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An accurate seismic response simulation of civil structures requires accounting for the nonlinear soil response behavior. This, in turn, requires understanding the nonlinear material behavior of in situ soils under earthquake excitations. System identification methods applied to data recorded during earthquakes provide an opportunity to identify the nonlinear material properties of in situ soils. In this study, we use a Bayesian inference framework for nonlinear model updating to estimate the nonlinear soil properties from recorded downhole array data. For this purpose, a one-dimensional finite element model of the geotechnical site with nonlinear soil material constitutive model is updated to estimate the parameters of the soil model as well as the input excitations, including incident, bedrock, or within motions. The seismic inversion method is first verified by using several synthetic case studies. It is then validated by using measurements from a centrifuge test and with data recorded at the Lotung experimental site in Taiwan. The site inversion method is then applied to the Benicia–Martinez geotechnical array in California, using the seismic data recorded during the 2014 South Napa earthquake. The results show the promising application of the proposed seismic inversion approach using Bayesian model updating to identify the nonlinear material parameters of in situ soil by using recorded downhole array data. [ABSTRACT FROM AUTHOR]

• Unbonded Fe-SMA bars are used as self-centering elements in ABC column. • Shake table tests are performed on 1/3-scale specimen with near-fault motions. • Observed residual drifts were smaller than a similar conventional column. • Pre-test finite model predictions are compared with the experimental measurements. Excessive seismic damage and residual deformation in bridge piers can result in time-consuming post-earthquake repair and replacement, which will adversely affect the recovery and seismic resilience of the transportation infrastructure. Seismic damage or permanent deformation in bridges can be minimized using advanced materials and novel technologies, of which shape memory alloy (SMA) has gained substantial momentum. The goal of this study is to investigate the feasibility of using unbonded iron-based shape memory alloy (Fe-SMA) bars as self-centering elements in accelerated bridge construction (ABC) piers to reduce residual seismic deformation. Shake table testing is performed on a one-third-scale bridge column specimen subjected to near-fault ground motions. A numerical model of the test specimen is developed in OpenSees, and the "pre-test" analysis results are compared with the experimental measurements. Acceptable correlations are observed between the simulated and measured residual drifts, accelerations, forces, and hysteretic responses. Furthermore, the test results are compared to that of a similar conventional cast-in-place RC column without self-centering elements. The maximum residual drift ratio in the tested Fe-SMA column specimen is found to be much smaller than the conventional column specimen highlighting the potential benefits of the proposed design. [ABSTRACT FROM AUTHOR]

Simulation of nitrogen (N) uptake during crop growing season is very practical in improving fertilizer management and environmental protection. It is very valuable to have a tool which can perform this simulation in a reliable way. HYDRUS-2D model has widely been used to predict the water flow and fate of nutrients in soil. However, no specific research has been conducted so far to evaluate this model in simulating uptake of different N forms (i.e., nitrate and ammonium) during crop growing season. The objectives of this study were to simulate N uptake and transport under furrows and ridges during the maize growth period using HYDRUS-2D model under different N stresses. Thus, maize was planted with different application rates of urea including 0, 150 and 250 kg N ha−1 over 2015 and 2016 growing seasons. Data from the first and second seasons were applied to calibrate and validate model parameters, respectively. The N uptake, soil water content and nitrate and ammonium concentrations in different depths under furrows and ridges were measured during both seasons. Results indicated appropriate agreement between predicted and measured water content, nitrate and ammonium distribution in soil during the validation stage. The ranges of the normalized root mean square error (NRMSE) for nitrate and ammonium concentrations in soil for all treatments were about 22.28 to 23.36% and 17.06 to 24.79% that implies a fair accuracy of HYDRUS-2D model. In spite of appropriate accuracy in estimating total N uptake at harvest time, the model was not capable to simulate accumulated N uptake with high accuracy (13.82 ≤ NRMSE ≤ 29.53) during the growth periods. Consequently, ignoring some processes such as the active uptake to simplify the simulation caused the model to underestimate N uptake rate during the vegetative stage when the maize reaches its maximum growth rate. In addition, the N uptake was simulated more accurately in the absence of N stress. [ABSTRACT FROM AUTHOR]

