:53.620 --> 00:14:58.480 And the water content profile is going to follow this particular shape here, which, 00:14:58.480 --> 00:15:01.710 as we’re going to see on the next slide, is related to the shape of the 00:15:01.710 --> 00:15:04.830 Soil-Water Retention Curve, which is telling you how much water 00:15:04.830 --> 00:15:08.720 is there in the soil for a given matric suction. 00:15:08.720 --> 00:15:12.370 But if we have infiltration from the surface, it’s going to lead to 00:15:12.370 --> 00:15:14.920 a shift in the suction profile. Or if you have evaporation 00:15:14.920 --> 00:15:18.890 from the surface, it’s going to lead to a shift in the water profile. 00:15:18.890 --> 00:15:21.480 So there’s a lot of uncertainty that’s going to be happening in the soil 00:15:21.480 --> 00:15:25.250 depending on the position of the water table at a given location 00:15:25.250 --> 00:15:28.540 and on the atmospheric interactions that are happening. 00:15:28.540 --> 00:15:32.580 So it’s not a simple case to say, like, this soil is unsaturated, and we’re 00:15:32.580 --> 00:15:38.020 going to do a particular location. Because the water content is probably 00:15:38.020 --> 00:15:41.620 also going to be varying with height above the water table. 00:15:43.690 --> 00:15:48.200 So the fundamental relationship that we try to use in all analyses 00:15:48.210 --> 00:15:52.050 of unsaturated soils is the Soil-Water Retention Curve. 00:15:52.050 --> 00:15:55.980 And this is going to change depending on the pore size distribution of the soils. 00:15:55.980 --> 00:16:01.100 Clays are going to be able to retain water to much higher suctions than sands. 00:16:01.100 --> 00:16:05.589 Sands, if you apply very low suction, is going to reach residual saturation 00:16:05.589 --> 00:16:09.400 under a very relatively low suction. 00:16:09.400 --> 00:16:15.589 And we also have different behavior when we dry the soil and re-wet it, 00:16:15.589 --> 00:16:18.130 and that is going to potentially lead to some challenges when we 00:16:18.130 --> 00:16:22.320 look at the effects of drying and wetting on the shear modulus. 00:16:22.320 --> 00:16:25.690 When you dry the soil and re-wet it, you’re going to trap some air bubbles 00:16:25.690 --> 00:16:29.220 in the soil, and you’re never going to be able to get those out. 00:16:29.220 --> 00:16:32.520 So that could lead to some changes in behavior. 00:16:34.029 --> 00:16:37.040 We can also try to link the shape of the Soil-Water Retention Curve 00:16:37.040 --> 00:16:39.520 together with the effective stress. 00:16:39.520 --> 00:16:42.480 And the model of Lu and Likos – 00:16:42.480 --> 00:16:46.480 Ning Lu from Colorado School of Mines – 00:16:46.480 --> 00:16:52.220 defined the mean effective stress as the net confining stress 00:16:52.220 --> 00:16:56.529 from outside plus some additional term called the suction stress. 00:16:56.529 --> 00:17:01.670 And the suction stress incorporates the effects of unsaturated conditions 00:17:01.670 --> 00:17:05.620 on the internal confining – on the mean effective stress. 00:17:05.620 --> 00:17:08.230 And there’s many techniques that you can use to define 00:17:08.230 --> 00:17:12.620 what that suction stress is. This is just one example showing 00:17:12.620 --> 00:17:16.400 how you can perform some different shear strength tests at different 00:17:16.419 --> 00:17:21.689 matric suctions, and back-calculate what the apparent tensile strength is, 00:17:21.689 --> 00:17:24.990 and then correlate that together with your suction, and then link 00:17:24.990 --> 00:17:27.370 all of these points together with a curve and say that 00:17:27.370 --> 00:17:30.230 that’s your relationship between the suction stress 00:17:30.230 --> 00:17:34.620 and the matrix suction – or the suction stress characteristic curve. 00:17:35.980 --> 00:17:41.420 But that’s a little bit problematic to try to define, so it’s much easier to 00:17:41.429 --> 00:17:44.700 take our Soil-Water Retention Curve, which is starting to become a 00:17:44.700 --> 00:17:48.360 standard measurement in unsaturated soil mechanics, and link the shape of 00:17:48.360 --> 00:17:54.070 that together with – to define this suction stress characteristic curve. 00:17:54.070 --> 00:18:00.759 So Ning Lu also defined this simple relationship for the suction stress here, 00:18:00.759 --> 00:18:04.100 where we just multiply our capillary pressure, or suction, 00:18:04.100 --> 00:18:06.179 together with the effective saturation. 00:18:06.179 --> 00:18:08.669 And the good thing with that is that most models for the 00:18:08.669 --> 00:18:12.330 Soil-Water Retention Curve are defined in terms of the effective saturation. 00:18:12.330 --> 00:18:16.590 So if we have the Soil-Water Retention Curve, we can put in 00:18:16.590 --> 00:18:20.440 this relationship for the Soil-Water Retention Curve into the 00:18:20.440 --> 00:18:24.320 effective saturation and predict what our suction stress is going to be. 00:18:24.320 --> 00:18:26.640 And the interesting thing that happens is that, if you have some 00:18:26.640 --> 00:18:31.470 certain parameters of the Soil-Water Retention Curve, you could have 00:18:31.470 --> 00:18:36.750 a increasing and decreasing trend, which is something that we see for sands. 00:18:36.750 --> 00:18:39.850 If you have completely dry sand, or if you have completely wet sand, 00:18:39.850 --> 00:18:41.390 it’s going to fall apart. 00:18:41.390 --> 00:18:44.600 But if you have sand with an intermediate degree of saturation, 00:18:44.600 --> 00:18:49.340 it’s going to have a good amount of inter-particle strength. 00:18:49.340 --> 00:18:52.710 Whereas, if you have a clay, and you dry it out, it’s going to 00:18:52.710 --> 00:18:57.460 eventually turn into a brick when you go to high suction values. 00:18:58.300 --> 00:19:00.950 So this is a nice model that it can show this difference 00:19:00.950 --> 00:19:04.039 in behavior that you can expect between sands and clays. 