Department of Civil and Environmental Engineering, UCLA

We appraised factors and processes related to human activities and high water, subsidence, and seismicity. Farming and drainage of peat soils caused subsidence, which contributed to levee internal failures. Subsidence rates decreased with time, but still contributed to levee instability. Modeling changes in seepage and static slope instability suggests an increased probability of failure with decreasing peat thickness. Additional data is needed to assess the spatial and temporal effects of subsidence from peat thinning and deformation. Large-scale, state investment in levee upgrades (> $700 million since the mid-1970s) has increased conformance with applicable standards; however, accounts conflict about corresponding reductions in the number of failures.

Modeling and history suggest that projected increases in high-flow frequency associated with climate change will increase the rate of levee failures. Quantifying this increased threat requires further research. A reappraisal of seismic threats resulted in updated ground motion estimates for multiple faults and earthquake-occurrence frequencies. Estimated ground motions are large enough to induce failure. The immediate seismic threat, liquefaction, is the sudden loss of strength from an increase in the pressure of the pore fluid and the corresponding loss of inter-particle contact forces. However, levees damaged during an earthquake that do not immediately fail may eventually breach. Key sources of uncertainty include occurrence frequencies and magnitudes, localized ground motions, and data for liquefaction potential.

Estimates of the consequences of future levee failure range up to multiple billions of dollars. Analysis of future risks will benefit from improved description of levee upgrades and strength as well as consideration of subsidence, the effects of climate change, and earthquake threats. Levee habitat ecosystem benefits in this highly altered system are few. Better recognition and coordination is needed among the creation of high-value habitat, levee needs, and costs and benefits of levee improvements and breaches.

This study presents physics‐based, 3D simulations using the EQSIM framework for several earthquakes in the Los Angeles region. The primary objective was to assess the ability of deterministic physics‐based ground motion simulations to reproduce the observed motions from historical events. The selected events included the 5.4 2008 Chino Hills, the 4.4 2024 Highland Park, and the 4.3 2021 Carson events. The simulated motions were evaluated by comparing the recorded and simulated seismograms, as well as the Fourier amplitude spectra, across multiple seismic stations. The SCEC 3D velocity model, CVM‐S4.26.M01, was used to represent the regional geology, and ground motion simulations were carried out with a resolution of up to 5 Hz. The results indicate that the simulated motions captured the recorded motions up to approximately 4 Hz. While careful iterations regarding source parameters and corner frequencies were required, and, for the case of the Highland Park event, some of the near‐source stations had relatively low accuracy, the present study established a positive step toward the utilization of physics‐based simulations in practical applications. The computational efficiencies exhibited by EQSIM, especially on GPU clusters, further supported this assertion, as wall‐clock times of simulations involving more than 10 billion grid points were as low as minutes. This permits ensemble simulations for a considered scenario event so that modeling uncertainties (e.g., source and geology) can be bracketed.

The National Seismic Hazard Model Program Steering Committee (NSHM-SC) conducts participatory peer-review during the development of NSHM products, working with the NSHM development team in the U.S. Geological Survey (USGS). This report documents the review processes and outcomes related to the development of NSHM products for application to Puerto Rico and the Virgin Islands (PRVI). The committee consists of nine members who were selected by the USGS, based on expertise and experience. The USGS requested a written report from the NSHM-SC answering the following questions relevant to publication of the 2023 NSHM for PRVI: • Was the National Seismic Hazard Model adequately reviewed? • Did the USGS respond appropriately to the review comments and recommendations? • Is the model suitable for release and to serve as the basis for hazard mitigation? The SC responses to the three main questions are: Was the National Seismic Hazard Model adequately reviewed? The NSHM-SC, and earthquake rupture forecast (ERF) and ground motion characterization (GMC) expert panels called by the NSHM-SC, engaged in several meetings to review components and the complete model between August 2024 and December 2025. The NSHM-SC consensus is that the level of review of the materials provided has been adequate. Did the USGS respond appropriately to the review comments and recommendations? The USGS responded to all NSHM-SC and panel comments and recommendations. USGS adopted most of our recommendations. When not adopted, USGS provided explanations that often indicated that a suggested modification could not be made due to limited time or because adopting the suggestion would break long-standing precedent. Because the panels and the NSHM-SC are advisory in nature, this is acceptable. Is the model suitable for release and to serve as the basis for hazard mitigation? The NSHM-SC considers the 2025 NSHM for PRVI to be suitable for use in building code and similar applications at return periods of 475 years (i.e., corresponding to exceedance probabilities of 10% in 50 years) or longer. The 2025 NSHM for PRVI represents a substantial improvement over the previous NSHM for PRVI developed in 2003, because of the use of better input source characterization models, better ground-motion models, and overall improved computational techniques.