This paper presents a recursive Bayesian inference framework for digital twinning of an offshore wind turbine (OWT) in the Block Island Wind Farm (BIWF), the first commercial offshore wind farm in the U.S. with jacket substructure. The framework employs a window-based extended Kalman filter (EKF) to jointly estimate the dynamic input loads and unknown model parameters of the finite element (FE) model of the turbine using sparse output-only measurements. The estimation of input loads on the OWT is critical for fatigue life estimation, foundation monitoring and control strategy. In the proposed joint estimation algorithm, unknown input time histories are divided into overlapping windows and the current window of inputs is concatenated with uncertain model parameters to form an augmented state vector. To account for the uncertainty about wind direction and misalignment between wind direction and the turbine yaw angle, two independent horizontal wind load time histories in X and Y directions are estimated at the nacelle level. A numerical validation study is first performed in which wind loads of the OWT are simulated using the multi-physics platform FAST, and structural vibration responses are generated using the FE model in OpenSees. The window-based EKF is implemented using the simulated measurements, and the estimations of structural stiffness as the updating model parameter and input loads are observed to have high accuracy. The proposed algorithm is then validated using in-situ measurements on one of the OWT in BIWF. To investigate the effectiveness and robustness of the method under different environmental and operational conditions, four cases are considered in which the turbine is operating at the rated rotor speed (region 3 of power generation), below the rated rotor speed (region 2), idling rotor state (region 1 or no power), and region 4 beyond cut-out wind speed. Accurate predictions of acceleration and strain data are obtained using the proposed algorithm, and the estimations of model parameter are consistent and close to their nominal value under different conditions. These results from numerical and experimental studies validate the proposed window-based EKF for joint parameter-input estimation and digital twinning for long-term structural health monitoring of the OWT. [ABSTRACT FROM AUTHOR]

In this study, we focus on the effects of fuel bed representation and fire heat and smoke distribution in a coupled fire-atmosphere simulation platform for two landscape-scale fires: the 2018 Camp Fire and the 2021 Caldor Fire. The fuel bed representation in the coupled fire-atmosphere simulation platform WRF-Fire currently includes only surface fuels. Thus, we enhance the model by adding canopy fuel characteristics and heat release, for which a method to calculate the heat generated from canopy fuel consumption is developed and implemented in WRF-Fire. Furthermore, the current WRF-Fire heat and smoke distribution in the atmosphere is replaced with a heat-conserving Truncated Gaussian (TG) function and its effects are evaluated. The simulated fire perimeters of case studies are validated against semi-continuous, high-resolution fire perimeters derived from NEXRAD radar observations. Furthermore, simulated plumes of the two fire cases are compared to NEXRAD radar reflectivity observations, followed by buoyancy analysis using simulated temperature and vertical velocity fields. The results show that while the improved fuel bed and the TG heat release scheme have small effects on the simulated fire perimeters of the wind-driven Camp Fire, they affect the propagation direction of the plume-driven Caldor Fire, leading to better-matching fire perimeters with the observations. However, the improved fuel bed representation, together with the TG heat smoke release scheme, leads to a more realistic plume structure in comparison to the observations in both fires. The buoyancy analysis also depicts more realistic fire-induced temperature anomalies and atmospheric circulation when the fuel bed is improved. [ABSTRACT FROM AUTHOR]

On 30 December 2021, the Marshall Fire devastated the Boulder, Colorado region. The fire initiated in fine fuels in open space just southeast of Boulder and spread rapidly due to the strong, downslope winds that penetrated into the Boulder Foothills. Despite the increasing occurrence of wildland‐urban interface (WUI) disasters, many questions remain about how fires progress through vegetation and the built environment. To help answer these questions for the Marshall Fire, we use a coupled fire‐atmosphere model and Doppler on Wheels (DOW) observations to study the fire's progression as well as examine the physical drivers of its spread. Evaluation of the model using the DOW suggests that the model is able to capture general characteristics of the flow field; however, it does not produce as robust of a hydraulic jump as the one observed. Our results highlight limitations of the model that should be addressed for successful WUI simulations. Plain Language Summary: Wildland‐urban interface (WUI) fires are increasing in the United States and around the world as the built environment continues to expand into the wildland. To better inform real‐time management of active wildfires, it is critical that the scientific community can better predict WUI fire spread. In this study, we rely on multiple observational platforms, including the "Doppler on Wheels" radar, to investigate the performance of a state‐of‐the‐art fire behavior model that links with a weather model during the Marshall Fire, which was a recent WUI fire that occurred in Colorado. While the modeling system performs well during the fire's initial propagation in fine fuels, it is unable to accurately predict spread in the built environment. While turbulence‐resolving simulations can accurately represent atmospheric flow features, more reliable predictability of wildfire behavior in the WUI will require consideration of urban fuels and fire ember spotting. Key Points: Complex meso‐ and micro‐scale meteorology, along with fire ember spotting, were responsible for rapid spread of the Marshall FireRadar observations from "Doppler on Wheels" elucidates three‐dimensional flow structures that impact fire and plume evolutionInitial fire propagation in dry, fine fuels is well‐represented by the coupled WRF‐Fire model, but urban spread remains a challenge [ABSTRACT FROM AUTHOR]