00:19:04.039 --> 00:19:05.379 And we can plot that either in terms of 00:19:05.380 --> 00:19:09.580 degree of saturation or in terms of the matric suction. 00:19:12.100 --> 00:19:15.019 So the other interesting thing that happens when you 00:19:15.019 --> 00:19:19.999 dry unsaturated soils – we know that the drying of unsaturated soils 00:19:20.000 --> 00:19:23.480 is going to change the effective stress through the suction stress. 00:19:23.480 --> 00:19:25.900 But it’s also going to change the yield stress. 00:19:25.900 --> 00:19:30.960 And yield stress is going to tell you your zone of elasticity in the soil. 00:19:30.960 --> 00:19:32.880 These are just some compression curves on some 00:19:32.880 --> 00:19:36.799 different unsaturated soils with different initial degrees of saturation. 00:19:36.799 --> 00:19:42.690 And you can see that the bend in the curve from initial elastic conditions to 00:19:42.690 --> 00:19:47.720 plastic conditions is going to increase as the degree of saturation decreases. 00:19:47.720 --> 00:19:51.489 So your soil is going to be – have a – have a wider elastic range 00:19:51.489 --> 00:19:54.789 as it becomes drier. But then if you continue to 00:19:54.789 --> 00:19:56.730 load out those unsaturated soils, 00:19:56.730 --> 00:20:01.120 the compression curves all converge with the saturated behavior. 00:20:01.120 --> 00:20:03.720 So you’re basically going to squish all the air bubbles out 00:20:03.720 --> 00:20:07.900 at high stresses, and your soil is going to become saturated. 00:20:08.920 --> 00:20:10.340 Sorry. 00:20:13.100 --> 00:20:18.400 The other challenge – so now that we know about the effective stress 00:20:18.400 --> 00:20:20.850 and pre-consolidation stress, the next thing is to look at the 00:20:20.850 --> 00:20:23.169 shear modulus because the shear modulus is the main parameter 00:20:23.169 --> 00:20:27.070 that’s going to be controlling the seismic deformation. 00:20:27.070 --> 00:20:32.409 So our main goal is to look at this small strain shear modulus because 00:20:32.409 --> 00:20:36.509 that’s the easiest to isolate the effects of the effective stress. 00:20:36.509 --> 00:20:39.509 But we also have to know that, if we have larger shear strain amplitudes, 00:20:39.509 --> 00:20:43.090 there’s going to be a reduction in the shape of this curve. 00:20:43.090 --> 00:20:45.020 And that’s actually going to be the zone where we’re seeing a lot 00:20:45.020 --> 00:20:50.549 of the seismic compression. So we want to know the G-max value 00:20:50.549 --> 00:20:54.900 because that’s going to be a anchor of this modulus reduction curve. 00:20:54.900 --> 00:20:56.659 But when we have large shaking events, 00:20:56.660 --> 00:21:00.320 we’re probably going to be on this higher shear strain zone. 00:21:02.130 --> 00:21:05.160 So looking at the effects of effective stress on the shear modulus, we can 00:21:05.169 --> 00:21:11.779 go back to some historic models from the ’60s for the shear modulus. 00:21:11.779 --> 00:21:14.159 And it basically says that the shear modulus is some 00:21:14.159 --> 00:21:17.470 power law function of the effective stress in the soil. 00:21:17.470 --> 00:21:23.640 And there’s several problems with this model – mainly that the parameters 00:21:23.640 --> 00:21:28.600 have some unknown units because the effective stress here isn’t normalized. 00:21:28.600 --> 00:21:31.049 It doesn’t really take into account the coupling 00:21:31.049 --> 00:21:33.909 between the effective stress and void ratio very clearly. 00:21:33.909 --> 00:21:38.109 And it doesn’t take into account the over consolidation ratio, 00:21:38.109 --> 00:21:42.840 which is basically your yield stress divided by your current effective stress. 00:21:44.029 --> 00:21:46.619 So we can’t really incorporate this independent effect of suction 00:21:46.619 --> 00:21:50.120 on the effective stress and yield stress using this model. 00:21:50.120 --> 00:21:56.369 But we can first try to put in this model of Lu and Likos 00:21:56.369 --> 00:22:00.929 for the suction stress in here for p-prime and see how it works. 00:22:00.929 --> 00:22:03.789 So we took some soils from the literature where we had the Soil-Water Retention 00:22:03.789 --> 00:22:08.220 Curve, predicted the suction stress characteristic curve to get our effective 00:22:08.220 --> 00:22:14.879 stress, and then we plotted the effective stress as a function of the shear modulus. 00:22:14.879 --> 00:22:20.559 And we found that the model provided a pretty good fit, but it – 00:22:20.559 --> 00:22:24.129 you had, at the end of the day, to fit the parameters for the model. 00:22:24.129 --> 00:22:28.119 So it’s good, in it – putting the effective stress in there works, but there’s 00:22:28.119 --> 00:22:32.509 still going to be some shortcomings that we’re going to see here. 00:22:32.509 --> 00:22:35.929 So we’ve – in 2012, we came up with a little bit more 00:22:35.929 --> 00:22:39.970 advanced model that had a better normalization. 00:22:39.970 --> 00:22:44.720 So the units start to become normalized out. 00:22:44.720 --> 00:22:48.880 We don’t have units on the A or N. And we have a new parameter here 00:22:48.889 --> 00:22:52.980 to take into account the effects of the over consolidation ratio. 00:22:52.980 --> 00:22:55.429 And, as I mentioned, the over consolidation ratio 00:22:55.429 --> 00:23:00.419 is the ratio of the yield stress divided by the current stress. 00:23:00.419 --> 00:23:03.489 So we can put this into this equation up here 00:23:03.489 --> 00:23:06.640 and have a new function for the shear modulus. 00:23:06.640 --> 00:23:11.220 And this is interesting because, as the soil dries, p-c is going to change, 00:23:11.230 --> 00:23:14.259 but p is – p-prime is also going to change. 00:23:14.260 --> 00:23:19.380 So there’s going to be some differences in behavior that you’re going to see. 00:23:21.200 --> 00:23:26.059 So when you dry the soil, your pre-consolidation stress 00:23:26.059 --> 00:23:30.679 is going to increase in this nonlinear form here. 00:23:30.679 --> 00:23:34.470 But when you re-wet the soil, it’s going to follow a different path. 00:23:34.470 --> 00:23:38.919 And this was a very nice way that we could try to explain some of the 00:23:38.919 --> 00:23:43.260 observations in the literature that I’ll show later by having this model here, 00:23:43.260 --> 00:23:50.389 which takes into account the hysteretic shape of the pre-consolidation stress. 