In August 2024, the National Seismic Hazard Model (NSHM) Steering Committee appointed a 14-member panel (herein referred to as “The Panel” or “Panel”) to review the time-independent earthquake rupture forecast (ERF) for the 2025 update of the Puerto Rico and U.S. Virgin Islands (PRVI) component of the NSHM (herein referred to as “PRVI25-ERF”). This report summarizes the Panel’s findings and recommendations. The primary materials for Panel review were nine papers documenting the PRVI25-ERF draft model. The Panel was also informed about the process to update the ERF in three briefings by the U.S. Geological Survey (USGS) development team (herein referred to as the “USGS Team” or “Team”). The PRVI25-ERF model is a substantial improvement over the current model, which was released in 2003 (PRVI03-ERF; Mueller et al., 2003, 2010). The USGS Team has incorporated substantial new information obtained about the region and its tectonic environment over the past twenty years, and they have combined this information with state-of-the-art probabilistic seismic hazard modeling techniques to produce a complete probabilistic ERF that, for the most part, reflects the best available earthquake science. The publications and reports on the model development are generally excellent and comprehensive. State-of-the-art techniques have been employed in the neotectonic and paleoseismic evaluations of fault activity, in the analysis of earthquake catalogs, and in the incorporation of slip-rate estimates from paleoseismic and geodetically constrained tectonic block models. The model of fault slip rates has been generalized to a probabilistic representation, and the epistemic uncertainties of these rates have been incorporated into the logic-tree formulation using a novel stochastic sampling method. The fault system inversion techniques developed for the CONUS23-ERF for the western U.S. (Field et al., 2023; Milner and Field, 2023) have been successfully applied to the PRVI fault systems. The Panel raised questions about the adequacy of the PRVI seismicity data, which are compromised by magnitude inconsistencies; the structure of the crustal fault model and its accommodation of strain partitioning; the weakness of the geodetic constraints on seismic coupling factors; and the low amplitudes of the epistemic uncertainties in the hazard derived from the ERF logic tree. Several of the Panel’s 16 actionable recommendations are directed towards a more complete assessment of the ERF uncertainties. Of particular concern is the bias and uncertainty related to the seismic coupling factors. The Panel also offers 19 aspirational recommendations for model improvements that could be implemented in future PRVI updates. The Panel recommends that, after suitable responses to the actionable recommendations in this review, the PRVI25-ERF model be adopted as a component of the NSHM.

Conventional thin-film composite (TFC) reverse osmosis (RO) membranes experience irreversible performance loss at high temperatures, restricting their use in industries with high-temperature streams, including oil and gas, pharmaceuticals, electronics, power generation, food production, and hybrid desalination plants. However, the mechanisms driving the performance decline of TFC membranes at high temperatures remain poorly understood. Herein, we combine controlled experiments, molecular dynamics simulations, and micromechanical modeling to elucidate TFC failure mechanisms and to evaluate thermally resilient thin-film cross-linked (TFX) composite membrane. Upon exposure to elevated temperatures (>60°C), salt rejection of TFC dropped from ~99 to <90%, with irreversible structural damage in the polysulfone layer, confirmed by scanning electron microscopy. In contrast, the TFX membrane maintained ~99% salt rejection and showed no signs of physical degradation up to 80°C. Our combined analyses revealed that TFC membrane failure arises from irreversible pore expansion in the thermoplastic polysulfone support, leading to polyamide film rupture and delamination. TFX membranes resist thermal deformation, enabling ultrahigh-temperature RO desalination and water reuse.