Low efficiency of irrigation is a major problem with surface irrigation methods, mainly due to poor management and improper design. The objective of this study was to evaluate and improve the performance of different surge and alternate furrow irrigation methods. A field experiment was conducted during the grain maize growing season in the Jayedar Plain of Poldokhtar City (southwest of Iran) in the years 2020 and 2021. Five different furrow irrigation methods including conventional furrow irrigation (CFI), surge irrigation with on/off cycle ratios of 1 and 0.5 (SFI1–1 and SFI1–2 , respectively), fixed alternate irrigation (FFI) and variable alternate irrigation (AFI) were investigated. A total of 15 irrigation evaluations were performed at the initial, middle, and end of the growing season by measuring inflow rate, cutoff time, outflow rate, infiltration and advance time. The length and spacing of experimental furrows were 120 and 0.75 m, respectively, and the inflow rate of furrows and the flow cutoff times at the initial (3rd irrigation), middle (6th irrigation), and end (9th irrigation) of the season were 0.28 L/s and 240 min, 0.42 L/s and 360 min, 0.35 L/s and 300 min, respectively. The WinSRFR 3.1 software was calibrated and applied using field data based on the zero-inertia model. The results of the furrow irrigation sensitivity analysis with WinSRFR 3.1 software demonstrated that furrow irrigation in the studied area was most sensitive to the inflow rate, cutoff time and infiltration parameters, respectively. There were high correlations between the measured and simulated values of runoff, infiltration and advance time (coefficient of determination of 1.0, 0.94, and 0.97, respectively). The relative error values in estimating runoff, infiltration and advance time were 5.60 %, 5.09 %, and 2.14 %, respectively. Mean performance indices of different furrow irrigation methods under field experiments showed that the highest values of Application Efficiency (78.7 %), Potential Application Efficiency of the minimum (69.5 %), and Potential Application Efficiency of low-quarter (74.1 %) were observed under the AFI method. By optimizing the inflow rate and flow cutoff time, application efficiencies of 66.6–80.7 % were obtained for different irrigation methods. The results of this study show that AFI is a more suitable method for maize irrigation in the study area. • The performance of different surge and alternate furrow irrigation methods is evaluated using WinSRFR and field experiments. • The best and worst performance corresponded to the variable alternate and conventional furrow irrigation, respectively. • Furrow irrigation performance was improved by managing the cutoff time and the inflow rate. [ABSTRACT FROM AUTHOR]

Accurate estimation of fuels is essential for wildland fire simulations as well as decision-making related to land management. Numerous research efforts have leveraged remote sensing and machine learning for classifying land cover and mapping forest vegetation species. In most cases that focused on surface fuel mapping, the spatial scale of interest was smaller than a few hundred square kilometers; thus, many small-scale site-specific models had to be created to cover the landscape at the national scale. The present work aims to develop a large-scale surface fuel identification model using a custom deep learning framework that can ingest multimodal data. Specifically, we use deep learning to extract information from multispectral signatures, high-resolution imagery, and biophysical climate and terrain data in a way that facilitates their end-to-end training on labeled data. A multi-layer neural network is used with spectral and biophysical data, and a convolutional neural network backbone is used to extract the visual features from high-resolution imagery. A Monte Carlo dropout mechanism was also devised to create a stochastic ensemble of models that can capture classification uncertainties while boosting the prediction performance. To train the system as a proof-of-concept, fuel pseudo-labels were created by a random geospatial sampling of existing fuel maps across California. Application results on independent test sets showed promising fuel identification performance with an overall accuracy ranging from 55% to 75%, depending on the level of granularity of the included fuel types. As expected, including the rare—and possibly less consequential—fuel types reduced the accuracy. On the other hand, the addition of high-resolution imagery improved classification performance at all levels. [ABSTRACT FROM AUTHOR]

