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1. Evaluating Liquefaction Triggering Potential Using Seismic Input Parameters that Are Consistent with ASCE 7-16

   https://doi.org/10.1061/9780784482810.010  

   Green, R.A. and (February 2020, Geo-Congress 2020: Geotechnical Earthquake Engineering and Special Topics)

   J.P. Hambleton, R. Makhnenko (Ed.)

   ASCE 7-16 details how the peak ground acceleration (PGA) should be determined for evaluating liquefaction triggering, with this PGA reflecting the influence of a range of earthquake magnitudes on a site’s seismic hazard. Similarly, the Finn and Wightman magnitude-weighting scheme can be used to account for the full range of magnitudes influencing the seismic hazard at a site, where the weights are derived from a site’s seismic hazard deaggregation data. However, the deaggregation data for the seismic hazard maps for the Central/Eastern U.S. are only available for rock motions and not motions at the surface of the soil profile. The authors explore this issue by comparing the weighted average magnitude scaling factors (MSF) and depth-stress weighting factor (rd) values for multiple sites in the Western U.S. developed using deaggregation data for rock motions and for motions at the surface of the soil profiles. Based on these comparisons, the authors found that using the PGA deaggregation data for rock conditions yield similar weighted averages for MSF and rd as those computed using deaggregation data for the PGA at the surface of the soil profile. 

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2. The 2023 US 50-State National Seismic Hazard Model: Overview and implications

   https://doi.org/10.1177/87552930231215428  

   Petersen, Mark D; Shumway, Allison M; Powers, Peter M; Field, Edward H; Moschetti, Morgan P; Jaiswal, Kishor S; Milner, Kevin R; Rezaeian, Sanaz; Frankel, Arthur D; Llenos, Andrea L; et al (February 2024, Earthquake Spectra)

   The US National Seismic Hazard Model (NSHM) was updated in 2023 for all 50 states using new science on seismicity, fault ruptures, ground motions, and probabilistic techniques to produce a standard of practice for public policy and other engineering applications (defined for return periods greater than ∼475 or less than ∼10,000 years). Changes in 2023 time-independent seismic hazard (both increases and decreases compared to previous NSHMs) are substantial because the new model considers more data and updated earthquake rupture forecasts and ground-motion components. In developing the 2023 model, we tried to apply best available or applicable science based on advice of co-authors, more than 50 reviewers, and hundreds of hazard scientists and end-users, who attended public workshops and provided technical inputs. The hazard assessment incorporates new catalogs, declustering algorithms, gridded seismicity models, magnitude-scaling equations, fault-based structural and deformation models, multi-fault earthquake rupture forecast models, semi-empirical and simulation-based ground-motion models, and site amplification models conditioned on shear-wave velocities of the upper 30 m of soil and deeper sedimentary basin structures. Seismic hazard calculations yield hazard curves at hundreds of thousands of sites, ground-motion maps, uniform-hazard response spectra, and disaggregations developed for pseudo-spectral accelerations at 21 oscillator periods and two peak parameters, Modified Mercalli Intensity, and 8 site classes required by building codes and other public policy applications. Tests show the new model is consistent with past ShakeMap intensity observations. Sensitivity and uncertainty assessments ensure resulting ground motions are compatible with known hazard information and highlight the range and causes of variability in ground motions. We produce several impact products including building seismic design criteria, intensity maps, planning scenarios, and engineering risk assessments showing the potential physical and social impacts. These applications provide a basis for assessing, planning, and mitigating the effects of future earthquakes. 