Extensive field reconnaissance damage surveys, publicly available after the Türkiye earthquake sequence of 2023, provided a unique opportunity to devise and validate a rapid postevent damage assessment framework that uses Artificial Intelligence (AI) techniques to overcome typical challenges encountered in rapid regional damage assessment efforts. By analyzing publicly available satellite images of the significantly impacted city of Antakya, we manually identified and segmented fully or partially collapsed buildings and buildings with visible damage. These were then paired with the various damage state labels in the government data. An AI-based framework was subsequently developed to automate segmentation and damage assessment processes, delivering damage state estimates for other affected regions. The resulting tool, dubbed rapid postearthquake aerial imagery damage assessment (RAPID-A), is an ensemble of various deep segmentation models that work on satellite image channels with a resolution of 30 cm per pixel, augmented with damage proxy maps, such as NASA ARIA maps with a resolution of 30 m per pixel. Using an image segmentation strategy over object detection, RAPID-A provides an uncertainty-aware estimate of different intensities of collapsed and damaged-but-not-collapsed buildings as log-normal distributions. Test case studies carried out over Gaziantep and Kahramanmaras—two of the heavily impacted cities—demonstrate that RAPID-A is generalizable, accurate, and efficient. Such tools can offer significant aid in rapid initial damage evaluations before first responders and damage assessment teams are dispatched. It could also be a main tool for countries that might be unable to launch large-scale regional assessment campaigns. The latter is highly important as it helps other nations focus their aid. The results further suggest that RAPID-A is generalizable and can be applied to future affected areas with an unseen urban fabric.

This study evaluates the efficacy of two alternative methods for estimating vertical seismic load effects (Ev), which are specified in United States building codes (e.g., ASCE 7-22) and guidelines, and employed in the seismic design of structures. In both approaches, the vertical seismic load is computed as a percentage of the nominal dead load. One method uses 20% of the horizontal short-period design spectral acceleration (SDS) as the dead load factor, while the other computes the dead load factor as 30% of the vertical spectral acceleration value at the (vertical) structural period of interest (Sav). The two approaches are evaluated and compared using (1) the results of probabilistic seismic hazard analysis carried out by the USGS (which is the backbone data for ASCE 7-22) for 19,316 sites in California, and (2) the NGA-West2 ground motion models for over 3,000 seismic scenarios. The results are presented in terms of the probability distribution of the ratio of the Ev values obtained from the two approaches (0.3Sav/0.2SDS) as a function of the vertical period of the structure. The evaluation reveals that the outcomes of applying the two methods are generally not consistent and depend significantly on the vertical period range of the structure. The study pinpoints period ranges where the results of one method substantially diverge from the other and provides recommendations for Ev calculations across various seismic design categories.

Model parameter updating can enhance the use of nonlinear structural response simulation to guide decision-making in the postearthquake environment. Since most structures in high seismic regions are not instrumented with sensors, the response history during ground shaking is usually not available after an earthquake. Nevertheless, technologies such as Light Detection and Ranging (LiDAR) and drone-mounted imaging devices have made it possible to more effectively measure residual deformations after the shaking has subsided. It is within this context that a framework for performing nonlinear structural model parameter updating based only on residual drift measurements is proposed. The considered setting is one where a structure is subjected to a sequence of ground motions (without repairs), whereby after each event, the structural model parameters are updated using a Bayesian formulation and the measured residual drift. The methodology is demonstrated by using experimental data from a reinforced concrete bridge pier subjected to six back-to-back ground motions with significant residual drifts recorded after the third, fourth, and fifth records in the sequence. The results showed that the updating procedure is able to incrementally (after each record) improve the accuracy of both the concrete and steel model parameters which also enhanced the estimates of the simulated peak and residual drifts. Finally, a sensitivity analysis is also carried out using an Extreme Gradient Boosting (XGBoost) machine learning model to assess the relative influence of the model parameters on the peak and residual drifts of the bridge pier.