Wind turbine drivetrain systems experience occasional failure in their operational life-time. Dynamic loads in these systems often differ from those predicted in the design phase, which may lead to incipient faults and damage eventually resulting to unpredicted failures. Faults in a system cause changes in the system dynamic properties. Therefore, a calibrated dynamic model can be used for fault diagnosis of the drivetrain system. The current paper implements a Bayesian inference method for physics-based model updating for the first time to estimate the parameters of a physics-based wind turbine drivetrain model as well as the unknown input loads using measurement data. An information-theoretic approach is adopted to study the identifiability of different model parameters given different subsets of drivetrain measurements. The 5 MW NREL wind turbine is used as a case study, and its drivetrain is modeled by a 5-mass torsional model. Torsional shaft stiffnesses and rotor mass moment of inertia are the parameters reflecting dynamics of the drivetrain and are assumed unknown. Moreover, the aerodynamics input torque applied to the drivetrain system is assumed as unknown. The Bayesian inference method is used to estimate jointly the unknown model parameters and aerodynamic input torque using numerically simulated measurement data. The case studies show that the proposed model updating approach can accurately estimate the low-speed shaft stiffness, rotor mass moment of inertia, and the time history of aerodynamic torque jointly using only measurements from the generator including generator torque and rotational speed. • A Bayesian inference method is developed to estimate the parameters and input of a wind turbine drivetrain model. • An information-theoretic approach is adopted to assess the identifiability of model parameters. • The best sensor set is selected for parameter and input load estimation. • Given the model and data, aerodynamic load, low-speed shaft stiffness, and rotor mass moment of inertia are estimated." [ABSTRACT FROM AUTHOR]

Rapid post-earthquake damage diagnosis of bridges can guide decision-making for emergency response management and recovery. This can be facilitated using digital technologies to remove the barriers of manual post-event inspections. Prior mechanics-based Finite Element (FE) models can be used for post-event response simulation using the measured ground motions at nearby stations; however, the damage assessment outcomes would suffer from uncertainties in structural and soil material properties, input excitations, etc. For instrumented bridges, these uncertainties can be reduced by integrating sensory data with prior models through a model updating approach. This study presents a sequential Bayesian model updating technique, through which a linear/nonlinear FE model, including soil-structure interaction effects, and the foundation input motions are jointly identified from measured acceleration responses. The efficacy of the presented model updating technique is first examined through a numerical verification study. Then, seismic data recorded from the San Rogue Canyon Bridge in California are used for a real-world case study. Comparison between the free-field and the foundation input motions reveals valuable information regarding the soil-structure interaction effects at the bridge site. Moreover, the reasonable agreement between the recorded and estimated bridge responses shows the potentials of the presented model updating technique for real-world applications. The described process is a practice of digital twinning and the updated FE model is considered as the digital twin of the bridge and can be used to analyze the bridge and monitor the structural response at element, section, and fiber levels to diagnose the location and severity of any potential damage mechanism. [ABSTRACT FROM AUTHOR]

• A new percolation-based model for unsaturated hydraulic conductivity is developed. • Weibull distribution is used in combination with critical path analysis and percolation theory. • Model validation using measured and simulated data confirms good performance. • Capillary pressure curve slightly outperforms pore-throat size in estimating K (S w). Accurate estimation of unsaturated hydraulic conductivity, K (S w), is crucial for modeling water and solute transport in variably-saturated soils. In this study, a new model for the estimation of K (S w) based on percolation theory and critical path analysis is presented, in which the pore-throat size distribution is described by the three-parameter Weibull probability density function. To evaluate the performance of the proposed K (S w) model, two datasets including more than three hundred samples are analyzed. The first dataset includes 240 pore-network simulations, while the second dataset consists of 101 soil samples from the UNSODA database. For the pore-network simulations, for which both pore-throat size distributions and capillary pressure curves are available, we estimate K (S w) from the capillary pressure curve with RMSLE = 0.54 slightly better than the estimations obtained from the pore-throat size distribution with RMSLE = 0.6. For the soil samples, since only the capillary pressure curve is measured, we estimate K (S w) from the capillary pressure curve and evaluate three previously proposed approaches for determining the exponent, α, in Poiseuille's law. We find that RMSLE = 0.98 for α = 3, RMSLE = 0.87 for α = 2(4 − D) − (3 − D)/(2 D − 3), and RMSLE = 1.06 for α = 4 − 0.74 D , in which D is the fractal dimension characterizing the pore space. Comparing the results with previous studies in which the power-law probability density function was used to characterize capillary pressure curve and pore sizes shows that the proposed three-parameter Weibull distribution, in conjunction with the CPA analysis, may more accurately estimate K (S w) for a wide variety of soils ranging from very fine to very coarse textures. [ABSTRACT FROM AUTHOR]

