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Hydraulic jacking is a serious threat to concrete spillway chutes, demonstrated by the catastrophic spillway chute failure at California's Oroville Dam in 2017. To improve the understanding of uplift pressure and joint flow developed at open, offset joints and enable design and evaluation of anchors, drain systems, and joint remediations that could help prevent such failures, laboratory tests were performed in a supercritical flume furnished with a model joint where the gap width to offset height ratio was varied over a 725∶1 range. The tests included measurement of boundary layer velocity profiles approaching the joint. Uplift pressures were normalized to the velocity head near the boundary, which is related to the depth-wise velocity profile exponents determined in the experiments and can be estimated for field applications from the chute friction factor. The normalized uplift varies with the joint aspect ratio and the flow depth to offset height ratio. The new relations reduce the uncertainty of modeled uplift pressures by a factor of 2.87 over previous methods. Example applications demonstrate practical upper limits for potential uplift pressure. Subsequent articles will address discharge into offset joints, the dissipation of uplift via drainage, and the effect of different methods for remediating existing offsets to reduce uplift. Practical Applications: Concrete spillway chutes develop cracks and must necessarily be constructed with joints, both of which are prone to displacement over time that may create offsets into the flow. Flow striking such offsets is brought suddenly to rest, similar to a pedestrian tripping on a sidewalk crack. The local stoppage of flow at the offset creates dangerously high pressures that can be injected into the foundation, leading to erosion beneath the slab and potential uplift failure, often called 'hydraulic jacking'. This mechanism has caused several notable failures including the Oroville Dam spillway in 2017. Protection against such failures is usually provided by a combination of the weight (thickness) of the slab itself, anchors that hold the slab down, and subsurface drains that reduce the buildup of pressure. This paper provides experimentally based equations for predicting uplift pressure to enable effective design of new spillways and evaluation of existing spillways. The new equations are significantly more accurate than previous methods because they account for the roughness of the chute surface and the reduced flow velocity near the boundary. Subsequent papers will address flow rate through joints, pressure dissipation by drainage, and methods for treating existing offsets to reduce potential uplift. [ABSTRACT FROM AUTHOR]

For evaluating the resistance performance of cement-stabilized soils in cold regions, the variation of the strength of the cemented sand–gravel (CSG) mixture concerning the hydration process should be explored. This paper aims to study the effect of freeze–thaw (F–T) cycles on the strength and microstructure of a CSG mixture with 10% cement that is subjected to 12 cycles of freezing at a temperature of −23°C for 24 h and then melted at room temperature of 24°C for the next 24 h. The uniaxial compressive strength (UCS), California bearing ratio (CBR), and weight volume loss of the samples were measured after individual F–T cycles. Furthermore, the change in the microstructure of the CSG mixture in various F–T cycles was explored. The results showed a considerable reduction in the UCS up to Cycle 3, then a slight increase for Cycles 3–6, and finally a gradual decrease for further cycles. However, the CBR and weight loss slightly fluctuated up to Cycle 6, and then gradually decreased for subsequent cycles. The majority of compounds of hydrated cement were damaged in the first three cycles. In the following cycles, between Cycles 3 and 6, the portlandite compound was dissolved and recrystallized within the microvoids. Depending on the environmental conditions, carbonation may be generated from the hydrated cement fraction, which fills the microvoids and improves the strength and structure of the mixture. During further cycles after the sixth cycle, the mechanical action of the ice lenses coupled with the disintegration of the hydrate compounds imposed many new microvoids and cracks with considerable length and width, which intensified the strength reduction of the moisture and weakened the adhesion between grains. Since cement is widely used in pavement and dam engineering for stabilizing soils, the durability of cemented soils is of prime concern. This study may help improve the durability and resistance of cemented soils in cold climates. The F–T action not only influences the macrostructure of cement-stabilized soils by imposing a wide crack and ice lens but also induces a considerable change in the complexes existing in the hydrated cement paste of the mixture. Three patterns govern the change of the mixture microstructure in various F–T cycles that correspond to the observed trend in strength. The mentioned trend for the microstructure change and, consequently, the strength variation of the CSG mixture are associated with many factors such as pH, cement content, CO2 content, moisture content within the mixture, and relative humidity within the environment. Accordingly, the pattern of microstructural changes in the CSG mixture after the middle F–T cycles may vary depending on environmental conditions. [ABSTRACT FROM AUTHOR]

The intensity of an atmospheric river (AR) is only one of the factors influencing the damage it will cause. We use random forest models fit to hazard, exposure, and vulnerability data at different spatial and temporal scales in California to predict the probability that a given AR event will cause flood damage, as measured by National Flood Insurance Program (NFIP) claims. We first demonstrate the usefulness of data-driven models and interpretable machine learning to identify and describe drivers of AR flood damage. Hazard features, particularly measures of AR intensity such as total precipitation, increase the probability of damage with increasing values up to a threshold point, after which the probability of damage saturates. Although hazard is generally the most important risk dimension across all models, exposure and vulnerability contribute up to a third of the explanatory power. Exposure and variability features generally increase the probability of damage with increasing values, apart from a few instances which can be explained by physical intuition, but tend to affect the probability of damage less for the largest AR events. Comparisons between random forest models at different spatial and temporal scales showed general agreement. We then examine limitations inherent in publicly available exposure, vulnerability, and loss data, focusing on the difference in temporal resolution between variables from different risk dimensions and discrepancies between NFIP claims and total flood losses, and describe how those limitations may affect the model results. Overall, the application of interpretable machine learning to understand the contributions of exposure and vulnerability to AR-driven flood risk has identified potential community risk drivers and strategies for resilience, but the results must be considered in the context of the data that produced them. [ABSTRACT FROM AUTHOR]