00:23:50.389 --> 00:23:54.429 And this model here was developed together with the shape of the 00:23:54.429 --> 00:24:00.500 Soil-Water Retention Curve, which goes into this part of the equation here. 00:24:01.800 --> 00:24:03.560 So this is a little bit more complicated model. 00:24:03.560 --> 00:24:08.720 If you suddenly put in this relationship for the OCR, 00:24:08.720 --> 00:24:13.480 You end up with a very big equation with a lot of parameters, 00:24:13.489 --> 00:24:16.980 but you’re able to see this hysteretic behavior during drying 00:24:16.980 --> 00:24:20.100 and wetting of the small strain shear modulus. 00:24:21.120 --> 00:24:25.220 So we calibrated it with some data from the literature. 00:24:25.220 --> 00:24:27.799 Most soils only looked at – most studies only looked at 00:24:27.799 --> 00:24:32.070 the monotonic drying of soils, but there were a couple 00:24:32.070 --> 00:24:35.620 where people looked at both drying and then re-wetting. 00:24:35.620 --> 00:24:40.460 So we calibrated the model by trying to fit it to this set of data. 00:24:40.460 --> 00:24:45.180 And come up with some correlations for the different parameters. 00:24:45.190 --> 00:24:50.739 And then we wanted to validate the model for an independent set of soil. 00:24:50.739 --> 00:24:55.090 So we did some resonant column tests on unsaturated soil specimens where 00:24:55.090 --> 00:24:59.609 we used a flow pump to extract water to reach a certain degree of saturation and 00:24:59.609 --> 00:25:05.700 then applied a torsional vibration to the surface to measure the shear modulus. 00:25:05.700 --> 00:25:09.739 So we dry the soil out gradually, measure shear modulus continuously, 00:25:09.740 --> 00:25:15.740 and then re-wet the soil to have a hydraulic hysteresis loop. 00:25:15.740 --> 00:25:18.240 This is just the operation of the pump. 00:25:18.240 --> 00:25:21.399 And these are the results from the different tests. 00:25:21.399 --> 00:25:25.019 We saw a pretty good match between the shear modulus 00:25:25.019 --> 00:25:28.119 as a function of matric suction during wetting and drying 00:25:28.119 --> 00:25:33.539 or shear modulus and degree of saturation during drying and wetting. 00:25:33.540 --> 00:25:37.580 And then, if we put those together, we can define the effective stress 00:25:37.580 --> 00:25:41.179 relationship. And, different than the previous study before, 00:25:41.179 --> 00:25:43.729 that’s not necessarily going to be a one-to-one relationship, 00:25:43.729 --> 00:25:49.080 but it’s going to depend on your current wetting or drying path. 00:25:50.660 --> 00:25:55.259 And so that’s very complicated, so it’s not very useful to use in practice. 00:25:55.259 --> 00:25:59.450 So we’ve since developed some simpler models based on 00:25:59.450 --> 00:26:03.100 bender element tests where we’ve tried to more closely link the 00:26:03.100 --> 00:26:06.250 shape of the Soil-Water Retention Curve directly with some of the 00:26:06.250 --> 00:26:10.919 other parameters and the small strain shear modulus 00:26:10.920 --> 00:26:15.160 and got some good correlations for some of the sets of data. 00:26:15.160 --> 00:26:19.720 And most recently, we’ve come up with some relationships between – 00:26:19.720 --> 00:26:24.059 we’ve incorporated that small strain model into our shear modulus reduction 00:26:24.059 --> 00:26:27.759 curve and come up with some other correlations for the parameters of 00:26:27.759 --> 00:26:33.269 that model to get some good match – good matches between this model 00:26:33.269 --> 00:26:36.639 and some experimental data available in the literature. 00:26:36.640 --> 00:26:42.560 But there isn’t a lot of information on this particular condition available. 00:26:43.460 --> 00:26:45.340 And in the future, we need to look at the effects of 00:26:45.360 --> 00:26:48.700 unsaturated conditions on damping. There’s only been a few studies 00:26:48.700 --> 00:26:52.920 on logarithmic decay approaches to measure this variable. 00:26:54.020 --> 00:26:57.259 Okay, so talking quickly now about the empirical 00:26:57.259 --> 00:27:01.500 methodology for seismic compression. 00:27:01.500 --> 00:27:04.429 So this was a different student working on this topic. 00:27:04.429 --> 00:27:05.759 We had shaking from the surface, 00:27:05.760 --> 00:27:09.700 and then we treated a soil layer as an equivalent linear approach. 00:27:11.500 --> 00:27:16.900 And then we summed up the – we estimated the volumetric strains of each 00:27:16.900 --> 00:27:22.220 of the layers and then summed up the change in height of the entire soil layer. 00:27:23.509 --> 00:27:27.480 So first we looked at the seismic compression of dry sand. 00:27:27.480 --> 00:27:33.090 This was studied first by Seed and Silver, and they came up with an 00:27:33.090 --> 00:27:40.080 equivalent linear approach, which they – several people applied to dry sands. 00:27:40.080 --> 00:27:43.460 And, in most of the cases, there’s some empirical relationship 00:27:43.470 --> 00:27:46.679 between the number of cycles that you apply to the amount of 00:27:46.680 --> 00:27:49.700 volumetric strain you’re going to get in the soil. 00:27:51.429 --> 00:27:55.240 For saturated sands – so dry sands are going to settle mainly due to 00:27:55.249 --> 00:27:58.029 compression of the air voids, whereas saturated sands are 00:27:58.029 --> 00:28:01.419 mainly going to settle due to consolidation associated with 00:28:01.420 --> 00:28:05.400 the dissipation of the shear induced excess pore water pressures. 00:28:06.900 --> 00:28:13.040 And, again, there were some empirical studies by Lee and Albaisa where, 00:28:13.049 --> 00:28:16.950 as the initial relative density changed, 00:28:16.950 --> 00:28:20.529 you’re going to have some different pore water pressure ratio generated. 