Disturbance events can happen at a temporal scale much faster than wildland fire fuel data updates. When used as input for wildland fire behavior models, outdated fuel datasets can contribute to misleading forecasts, which have implications for operational firefighting, mitigation, and wildland fire research. Remote sensing and machine learning methods can provide a solution for on-demand fuel estimation. Here, we show a proof of concept using C-band synthetic aperture radar and multispectral imagery, land cover classes, and tree mortality surveys to train a random forest classifier to estimate wildland fire fuel data in the East Troublesome Fire (Colorado) domain. The algorithm classified over 80% of the test dataset correctly, and the resulting wildland fire fuel data was used to simulate the East Troublesome Fire using the coupled atmosphere—wildland fire behavior model, WRF-Fire. The simulation using the modified fuel inputs, where 43% of original fuels are replaced with fuels representing dead trees, improved the burn area forecast by 38%. This study demonstrates the need for up-to-date fuel maps available in real time to provide accurate prediction of wildland fire spread, and outlines the methodology based on high-resolution satellite observations and machine learning that can accomplish this task. [ABSTRACT FROM AUTHOR]

Lowland rice flooding, while consuming considerable water, has adverse environmental effects, including releasing methane into the atmosphere, aggravating climate change. Several drawbacks can be attributed to water-saving alternatives, such as aerobic rice, whereas rice drip irrigation techniques have been evaluated primarily under aerobic conditions. This study investigated drip tape irrigation technique with different lateral spacings for transplanted rice in puddled paddy soil at the Rice Research Institute of Iran in Rasht over two years (2020 and 2021), compared to intermittent irrigation. In twelve isolated concrete basins, three treatments with lateral spacings of 40 (T40), 60 (T60), and 80 cm (T80), were compared with a fixed five-day period intermittent irrigation (INT). In 2020 and 2021, the INT treatment produced the highest grain yield (3814 and 4758 kg ha−1, respectively) and among drip tape treatments, T40 demonstrated the highest grain yield (3613 and 4020 kg ha−1, respectively), comparable with INT's yield but with the highest sterile spikelets ratio. In contrast, T80 resulted in the lowest grain weight (2892 and 3653 kg ha−1, respectively) and yield characteristics values, while producing the highest weeds population. Drip tape irrigation significantly reduced water application by 27% and 36% in 2020 and 2021, respectively and T40 achieved the highest water productivity, at 0.68 kg m−3. The economic water productivity estimation of drip tape irrigation can range from 0.11 to 0.51 US$ m−3 in various scenarios, while that of intermittent irrigation remains at 0.25 US$ m−3. • Drip tape irrigation of transplanted rice compared to intermittent irrigation (INT). • Drip irrigation saved water by up to 36% compared to INT. • WP ranged between 0.44 and 0.68 kg m−3, an increase of up to 22% compared to INT. • 40 cm lateral spacing is the most yielding and profiting drip irrigation formation. [ABSTRACT FROM AUTHOR]

Wildland fire simulation models can be used to inform wildfire risk assessment and mitigation strategies. However, existing models tend to simulate fire spread only inside the wildland or the wildland-urban interface (WUI) communities, but not in both. As a result, there is a need to integrate methodologies to enable seamless simulations of events that ignite in the wildland and continue spreading both across the wildland and inside WUI communities. This paper investigates a systematic methodology to provide a WUI fire spread model with the capacity to assimilate dynamic wildland fire position inputs. The paper uses the streamlined wildland-urban interface fire tracing (SWUIFT) model to simulate WUI fire spread, weather radar data to track the fire line inside the wildland, and the 2018 Camp Fire event as a case study. The work proposes and evaluates a methodology based on a 'Three-Domain Solution' (Wildland, Transition, Community) for a smooth fire transition between wildland and community settings. Alternative approaches are examined to describe the boundaries of a Community Domain for simulation purposes. Existing WUI definitions are studied, and considering the analysis results, a non-WUI neighborhood-based housing density (NBHD) method is proposed. This work establishes a systematic approach for a unified wildland-WUI fire simulation. • Integration of observations and models replicate fire spread during the Camp Fire. • The use of three simulation domains allows a continuous wildland-WUI fire tracking. • Existing WUI definitions can inform the boundaries of a community simulation domain. • A new housing density method provides valid community simulation domain boundaries. • SWUIFT can be systematically connected to dynamic wildland fire position inputs. [ABSTRACT FROM AUTHOR]