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3. Uncertainty Differences in Computing Hierarchical Pedestrian Behaviors

   https://doi.org/10.1177/10711813241275937  

   Zhang, Zhengming; Tian, Renran (September 2024, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   The development of autonomous vehicles presents significant challenges, particularly in predicting pedestrian behaviors. This study addresses the critical issue of uncertainty in such predictions by distinguishing between aleatoric (intrinsic randomness) and epistemic (knowledge limitations) uncertainties. Using evidential deep learning (EDL) techniques, we analyze these uncertainties in two key pedestrian behaviors: road crossing and short-term movement prediction. Our findings indicate that epistemic uncertainty is consistently higher than aleatoric uncertainty, highlighting the greater difficulty in predicting pedestrian actions due to limited information. Additionally, both types of uncertainties are more pronounced in crossing predictions compared to destination predictions, underscoring the complexity of future-oriented behaviors. These insights emphasize the necessity for AV algorithms to account for different levels of behavior-related uncertainties, ultimately enhancing the safety and efficiency of autonomous driving systems. This research contributes to a deeper understanding of pedestrian behavior prediction and lays the groundwork for future studies to explore scenario-specific uncertainty factors. 

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4. Geodetic Strain Rates for the 2022 Update of the New Zealand National Seismic Hazard Model

   https://doi.org/10.1785/0120230145  

   Maurer, Jeremy; Johnson, Kaj; Wallace, Laura M.; Hamling, Ian; Williams, Charles A.; Rollins, Chris; Gerstenberger, Matt; Van Dissen, Russ (November 2023, Bulletin of the Seismological Society of America)

   ABSTRACT Geodetic data in plate boundary zones reflect the accrual of tectonic strain and stress, which will ultimately be released in earthquakes, and so they can provide valuable insights into future seismic hazards. To incorporate geodetic measurements of contemporary deformation into the 2022 revision of the New Zealand National Seismic Hazard Model 2022 (NZ NSHM 2022), we derive a range of strain-rate models from published interseismic Global Navigation Satellite Systems velocities for New Zealand. We calculate the uncertainty in strain rate excluding strain from the Taupō rift–Havre trough and Hikurangi subduction zone, which are handled separately, and the corresponding moment rates. A high shear strain rate occurs along the Alpine fault and the North Island dextral fault belt, as well as the eastern coast of the North Island. Dilatation rates are primarily contractional in the South Island and less well constrained in the North Island. Total moment accumulation derived using Kostrov-type summation varies from 0.64 to 2.93×1019  N·m/yr depending on method and parameter choices. To account for both aleatory and epistemic uncertainty in the strain-rate results, we use four different methods for estimating strain rate and calculate various average models and uncertainty metrics. The maximum shear strain rate is similar across all methods, whereas the dilatation rate and overall strain rate style differ more significantly. Each method provides an estimate of its own uncertainty propagated from the data uncertainties, and variability between methods provides an additional estimate of epistemic uncertainty. Epistemic uncertainty in New Zealand tends to be higher than the aleatory uncertainty estimates provided by any single method, and epistemic uncertainty on dilatation rate exceeds the aleatory uncertainty nearly everywhere. These strain-rate models were provided to the NZ NSHM 2022 team and used to develop fault-slip deficit rate models and scaled seismicity rate models. 

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5. Decision-Based Approach to Account for Uncertainty in Estimating the Overtopping Hazard to Manage Risk for Dams

   https://doi.org/10.3850/978-981-11-2725-0-IS2-2-cd  

   Feng, K.; Mostofi, A.; Habibi, M.; Nadim, F. (January 2019, 7th International Symposium on Geotechnical Safety and Risk (ISGSR))

   Ching, J.; Li, D-Q.; Zhang, J. (Ed.)

   This paper describes and demonstrates an approach to improve the management of risks from small-probability events that can lead to large consequences. It applies a decision-based theory to account for limited information in estimating frequencies for rare events to large rockfill dam in Norway that is being assessed for rehabilitation. Uncertainties are considered specifically in estimating the overtopping hazard for the existing dam and for an elevated dam crest. Uncertainty in the estimates of the overtopping hazard curve means that smaller costs of dam failure and/or larger costs of rehabilitation may be justified. From a practical perspective, a cost of rehabilitation in this case that is nearly ten times larger could be justified when the uncertainty in the estimate of the hazard curve is considered. The value of perfect information about the hazard curve increases as the amount of information available decreases and as the cost of failure relative to the cost of rehabilitation decreases. In this case, the value of perfect information about the hazard curve is about 25 percent of the cost to raise the dam crest. 

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