00:28:20.529 --> 00:28:22.679 So the density is going to have a big effect on the 00:28:22.680 --> 00:28:25.900 post-liquefaction consolidation. 00:28:25.900 --> 00:28:32.139 So, for unsaturated soils, we decided to combine the observations from dry sands 00:28:32.139 --> 00:28:35.749 and saturated sands together using some scaling relationships for the 00:28:35.749 --> 00:28:39.050 degree of saturation, saying that our total volumetric strain 00:28:39.050 --> 00:28:41.299 is going to be that due to the compression of the air voids 00:28:41.300 --> 00:28:45.040 and then the consolidation of the water-filled voids. 00:28:47.000 --> 00:28:50.879 So looking first to the compression of the air-filled voids, 00:28:50.879 --> 00:28:54.970 we said that this is mainly a function of the effective strain in the soil, and the 00:28:54.970 --> 00:28:59.549 number of cycles, and then the initial relative density and degree of saturation. 00:28:59.549 --> 00:29:01.679 So we took the model of Tokimatsu and Seed 00:29:01.680 --> 00:29:07.240 and added a linear reduction factor for the degree of saturation. 00:29:07.240 --> 00:29:12.190 And there’s only a few sets of data available to really verify that, 00:29:12.190 --> 00:29:16.520 so we still need to do some additional tests to check that. 00:29:18.860 --> 00:29:22.610 And one other important thing. If you’re trying to figure out what 00:29:22.610 --> 00:29:26.539 the effective strain is, we need to know what the shear modulus is. 00:29:26.539 --> 00:29:29.739 So that brings us back to applying some of the different trends – 00:29:29.739 --> 00:29:34.190 relationships we had between the shear modulus and the mean effective stress. 00:29:34.190 --> 00:29:37.899 So, just to be simple, we started with one of the earlier models from 00:29:37.899 --> 00:29:43.409 Hardin instead of going with this more complicated one from [inaudible], 00:29:43.409 --> 00:29:45.669 but that shows that we have this nonlinear increase 00:29:45.669 --> 00:29:48.980 in shear modulus with mean effective stress. 00:29:50.760 --> 00:29:54.000 Then the next thing we need is our average shear stress 00:29:54.000 --> 00:29:55.879 and the soil induced by shaking. 00:29:55.879 --> 00:29:59.470 So we used some of the established models for this. 00:29:59.470 --> 00:30:04.499 And then we used the iteration approach to figure out what our consistent value 00:30:04.499 --> 00:30:07.919 of the shear modulus and shaking-induced shear strain is going to 00:30:07.919 --> 00:30:14.159 be to figure out what our effective shear strain in the soil is and put that 00:30:14.160 --> 00:30:19.420 back into our equation for the volumetric compression of the air-filled voids. 00:30:21.000 --> 00:30:23.409 So then the second part of the methodology is, you want to estimate 00:30:23.409 --> 00:30:29.999 what the volumetric strain is from consolidation after liquefaction. 00:30:29.999 --> 00:30:32.809 So similar to saturated soils, it’s going to be a function of 00:30:32.809 --> 00:30:37.700 the pore water pressure ratio, density, and overburden stress. 00:30:37.700 --> 00:30:41.580 But also we’re adding the degree of saturation onto this. 00:30:42.560 --> 00:30:46.679 So there’s only been one study really that looked at liquefaction 00:30:46.680 --> 00:30:51.500 of unsaturated soils by Yoshimi. There’s been maybe a couple other ones. 00:30:54.360 --> 00:31:01.100 But figuring out what the pore water pressure ratio is is also very complicated. 00:31:01.120 --> 00:31:10.340 And most of these relationships end up having some additional 00:31:10.340 --> 00:31:14.340 empiricism inside of them. So this is one of the few sets of data. 00:31:14.340 --> 00:31:17.120 You can see there’s only a few points that are relatively close to 00:31:17.120 --> 00:31:22.739 saturation that are telling you what the pore pressure is – 00:31:22.739 --> 00:31:26.960 the pore pressure ratio required to reach liquefaction. 00:31:26.960 --> 00:31:30.309 So we adapted Kramer’s equation here and applied that to this 00:31:30.309 --> 00:31:34.399 unsaturated data by having this power law of degree of saturation. 00:31:34.400 --> 00:31:38.120 And it had a good fit, but it’s not very well-verified. 00:31:40.509 --> 00:31:43.120 The other thing that you need to apply this is you have to have 00:31:43.129 --> 00:31:46.529 the volumetric strain of a liquefied soil. 00:31:46.529 --> 00:31:50.640 And we don’t really know which one to use. We have several different choices. 00:31:52.380 --> 00:31:56.200 And we multiply that together with our pore pressure ratio to figure out what our 00:31:56.200 --> 00:32:00.570 volumetric strain is due to consolidation. And one of the other challenges here 00:32:00.570 --> 00:32:04.700 is that these depend on our blow counts, 00:32:04.700 --> 00:32:07.960 which sometimes you don’t have in a laboratory test. 00:32:09.009 --> 00:32:12.980 But putting together those two trends between the compression due to 00:32:12.989 --> 00:32:18.539 consolidation, which is zero for dry soil and maximum for a saturated soil, 00:32:18.539 --> 00:32:21.399 and the compression due to collapse of the air voids – 00:32:21.399 --> 00:32:24.940 maximum for a dry soil and zero for a saturated soil, 00:32:24.940 --> 00:32:31.870 you end up getting this nonlinear shape for the seismic compression. 00:32:31.870 --> 00:32:35.389 So the nice thing about this model is that it kind of matches the shape of this 00:32:35.389 --> 00:32:40.659 suction stress characteristic curve where you had the highest for sand, where we 00:32:40.660 --> 00:32:45.200 saw some highest suction stress at some intermediate degree of saturation. 00:32:46.720 --> 00:32:49.100 But one of the challenges is that there’s many different choices 00:32:49.100 --> 00:32:53.580 for the parameters, and they’re all empirical. 