• Two adaptive Kalman filters (KFs) are proposed for nonlinear model updating. • They include forgetting factor and moving window covariance-matching methods. • Proposed methods account for modeling errors in estimated noise covariance matrix. • The methods are evaluated through two numerical applications. • More accurate parameter estimations and response predictions are obtained. This paper presents two adaptive Kalman filters (KFs) for nonlinear model updating where, in addition to nonlinear model parameters, the covariance matrix of measurement noise is estimated recursively in a near online manner. Two adaptive KF approaches are formulated based on the forgetting factor and the moving window covariance-matching techniques using residuals. Although the proposed adaptive methods are integrated with the unscented KF (UKF) for nonlinear model updating in this paper, they can be alternatively combined with other types of nonlinear KFs such as the extended KF (EKF) or the ensemble KF (EnKF). The performance of the proposed methods is investigated through two numerical applications and compared to that of a non-adaptive UKF and an existing dual adaptive filter. The first application considers a nonlinear steel pier where nonlinear material properties are selected as updating parameters. Significant improvements in parameter estimation results are observed when using adaptive filters compared to the non-adaptive approach. Furthermore, the covariance matrix of simulated measurement noise is estimated from the adaptive approaches with acceptable accuracy. Effects of different types of modeling errors are studied in the second numerical application of a nonlinear 3-story 3-bay steel frame structure. Similarly, more accurate and robust parameter estimations and response predictions are obtained from the adaptive approaches compared to the non-adaptive approach. The results verify the effectiveness and robustness of the proposed adaptive filters. The forgetting factor and moving window methods are shown to have a simpler tuning process compared to the dual adaptive method while providing similar performance. [ABSTRACT FROM AUTHOR]

• Paper highlights instrumentation and monitoring of the first US offshore wind farm. • Modal parameters of the OWT are reported over one year of continuous monitoring. • Modal parameters show correlation with operational and environmental conditions. • The side-to-side modal properties are generally stable with little variability. • The fore-aft modal properties vary significantly with wind speed and turbine operation. This paper presents the variation of identified modal parameters of an offshore wind turbine (OWT) over one year of continuous monitoring. The considered OWT is a Haliade 150–6 MW GE turbine on a jacket substructure at the Block Island Wind Farm in Rhode Island, U.S. The turbine was instrumented with a monitoring system including accelerometers, strain gauges, inclinometers, and a data acquisition system with automatic data transfer to cloud storage. Continuous vibration measurements of the OWT have been collected over one year from April 2021 to April 2022. An automated output-only operational modal identification approach is developed to extract modal parameters of the OWT over this one-year period using windows of 10-min data. The identified modal parameters of the first few vibration modes show variability and correlation with several ambient and operational conditions. It is observed that the identified natural frequencies and damping ratios of the first side-to-side (SS) mode are stable over time and have very small variation, but those of the first fore-aft (FA) mode show significant variability. The second FA/SS modes can only be identified with reasonable accuracy when the turbine is in idle condition. Similar to the first FA/SS modes, the second FA mode has larger variability in frequencies and damping ratios than the second SS mode because of the aerodynamics effects. The system identification results over one full year verify the effectiveness and robustness of the proposed approach for long-term monitoring of this OWT and serve as the reference database. [ABSTRACT FROM AUTHOR]