Changes in demand, various hydrological inputs, and environmental stressors are among the issues that reservoir managers and policymakers face on a regular basis. These concerns have sparked interest in applying different techniques to determine reservoir operation policy decisions. As the resolution of the analysis increases, it becomes more difficult to effectively represent a real-world system using traditional methods such as dynamic programming and stochastic dynamic programming for determining the best reservoir operation policy. One of the challenges is the "curse of dimensionality," which means the number of samples needed to estimate an arbitrary function with a given level of accuracy grows exponentially with respect to the number of input variables (i.e., dimensionality) of the function. Deep reinforcement learning (DRL) is an intelligent approach to overcome the curses of stochastic optimization problems for reservoir operation policy decisions. To our knowledge, this study is the first attempt that examines various novel DRL continuous-action policy gradient methods, including deep deterministic policy gradients, twin delayed DDPG (TD3), and two different versions of Soft Actor-Critic (SAC18 and SAC19) for optimizing reservoir operation policy. In this study, multiple DRL techniques were implemented to find an optimal operation policy for Folsom Reservoir in California. The reservoir system supplies agricultural, municipal, hydropower, and environmental flow demands and flood control operations to the City of Sacramento. Analysis suggests that the TD3 and SAC are robust to meet the Folsom Reservoir's demands and optimize reservoir operation policies. Experiments on continuous-action spaces of reservoir policy decisions demonstrated that the DRL techniques can efficiently learn strategic policies in spaces and can overcome the curse of dimensionality and modeling. [ABSTRACT FROM AUTHOR]

In recent years, the construction industry has shifted toward sustainability, replacing traditional binders with eco-friendly alternatives. One such alternative is vinyl acetate-ethylene (VAE) polymer, known for its hydrophobic properties and used in various construction materials. This study explores the potential of VAE polymer to stabilize expansive clay as road subgrade material. VAE polymer was introduced into low-plasticity clay in varying percentages (1%, 1.5%, and 2% by dry weight of clay), and the mixtures underwent a series of geotechnical tests (compaction, unconfined compressive strength (UCS), California bearing ratio (CBR), resilient modulus, shrinkage-swelling, and soil-water retention curve) and microstructural and chemical analyses [x-ray diffraction (XRD), x-ray fluorescence (XRF), microcomputed tomography (MicroCT), and thermogravimetric analysis (TGA)]. The results show that 1 adding 1% polymer resulted in the highest increase in mechanical strength (UCS, CBR, resilient modulus) by 34%, 40%, and 51%, respectively. Moreover, the incorporation of 2% polymer reduced the swelling-shrinkage potential by 67%. Microstructural analyses support these findings. [ABSTRACT FROM AUTHOR]

Stiffness quantification of unbound granular materials (UGMs) is essential for designing pavement structures. This stiffness can be determined directly through the resilient modulus test, or indirectly, by correlation, with the California bearing ratio (CBR) test. Alternatively, the dynamic CBR test (dCBR) has been proposed as a simple and low-cost method for this assessment. This study investigated a new dCBR test protocol based on a novel strain-stage approach, applying 20 load cycles/stage. This testing protocol is expected to facilitate characterization and on-site control of stiffness for UGMs, including marginal granular materials (MGMs). The study characterized four MGMs using the new dCBR test protocol and the resilient modulus test; four replicate specimens manufactured by gyratory compaction were assessed per material and test. The protocol was implemented successfully for the MGMs studied to compute an equivalent resilient modulus. A methodology is proposed for calculating an adjusted equivalent resilient modulus with values similar to those of the resilient modulus. Further research is suggested to explore the relationship between the dCBR and CBR tests to advance the characterization of granular materials for pavement structures and expand the experimental data using a broader set of soils to validate the protocol. [ABSTRACT FROM AUTHOR]

Phosphogypsum, which contains toxic components (e.g., heavy metal elements and fluoride), is one of the byproducts of phosphoric acid production, and filling subgrade is one of the recycling methods for it. In this study, phosphogypsum was stabilized by lime to improve the mechanical properties [California bearing ratio (CBR), resilience modulus, unconfined compressive strength, and shear strength], water stability, and harmful substances dissolubility. Combined with scanning electron microscopy, the strength formation and water stability enhancement mechanism of lime-stabilized phosphogypsum (LSP) were explored. The results demonstrated that the mechanical properties of LSP were better with the lime content of 6%–10%. The CBR, resilience modulus, unconfined compressive strength, and shear strength were 3.35 times, 2.46 times, 8.61 times, and 1.39 times that of plain phosphogypsum, respectively. An intensity prediction model with a correlation of 97% was constructed. The CBR and resilience modulus softening coefficient of LSP reached best values when lime content was 6%–8%. The leaching concentration of arsenic, chromium, and lead of LSP with 2% lime met the quality standards of groundwater levels I, II, and IV, respectively. Fluoride and phosphate were not detected in LSP when lime content was greater than 6.0%. The results show that LSP is feasible as subgrade filler. Considering the mechanical properties, water stability, and dissolution of hazardous substances of LSP, it is recommended to add 6%–8% lime content to LSP as highway subgrade filler. [ABSTRACT FROM AUTHOR]