00:32:54.080 --> 00:32:59.140 So it’s a little bit vague on how to accurately predict this. 00:32:59.780 --> 00:33:02.759 But we decided to compare the results from this model 00:33:02.759 --> 00:33:07.860 with some centrifuge tests. So we did some – prepared soil 00:33:07.860 --> 00:33:11.320 specimens with different initial degrees of saturation, shook them, and measured 00:33:11.320 --> 00:33:16.420 the seismic compression, and then compared that with the model. 00:33:17.429 --> 00:33:20.180 To do this, we used a concept I developed in my Ph.D. 00:33:20.190 --> 00:33:23.379 where we had a steady-state infiltration from the surface. 00:33:23.379 --> 00:33:26.779 And, once you reach steady-state infiltration, the suction head is 00:33:26.780 --> 00:33:31.680 relatively linear through most of the soil model except near the base. 00:33:31.680 --> 00:33:35.919 And that – you can say that that zone is having a constant suction 00:33:35.919 --> 00:33:39.159 and a constant degree of saturation. 00:33:39.159 --> 00:33:44.109 So we had a whole bunch of stuff on the centrifuge platform. 00:33:44.109 --> 00:33:46.989 We had a sand specimen inside of a laminar container 00:33:46.989 --> 00:33:50.730 sitting on a shaking table. We had a pressurized tank of water 00:33:50.730 --> 00:33:54.539 so we could apply a constant infiltration rate. 00:33:54.539 --> 00:33:58.249 We sprayed water on the surface and then collected water in the outflow tank. 00:33:58.249 --> 00:34:00.169 So we would know what the inflow is from the surface, 00:34:00.169 --> 00:34:03.690 and then we collected the outflow. And we tried to have a uniform 00:34:03.690 --> 00:34:06.720 distribution of the water on the soil surface. 00:34:07.740 --> 00:34:10.849 This is just a picture of the laminar container, and the bottom ring 00:34:10.849 --> 00:34:14.119 had some plumbing connections for the water to come out. 00:34:14.119 --> 00:34:16.460 This is what it looked like on the centrifuge platform. 00:34:16.460 --> 00:34:18.270 There’s a lot of stuff. 00:34:18.270 --> 00:34:25.020 But the laminar container in the specimen is right here in the middle. 00:34:26.080 --> 00:34:29.460 And we had some different sensors embedded in the soil layer 00:34:29.460 --> 00:34:33.639 to measure acceleration profiles and degrees of saturation, 00:34:33.639 --> 00:34:37.330 as well as the surface settlement. 00:34:37.330 --> 00:34:41.740 Some different pictures. We had the membrane gravel layer 00:34:41.740 --> 00:34:45.020 at the bottom, placed sand using pluviation. 00:34:45.020 --> 00:34:47.280 Placed some different sensors. 00:34:47.280 --> 00:34:53.129 And had our spraying rack on top of the soil surface. 00:34:53.129 --> 00:34:58.260 This is just a validation showing that we do have a steady suction 00:34:58.260 --> 00:35:02.700 with height in the soil layer and a steady degree of saturation. 00:35:02.700 --> 00:35:07.080 So we were able to reach several different initial degrees of saturation 00:35:07.080 --> 00:35:10.920 by changing the flow rate that we were applying on the surface. 00:35:11.500 --> 00:35:14.400 Then we shook the table. There’s some challenges because 00:35:14.400 --> 00:35:17.560 the different degrees of saturation had different weights, so it changed 00:35:17.560 --> 00:35:25.619 the actual input motion – or the motion induced in the soil layer. 00:35:25.619 --> 00:35:29.950 So we used the induced surface accelerations to normalize the 00:35:29.950 --> 00:35:34.880 behavior of our observed accelerations at the – at the surface. 00:35:37.140 --> 00:35:42.300 Then this is an example of the surface settlements that we had during shaking. 00:35:42.320 --> 00:35:46.780 Sometimes about 10 millimeters or so for that small soil layer. 00:35:46.780 --> 00:35:50.700 And you could, again, see kind of a rough envelope 00:35:50.700 --> 00:35:55.740 that’s matching the trend that we saw in the predicted model. 00:35:55.740 --> 00:35:58.030 And these are for two different shaking amplitudes. 00:35:58.030 --> 00:36:01.480 But we thought that there was a little bit too much variation here. 00:36:01.480 --> 00:36:07.390 So once we normalized that to account for the different accelerations 00:36:07.390 --> 00:36:11.730 at the surface for the different models by using this information, 00:36:11.730 --> 00:36:15.150 we got a much tighter trend in the data. 00:36:15.150 --> 00:36:18.520 So that had a little bit of a reasonable match 00:36:18.520 --> 00:36:22.270 to that model that I showed you before. 00:36:22.270 --> 00:36:26.000 We also had a couple of pore water pressure sensors which 00:36:26.000 --> 00:36:30.750 we tried to make initially saturated. And you saw that, after you had 00:36:30.750 --> 00:36:33.410 the shaking event, there was some consolidation happening. 00:36:33.410 --> 00:36:38.760 The pore pressure decreased. So there is some transient decrease. 00:36:38.760 --> 00:36:42.160 And then this is just a comparison between our model. 00:36:42.160 --> 00:36:46.849 We kind of under-predicted the behavior for most cases. 00:36:46.849 --> 00:36:52.109 And we potentially think that this could be due to the scaling of the pore fluid. 00:36:52.109 --> 00:36:56.089 So we tried to do another saturated test with Metolose, and we had a closer 00:36:56.089 --> 00:37:01.840 match, but trying to do unsaturated tests with a different pore fluid is challenging. 00:37:02.480 --> 00:37:05.720 And this is just showing that, as your density of the soil changes, 00:37:05.730 --> 00:37:09.510 your amount of settlement is – so this is loose soil and dense soil. 00:37:09.510 --> 00:37:12.660 You can play around with the model at that point. 00:37:12.660 --> 00:37:18.549 So, just quickly, we’ve recently been trying to modify the UBCSand model, 00:37:18.549 --> 00:37:22.109 which is an elastoplastic model, to account for unsaturated conditions. 00:37:22.109 --> 00:37:25.079 Here, instead of going through all these empirical trends 00:37:25.079 --> 00:37:28.359 have been observed in the data, we’re modifying the equivalent 00:37:28.359 --> 00:37:32.380 fluid bulk modulus as a function of the degree of saturation. 