• A new laboratory method for estimating soil water retention is introduced. • The method only requires soil water content-dependent SWIR reflectance data. • Validation for 21 vastly different soils indicates a good performance. The soil water retention curve (SWRC) is an essential soil property that relates soil water content and matric potential. It plays a crucial role in soil water dynamics and the understanding of various hydrological phenomena at the land surface, including infiltration, runoff, evaporation, and energy exchange processes. In recent years, proximal sensing methods have shown great potential for retrieving this challenging-to-measure property from spectral reflectance. However, a physically-based approach is still lacking as current methods rely on empirical data-driven algorithms. Here we propose a novel physics-based laboratory method that, for the first time, enables direct estimation of the entire SWRC from saturated to dry using soil water content/reflectance data pairs within the shortwave infrared domain. The main hypothesis underlying the new method is that soil optical properties not only vary with soil water content but also with the pore scale distribution of capillary and adsorbed soil water. For evaluation, retrieved soil water retention curves of 21 soils that vastly differ in physical and hydraulic properties were compared to direct measurements. The results suggest that the new method is a rapid and efficient alternative to established laboratory measurement methods. [ABSTRACT FROM AUTHOR]

Wildfires are part of the natural cycle of life in vegetated regions. The apparent increase in size and frequency of recent years reflects land management legacy, expansion of human activity, and changes in climatic conditions. The primary objectives of the review are: (1) to introduce hydrologists, soil scientists and ecologists to principles and advances in modeling of wildfire dynamics and rates of spread to improve understanding of capabilities and limitations offered by modern wildfire models; (2) to highlight persistent omissions of wildfire effects on soil processes and how soil and hydrology communities can harness wildfire models to bridge these gaps and provide the necessary boundary conditions for quantifying thermal alterations of soils. The review aims to enhance cross-disciplinary understanding critical for advancing quantitative representation of wildfire impacts on soil and hydrologic processes and formulating boundary conditions for subsequent hydro-ecological recovery of fire-affected landscapes. The review presents key elements used to represent wildfire characteristics, processes and metrics considered in wildfire science. We then provide an overview of wildfire modeling approaches and models used for research and for prescribed burning planning that include wildfire behavior models, semi-physical and mechanistic large eddy simulation models. We highlight key features and assumptions, and common applications of each class of models and their inputs (spatially resolved databases of fuel maps, digital elevation, land cover, and weather). The second section of the review proposes a path towards building a systematic framework for quantifying soil, ecological and hydrological wildfire-induced alterations to parameterize post-fire hydrologic responses and predict landscape recovery rates across seasonal to climatic time scales. [ABSTRACT FROM AUTHOR]

• New bridge column seismic repair method based on ABC-inspired connection. • Shake table testing of large-scale repaired bridge subassembly (inverted column and cap beam). • Analysis of test specimen global and local response. • Comparison of repaired and original test specimens under similar seismic loading protocol. • Recommendations for proposed ABC-inspired CIP pocket connection and repair method. Accelerated bridge construction (ABC) has been utilized in precast bridge structures because of its advantage to expedite on-site construction. In ABC, one of the main concerns is the joint connection as it needs to be well designed to maintain structural integrity. A recent study at the University of Nevada, Reno (UNR) was conducted to utilize ABC pocket connection concept in cast-in-place (CIP) cap beam-column bridge joint. The cap beam was constructed by having the cap beam longitudinal reinforcement bundled outside the pocket joint allowing the placement of the column reinforcement cage uninhibited. The specimen was tested in an inverted position at one of UNR's shake tables and found to perform well under out-of-plane ground motion excitations, and the ductility of the column was confirmed through typical plastic hinge behavior. In this study, the specimen from the recent UNR test was further utilized by removing the damaged column in the cap beam through cutting and coring. A new column was constructed in the cap beam using the same reinforcement detailing from the original specimen. The repaired specimen was tested using the same loading protocol adopted from the 1994 Northridge earthquake as the original model. The objective of this study is to demonstrate that ABC-inspired CIP joints can provide a viable repair method after major earthquakes, where damaged columns can be replaced without affecting cap beam reinforcement. The repaired specimen performed well as the plastic hinge zone was developed in the column outside the joint, close to the interface of the cap beam, as desired and required by design. The cap beam remained essentially elastic, i.e. capacity-protected as required, throughout the test which is similar to the performance of the original model. High residual drift ratios were recorded at higher earthquake motions and were attributed to the slippage of the column in the joint. The paper also concludes with recommendations for the repair of CIP cap beam-column joints emulating ABC pocket connections. [ABSTRACT FROM AUTHOR]

