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PAPER 1: Broadening the Utilization of CSMIP Data: Double Convolution Methodology Towards Developing Input Motions for Site Response and Nonlinear Deformation Analyses by Renmin Pretell, Sumeet K. Sinha, Katerina Ziotopoulou, Jennie A. Watson-Lamprey, and Dimitrios Zekkos
[ABSTRACT - PAPER 1]
The double convolution methodology for the development of input motions for site response analyses and nonlinear deformation analyses is presented, and challenges associated with its development and implementation are discussed. This methodology uses deep VS profiles and random vibration theory to modify ground motions recorded on soil sites such that they are compatible with conditions at a selected depth within a deposit. This selected depth is commonly the base of a numerical domain for a 1D or 2D response analysis, i.e. halfspace. Ongoing efforts in the development of this methodology focus on constraining the ground motions’ high-frequency content, where unrealistic amplification may be estimated. Two approaches for addressing this issue are presented using examples in California.
PAPER 2: Move from Soil/Rock: Site Response Based on the Difference in the VS Profile for the GMPE and the Site-specific VS Profile by Norman Abrahamson
[ABSTRACT - PAPER 2]
The traditional approach used to incorporate site response into the ground-motion hazard analysis is to compute a design spectrum for a rock-site condition and then propagate the rock motion from the base of the soil model to the surface. The rock-site ground motion is computed for a given VS30 value which is often assumed to represent the outcropping motion at a depth at which the VS is equal to the VS30. For example, the ground motion computed for VS30=600 m/s is assumed to apply to the layer at depth with VS=600 m/s. There are two problems with this assumption. First, a site with a given VS30 value will have a gradient in the VS(z) profile so that the Vs at the surface is much lower than the VS30 value. As a result, the assumption that VS=VS30 leads to overestimation of the motion at depth. Second, the VS30 value is not a fundamental physical parameter for site response. The VS30 works in GMMs because the VS30 tends to be correlated with the deeper VS(z) profile that is the fundamental physical parameter for site response. The VS30 should be thought of as an index for the full VS(z) profile and not a key parameter by itself.
Adjusting the ground motion for an average site condition given by the GMMs to the site-specific condition requires first understanding what site condition is represented by the GMM, then computing the site factor to account for the differences. To be able to correct for the differences in the VS(z) profile implicit in the GMM and the site-specific VS(z) profile, requires knowing the VS(z) profile for the GMM. Current GMMs do not provide the VS(z) profiles that go with the GMM, but that is changing. An example of VS(z) and kappa for California that are estimated as part of the GMM development process is shown using the NGA-W2 data set. For each VS30 value, there is a full VS(z) profile and the kappa. These models provide a more complete description of the site condition that goes with the ground motions computed using the GMM. They also make it clear that VS30 is not the important physical parameter for site effects and their use should lead to clear handoffs between hazard analyses and site response studies.
The VS(z) profile correction method described in Williams and Abrahamson (BSSA 2021) is an alternative to the soil-over-rock approach routinely used in earthquake engineering practice. The approach is not new and has been used for Vs-kappa corrections to adjust a GMM from one region to another, but it has not been widely used for site response studies. This approach is similar to the standard soil-over-rock analysis, but it uses different input motions and involves performing two site response analyses -- one for the generic profile associated with the GMM(s) and one for the site-specific profile -- then applying the ratio of the two site response analysis results to correct the design spectrum for the reference site condition developed using the GMMs. An example application of the method is shown.
PAPER 3: Utilizing Instrumented Data to Assess ASCE-41 Acceptance Criteria for Linear and Nonlinear Procedures Using Instrumented Building Data by Laura L. Hernández-Bassal and Sashi K. Kunnath
[ABSTRACT - PAPER 3]
Current provisions in ASCE-41 for performance-based assessment are applied to an existing three-story steel moment frame building that was designed and constructed prior to the 1961 UBC code revisions. A computer model of a perimeter frame that comprises the primary lateral system of the building was developed and validated against available instrumented data from two earthquakes. Both linear and nonlinear procedures were used in the assessment. Findings from the study indicate that the linear static and dynamic procedures produced consistent demand-to-capacity ratios. The nonlinear static procedure resulted in the most severe demands at the lowest level with two beams failing the Collapse Prevention limit state whereas the nonlinear dynamic procedure produced the lowest demands on the building; however, the fact that some individual motions caused some beams to exceed Life Safety or Collapse Prevention limits indicates that ground motion selection can play a major role in the outcome of the assessment when using the nonlinear dynamic procedure.