00:37:32.380 --> 00:37:34.990 And we’re using that to predict what our pore pressures 00:37:34.990 --> 00:37:38.480 are going to be for a given degree of saturation. 00:37:39.100 --> 00:37:43.960 We’re also incorporating the effective stress from the Lu et al. model. 00:37:45.140 --> 00:37:47.880 So just a couple predictions for a general case. 00:37:47.880 --> 00:37:55.050 We applied some triangle waves of loading to the soil – to a generic soil. 00:37:55.050 --> 00:37:59.880 And you could see that, during shaking, the volumetric strain is going to 00:37:59.880 --> 00:38:04.000 start to accumulate as our plastic shear strain accumulates. 00:38:04.000 --> 00:38:10.920 And our modulus is going to increase as the soil settles and becomes stiffer. 00:38:11.700 --> 00:38:14.240 So, even though the effective stress is decreasing, 00:38:14.240 --> 00:38:17.980 and the soil should get softer as the soil settles, 00:38:17.980 --> 00:38:22.440 it makes it stiffer, so it – the equivalent shear modulus goes up. 00:38:23.860 --> 00:38:27.080 And the main thing we’re predicting is this volumetric strain. 00:38:27.080 --> 00:38:30.680 So, over these amount of cycles, we’re seeing the seismic 00:38:30.680 --> 00:38:34.319 compression happening. So this is still empirical on 00:38:34.319 --> 00:38:37.440 how we implement some of the different parameters here. 00:38:37.440 --> 00:38:42.369 So we’re doing some calibration tests using cyclic simple shear tests. 00:38:42.369 --> 00:38:44.400 So the main different thing that we’re doing here is 00:38:44.400 --> 00:38:47.700 we’re having a ceramic disc at the bottom to control the suction. 00:38:47.700 --> 00:38:50.980 We’re also measuring the suction so we can try to get this r-u parameter 00:38:50.990 --> 00:38:54.109 during cyclic loading. So you have a tensiometer going up 00:38:54.109 --> 00:38:58.380 into the bottom of the specimen, which you can kind of see here. 00:38:58.380 --> 00:39:02.160 And we’re controlling the suction using a Mariotte tube. 00:39:03.180 --> 00:39:10.380 So we’ve adapted an old NGI monotonic simple shear test to do cyclic tests. 00:39:10.380 --> 00:39:12.660 Here’s our sand specimen here. 00:39:12.660 --> 00:39:15.789 And we’ve done some tests and gotten some reasonable results. 00:39:15.789 --> 00:39:20.100 We’re still trying to fine-tune some of the different details here. 00:39:20.940 --> 00:39:23.460 But we’re getting a modulus reduction and damping curves. 00:39:23.460 --> 00:39:25.040 And these are all for dry sands. 00:39:25.040 --> 00:39:29.690 We’re going to be starting to apply this for different unsaturated conditions. 00:39:29.690 --> 00:39:33.490 So just quickly, we’re now trying to apply all of the seismic compression 00:39:33.490 --> 00:39:38.790 from both models to the settlement response of a geosynthetic 00:39:38.790 --> 00:39:43.010 reinforced soil bridge abutment to earthquake shaking at the base. 00:39:43.010 --> 00:39:45.171 So these were some full-scale – or, half-scale tests that 00:39:45.171 --> 00:39:49.779 we performed in our laboratory. So we built some different models – 00:39:49.780 --> 00:39:54.380 compacted soil with reinforcement in soil. 00:39:56.349 --> 00:40:00.880 So these are all relatively large tests with a big reinforced 00:40:00.880 --> 00:40:04.660 unsaturated soil layer inside of this reinforced system. 00:40:06.240 --> 00:40:08.800 A lot of instrumentation for acceleration profiles, 00:40:08.800 --> 00:40:11.940 displacements, strain reinforcements. 00:40:12.520 --> 00:40:14.760 We had a sliding platform for the other end of the beam, 00:40:14.760 --> 00:40:18.890 and this was the final setup for the test. 00:40:18.890 --> 00:40:21.940 And we had a lot of instrumentation. 00:40:21.940 --> 00:40:24.940 So we definitely saw some settlement here. 00:40:24.940 --> 00:40:26.800 We’re definitely seeing less settlement that you’re 00:40:26.800 --> 00:40:31.510 going to have in a free-field soil layer. 00:40:31.510 --> 00:40:33.760 And we’re also seeing some settlement because we’re having 00:40:33.760 --> 00:40:37.640 an outward bulging of the entire wall during shaking. 00:40:37.640 --> 00:40:42.420 But we’re trying to put together this UBCSand model for unsaturated soils 00:40:42.420 --> 00:40:48.400 to hopefully better predict the reasons behind this seismic settlement. 00:40:49.040 --> 00:40:51.087 But these are relatively small at the end of the day, 00:40:51.087 --> 00:40:53.340 which is good from the perspective of the bridge engineers, 00:40:53.340 --> 00:40:58.820 that we’re not going to be having problems with the beam – 00:40:58.820 --> 00:41:03.240 the bridge beam bending due to settlements at the boundaries. 00:41:04.600 --> 00:41:07.220 So, at the end of the day, just some final comments. 00:41:07.220 --> 00:41:10.411 I think the dynamic properties of unsaturated soils – 00:41:10.420 --> 00:41:13.700 we can use similar equations for dry or saturated soils as long as 00:41:13.700 --> 00:41:17.079 we incorporate the right definition of effective stress. 00:41:17.079 --> 00:41:20.402 We need to try to incorporate some of these suction-hardening 00:41:20.402 --> 00:41:23.420 mechanisms that describe changes in the yield stress 00:41:23.420 --> 00:41:26.940 if we want to account for hydraulic hysteresis. 00:41:27.920 --> 00:41:32.616 We presented an empirical methodology for seismic compression 00:41:32.620 --> 00:41:37.640 that we used by extending some available empirical relationships, 00:41:37.640 --> 00:41:43.280 but the input parameter selection is very complex, which makes us want to try to 00:41:43.280 --> 00:41:47.460 use some different types of models instead, like the UBCSand model. 00:41:47.460 --> 00:41:50.345 And, at the end of the day, I think it’s an interesting mechanism 00:41:50.345 --> 00:41:54.080 that is worthy of further research, and it could have some practical impact 00:41:54.080 --> 00:41:59.080 on geotechnical structures as well as maybe on-site response analysis 00:41:59.080 --> 00:42:01.440 involving unsaturated soils. 00:42:01.440 --> 00:42:04.780 So, with that, thank you. Happy to answer any questions. 00:42:04.780 --> 00:42:10.760 [ Applause ] 00:42:12.680 --> 00:42:15.420 [ Silence ] 00:42:16.220 --> 00:42:19.760 - Awesome. Thanks a lot, John, for that interesting presentation. 00:42:19.760 --> 00:42:22.820 We’ll open it up for some questions. 00:42:26.320 --> 00:42:31.040 - Could you go back to slide number 74? – Sure. [laughter] 00:42:33.780 --> 00:42:36.100 Sorry. [chuckles] 00:42:45.300 --> 00:42:48.840 Okay. - Okay. In looking at this slide, 00:42:48.840 --> 00:42:52.920 at least for the settlement aspect, what I’m interested in, 00:42:52.920 --> 00:42:56.020 and the question is whether or not that there are sort of rules of thumb 00:42:56.020 --> 00:43:00.060 for saturation that – and unsaturation when you go from 00:43:00.060 --> 00:43:05.600 wet to unsaturated soil and then from dry to unsaturated soil. 00:43:05.609 --> 00:43:12.160 And looking at that graph, I don’t really see a rule of thumb. 00:43:12.160 --> 00:43:17.920 It looks like it just begins changing right away. Is that a valid interpretation? 00:43:17.920 --> 00:43:22.600 And that also would you apply that to the modulus degradation as well? 00:43:23.760 --> 00:43:27.320 - So you’re saying the trend in the amount of settlements you’re 00:43:27.320 --> 00:43:30.200 going to get for saturated soils, and then the trend that you’re 00:43:30.200 --> 00:43:34.860 having for dry soils? - Yeah. I don’t see a marked transition 00:43:34.860 --> 00:43:38.250 in the behavior of the soil. 00:43:38.250 --> 00:43:41.460 So I could take a rule of thumb that if, you know, the soil is … 00:43:41.460 --> 00:43:44.560 - 90% saturated? - Yeah. Then I’ll treat it as 00:43:44.560 --> 00:43:47.140 saturated and be happy. 00:43:48.380 --> 00:43:52.640 - Yeah. And, I mean, so these are for different 00:43:52.640 --> 00:43:55.200 densities of soils as well. - Right. 00:43:55.200 --> 00:44:03.400 - So you’re – so these are mainly applied to sands. 00:44:03.400 --> 00:44:07.670 So sands have that increasing and decreasing effective stress. 00:44:07.670 --> 00:44:12.060 So you’d expect to have some lowest amount of settlement for 00:44:12.060 --> 00:44:14.980 some certain degree of saturation. 00:44:19.360 --> 00:44:24.800 And the most for the loosest case – 00:44:24.800 --> 00:44:31.120 yeah, so I don’t think you can necessarily group close to saturated soils together. 00:44:32.720 --> 00:44:36.140 It kind of also depends on that pore pressure ratio change 00:44:36.140 --> 00:44:39.160 that you’re getting for those soils that are very close to saturation. 00:44:39.160 --> 00:44:41.820 Because, if your soil is 90% degree of saturation, 00:44:41.820 --> 00:44:45.660 it could potentially still liquefy if you have enough cycles. 00:44:45.660 --> 00:44:50.680 - Mm-hmm. - Whereas the – yeah, drier soils are not. 00:44:50.680 --> 00:44:54.060 So you can – maybe it’s better to look at … 00:45:00.120 --> 00:45:03.700 So changing – if we went to some of the different 00:45:03.700 --> 00:45:07.560 empirical relationships that have been developed in the literature, 00:45:07.560 --> 00:45:12.200 you can have very different changes in the shape of that curve. 00:45:12.200 --> 00:45:17.720 So I don’t really like this approach very much because of that vagueness. 00:45:17.720 --> 00:45:21.740 But it’s the first step that you can do to try to put together 00:45:21.740 --> 00:45:25.540 this partially drained behavior of unsaturated soils. 00:45:25.720 --> 00:45:29.920 - What about a rule of thumb for modulus degradation? 00:45:29.920 --> 00:45:32.460 - For modulus … 00:45:35.320 --> 00:45:38.000 - Well, when do you start worrying about unsaturation, 00:45:38.000 --> 00:45:40.360 I guess, is really what I’m asking. 00:45:41.880 --> 00:45:45.140 - Well, I mean, the big thing that’s important is that your small strain 00:45:45.140 --> 00:45:48.620 shear modulus is going to increase significantly 00:45:48.620 --> 00:45:53.140 and almost log linearly with the suction. 00:45:53.140 --> 00:45:56.660 So, as your soil dries, your small strain shear modulus is going up, 00:45:56.660 --> 00:45:59.420 so your curve is jumping upwards. 00:46:00.600 --> 00:46:06.200 I don’t know about the onset of that cyclic degradation threshold. 00:46:08.100 --> 00:46:11.910 There’s only been, like, four studies so far on 00:46:11.910 --> 00:46:15.200 modulus reduction curves around saturated soils. 00:46:15.200 --> 00:46:19.780 And the ones that that we’ve done, we’ve kind of taken two points 00:46:19.780 --> 00:46:22.840 and used those to calibrate the model in between. 00:46:22.860 --> 00:46:25.800 So I think that still needs to be studied. 00:46:28.180 --> 00:46:37.060 [ Silence ] 00:46:39.280 --> 00:46:44.820 - Let’s see. I guess, in trying to mentally apply some of the stuff 00:46:44.820 --> 00:46:51.300 you’ve been describing here, one of the places we observe 00:46:51.300 --> 00:46:58.100 a lot of seismic-induced compaction – or maybe it’s not all seismic – 00:46:58.100 --> 00:47:07.740 is with bridge fills that overpasses the bridge decks on piles 00:47:07.740 --> 00:47:15.360 stay put, and the dirt piles on either sides collapse a bit. 00:47:17.280 --> 00:47:24.000 How much of that is the seismic compression and how much of it – 00:47:24.000 --> 00:47:28.780 any hammering effects from the nearby abutments? 00:47:28.780 --> 00:47:33.900 And then, how much of it is the – due to the gradient in density? 00:47:33.900 --> 00:47:36.060 It’s got to get less – I mean, you can’t get compaction 00:47:36.080 --> 00:47:38.140 equipment right at the edge. - At the face. 00:47:38.140 --> 00:47:43.380 - So there’s a terrible practical problem there. 00:47:43.390 --> 00:47:46.900 So there have got to be gradients in the – in the density as you approach this. 00:47:46.900 --> 00:47:50.820 It just seemed to me that predicting this is a – is a pretty difficult problem. 