PAPER 4: Collaborative Recorded Data Based Response Studies of Four Tall Buildings in California by Daniel Swensen and Mehmet Çelebi
[ABSTRACT - PAPER 4]
Seismic instrumentation, recorded earthquake responses, and collaborative studies of the response records from four tall California buildings are summarized in this summary paper. These buildings include the tallest San Francisco building, the 61-story Salesforce Tower, and the tallest California building, the 73-story Wilshire Grand Tower, as well as a 51-story residential building in Los Angeles and a 24-story government building in San Diego. Various system identification methods are used to analyze the largest earthquake response records retrieved from seismic arrays installed in each of the four buildings. Significant structural dynamics characteristics (fundamental periods and critical damping percentages) are extracted. In general, critical damping percentages for the first mode are <2.5%, consistent with recent studies and recommendations.
PAPER 5: Next Generation Seismic Hazard Analysis of Embankment Dams: Case of the Long Valley Dam, CA by Kim B. Olsen, Te-Yang Yeh, and Daniel Roten
[ABSTRACT - PAPER 5]
We have simulated the 0-7.5 Hz seismic response of the Long Valley Dam (LVD), CA, in a 3D velocity model using a supercomputer for a 2015 M3.7 event and the 1986 M6.2 Chalfant Valley earthquake. The simulations include frequency-dependent attenuation Q(f), surface topography, and near-surface low velocity material. We find the most favorable fit to data on and nearby the LVD, including amplification effects of the dam, for models with the shear wave 0.4 quality factor Qs(f) parameterized as 0.075Vs (f < 1Hz) and 0.075Vs f(f > 1Hz) (Vs in m/s), and a dam core with Vs=450 m/s.
PAPER 6: Artificial Intelligence-Enabled Structural Health Monitoring by Yuqing Gao and Khalid M. Mosalam
[ABSTRACT - PAPER 6]
In this data explosion epoch, artificial intelligence (AI)-enabled structural health monitoring (SHM) using the state-of-the-art machine learning (ML) and deep learning (DL) technologies has become of great interest in civil engineering. Based on data type, it can be further classified into two major directions, namely vision-based and vibration-based SHM....the developed advances and obtained promising results in AI-enabled SHM studies shed light on the high potential of these state-of-the-art methodologies in more practical structural engineering applications. In future pursuits, improved monitoring, learning, maintenance, and ultimately effective decision-making regarding the conditions, replacement or retrofit of the built environment can be reliably achieved.
PAPER 7: ShakeAlert Earthquake Warning: The Challenge of Transforming Ground Motion into Protective Actions by Douglas Given and the West Coast ShakeAlert Project Team
[ABSTRACT - PAPER 7]
The USGS ShakeAlert® earthquake early warning (EEW) system is operational and providing public alerting in three West Coast states: California, Washington, and Oregon. Since 2006 the USGS has pursued a strategy of incrementally developing and rolling out EEW for increasingly larger areas and uses. As funding from federal and state budgets grew the system became more capable, detection methods were developed and improved, core network sensor stations were built or upgraded, and partners were enlisted to deliver alerts and implement protective actions. In the fall of 2018, the system became sufficiently functional to publicly declare it “open for business” in all three states for use by licensed partners to alert personnel in limited settings and take automated machine-to-machine actions. State-wide public alerting began in California in October of 2019, expanded to Oregon in March of 2021, and to Washington in May of 2021. Today millions of people can receive ShakeAlert-powered EEW through a variety of delivery methods and dozens of machine-to-machine protective systems are in place in transportation systems, utilities, fire stations, schools, hospitals, and public and private buildings. The ShakeAlert System implementation plan calls for a supporting network of 1,675 seismic stations. 1,129 (73%) have been completed and the rest should be done by 2025.
PAPER 8: The Community Seismic Network for Dense, Continuous Monitoring of Ground and Structural Strong Motion by Monica Kohler and the Community Seismic Network Team
[ABSTRACT - PAPER 8]
The Community Seismic Network (CSN) is a cloud-based, strong-motion network of seismic stations deployed in the greater Los Angeles area. The sensors report three-component acceleration time series data and peak acceleration scalar data for use in assessments of earthquake shaking intensity in buildings and on the ground level, monitoring structural health of instrumented buildings, zonation maps of future shaking potential, and the ShakeAlert earthquake early warning system. The hardware and software behind CSN’s client and server architecture are described, as well as network subarrays deployed at Los Angeles Unified School District campuses, the NASA-JPL campus, and in mid-rises and high-rises.