00:47:50.820 --> 00:47:55.640 - Yeah. Well, the good thing, when you have this … 00:47:57.040 --> 00:47:58.960 Was I not projecting? 00:47:59.700 --> 00:48:04.060 Sorry. I have it here. I wasn’t combining on both screens. 00:48:06.200 --> 00:48:11.200 [ Silence ] 00:48:12.960 --> 00:48:15.980 Yeah, when you have this geosynthetic reinforced soil, 00:48:15.980 --> 00:48:18.280 one nice thing is that you can get higher compaction 00:48:18.280 --> 00:48:20.740 near the face there because of the reinforcements. 00:48:20.740 --> 00:48:25.340 They allow you to bring your construction equipment closer to the face. 00:48:25.340 --> 00:48:29.760 But that was one main motivation for doing that testing study 00:48:29.760 --> 00:48:36.600 was the interaction between the superstructure and the abutment itself. 00:48:36.600 --> 00:48:40.180 And we weren’t looking at the pile-supported situation. 00:48:40.180 --> 00:48:44.460 So if you have settlement of piles, the place where settlement occurs 00:48:44.460 --> 00:48:47.400 could be anywhere along the length of the pile 00:48:47.400 --> 00:48:51.660 and could potentially be down at the toe. Whereas, for this system, 00:48:51.660 --> 00:48:57.180 it’s probably most going to be in that compacted fill that you’re building. 00:48:58.320 --> 00:49:01.369 So that that’s why we were trying to focus on seismic compression 00:49:01.369 --> 00:49:05.580 for this particular case, where you’re actually designing the backfill. 00:49:05.580 --> 00:49:10.920 For the pile-supported gear, you’re going down deeper into a natural soil deposit. 00:49:13.880 --> 00:49:19.404 But we only saw interaction between – okay, so the bridge structure 00:49:19.404 --> 00:49:23.849 increases the confining stress on the soil and makes it stiffer. 00:49:23.849 --> 00:49:28.260 So that has a positive effect on the deformation response. 00:49:29.760 --> 00:49:32.033 Because if you’re – if you have a heavier bridge load, 00:49:32.040 --> 00:49:38.720 it’s going to make the soil more tightly confined than if you had a lower weight. 00:49:41.360 --> 00:49:46.580 But impacting of the bridge on the abutment is only going to occur 00:49:46.580 --> 00:49:49.640 if you have a very large magnitude earthquake. 00:49:50.660 --> 00:49:53.220 So we only saw that when we were applying the 00:49:53.220 --> 00:49:58.900 [inaudible] motion that we had increased the amplitude. 00:50:00.540 --> 00:50:03.880 - John, I had actually had a question. So the hysteresis effect seemed 00:50:03.880 --> 00:50:08.100 really interesting, and I thought that this research seems really timely 00:50:08.100 --> 00:50:11.880 here in California, given that we were in a long drought, last year was really rainy, 00:50:11.880 --> 00:50:14.640 and then this year, it’s only rained a couple of times so far. 00:50:14.640 --> 00:50:19.760 So I was wondering if anyone’s done – or if you’re aware of a systematic study 00:50:19.760 --> 00:50:25.000 that looks into those types of effects and accounts for that in terms of 00:50:25.000 --> 00:50:28.420 the earthquake hazard across the state. 00:50:28.420 --> 00:50:32.040 - Definitely, I don’t think people have looked at drought effects on earthquakes. 00:50:32.049 --> 00:50:37.599 But you probably have a much – your water tables are probably all lower than normal. 00:50:37.600 --> 00:50:41.620 And so you have a wider unsaturated zone than before. 00:50:41.620 --> 00:50:44.120 And then, if you re-wet it, you’re going to trap all the air 00:50:44.125 --> 00:50:48.009 in the ground, so it’s never going to go back to full saturation. 00:50:48.009 --> 00:50:51.560 So maybe – it could have a positive impact if you had a liquefiable sand 00:50:51.560 --> 00:50:56.340 that dried out, and then you re-wet it, you could trap some air bubbles there. 00:50:56.340 --> 00:51:01.950 But, yeah, I don’t know. - It might be interesting to pursue. 00:51:02.660 --> 00:51:06.260 Any other questions from the audience? 00:51:08.720 --> 00:51:12.560 - It looked like – one of the later slides, you showed an example, 00:51:12.560 --> 00:51:14.580 and it said "Imperial Valley earthquake." 00:51:14.580 --> 00:51:18.960 And I – maybe I missed the details of that, but could you elaborate on that a little bit? 00:51:18.960 --> 00:51:21.980 I’m just wondering what records you used or … 00:51:21.980 --> 00:51:24.580 - I think that – El Centro at the … 00:51:31.000 --> 00:51:37.720 - Yeah. That was on the shaking table. So those are all El Centro. 00:51:44.180 --> 00:51:49.220 Did it say – yeah, sorry. That should be – yeah – El Centro. 00:51:49.220 --> 00:51:52.260 - So what … - I’m not sure if the particular … 00:51:52.260 --> 00:51:58.120 - Oh, I’m just curious what the input to the shake table was meant to represent. 00:52:00.400 --> 00:52:04.840 - So we tried to represent – it never really – 00:52:04.840 --> 00:52:08.980 we’re using the soil records from the surface. 00:52:08.980 --> 00:52:12.440 So, because the shake table is rigid, you’re kind of almost saying that 00:52:12.440 --> 00:52:21.680 the wall is built on top of rock. But, yeah, if you had your motion below, 00:52:21.680 --> 00:52:25.760 then you’re going to have all you your site effects 00:52:25.760 --> 00:52:28.580 and then going into the wall on top of that. 00:52:28.580 --> 00:52:33.520 So, in this case, I think it was the surface records from that site. 00:52:33.520 --> 00:52:34.960 - Okay. 00:52:38.160 --> 00:52:40.420 - Very good. Okay, thanks, John. 00:52:40.420 --> 00:52:42.700 Let’s give John another round of applause. 00:52:42.700 --> 00:52:46.420 [ Applause ] 00:52:46.420 --> 00:52:49.240 So we’re going to grab some lunch at the café. 00:52:49.240 --> 00:52:52.470 If you guys want to meet us over there in about five minutes, at 11:30. 00:52:52.470 --> 00:52:55.339 And then also, John’s around for a little bit this afternoon. 00:52:55.339 --> 00:52:58.171 He’s got kind of a tight schedule, but if you are interested in 00:52:58.180 --> 00:53:02.019 meeting with him, just let me know, and we can try and arrange that. 00:53:02.520 --> 00:53:03.880 - Thanks.