• A new model links soil water retention and soil spectral reflectance. • Validation for 21 vastly different soils confirms good performance of the model. • Results imply potential for soil water retention retrieval from reflectance data. Soil hydraulic properties, including soil water retention, control various terrestrial hydrological processes such as infiltration, runoff, and evapotranspiration. Measuring and monitoring these properties at large scales is challenging. Therefore, over the past decade, remote sensing has been investigated as a promising tool for large-scale mapping of soil hydraulic properties. Nonetheless, a solid physical relationship between soil hydraulic and spectral properties is still lacking. To close this knowledge gap, this study introduces a novel physical-empirical model that to our best knowledge for the first time connects the soil water retention curve (SWRC) to soil spectral reflectance. The new model has been developed based on the hypothesis that the capillary and adsorbed water components of the SWRC exhibit vastly different optical properties due to their distinct distribution within the soil porous system. The model was validated using soil water retention and reflectance measurements of 21 soils, vastly differing in physical and hydraulic properties. The model provides not only a new and accurate soil moisture-reflectance functional relationship, but also a potential means for retrieval of the soil water retention curve from spectral reflectance in the shortwave infrared domain. [ABSTRACT FROM AUTHOR]

• The dynamic response of a base-isolated building tested on a shake table is studied. • Data from low-amplitude white noise and seismic base excitation tests are analyzed. • Effective stiffness and damping ratio are estimated from quality control, white noise, and seismic tests. • Automated system identification using ambient vibration data is conducted. • Input-output and output-only methods are used to identify the modal parameters using white noise base excitation test data. • Effects of environmental conditions and amplitude of excitation on the identified modal properties are discussed. This paper presents the analysis of the seismic response and the identification of the dynamic properties of an equivalent linear time-invariant model of a full-scale five-story base-isolated reinforced concrete building tested on the unidirectional NEES@UCSD shake table. A sequence of seven scaled historic earthquake records was applied by the shake table to the building, progressively increasing the seismic demand on the structure and its nonstructural components and systems. The effects of the isolation system on elongating the predominant period of the building, concentrating the displacement in the isolation layer, and augmenting the energy dissipation capacity of the building system are investigated. Low-amplitude white noise (WN) base excitation tests were conducted before and after each seismic test and ambient vibration (AV) data were recorded continuously for a period of approximately sixteen days containing the sequence of seven seismic tests performed. Because of the low intensity of the white noise base and ambient excitations, a quasi-linear response of the base-isolated building is assumed, allowing the modal parameters of an equivalent linear viscously damped time-invariant model to be estimated from vibration data recorded during the WN and AV tests. Different state-of-the-art system identification methods, including output-only and input-output methods, are used to estimate the modal properties of the base-isolated building. Results show that the identified modal parameters obtained from different methods are in good agreement and that the assumption of quasi-linear response for each low-amplitude test is appropriate. The effects of the amplitude of the excitation, earthquake input motions, and environmental conditions are clearly evidenced by the changes induced in the estimated modal parameters of the building. [ABSTRACT FROM AUTHOR]

Proximal and remote sensing techniques in the optical domain are cost-effective alternatives to standard soil property characterization methods. However, the extent of light penetration into the soil sample, also termed soil information depth, is not well understood. In this study a new analytical model that links the particle size distribution and soil reflectance in the near infrared (NIR) and shortwave infrared (SWIR) bands of the electromagnetic spectrum is introduced. The model enables the partitioning of measured reflectance spectra into surface and volume (subsurface) contributions, thereby yielding insights about the soil information depth. The model simulations indicate that the surface reflectance contribution to the total reflectance is significantly higher than the volume reflectance contribution for a broad range of soils that vastly differ in texture, mineralogical composition and organic matter contents. The ratio of volume to total reflectance is higher for sandy soils than for clayey soils, especially at longer optical wavelengths, but the ratio rarely exceeds 15%. Therefore, the light reflection from dry soils is predominantly a surface phenomenon and the information depth in most soils rarely exceeds 1 mm. The results of this study reveal an intimate physical relationship between soil reflectance and the particle size distribution in the NIR/SWIR range, which opens a potential new avenue for retrieval of the particle size distribution from remotely sensed reflectance via a universal process-based approach. • A new model links the particle size distribution and soil NIR/SWIR reflectance. • It partitions reflectance spectra into surface and volume contributions. • It shows that light reflectance from dry soils is dominantly a surface phenomenon. • The volume/total reflectance ratio is higher for coarse than for fine soils. • The model opens new avenues for remote sensing of soil hydraulic properties. [ABSTRACT FROM AUTHOR]

Understanding material behavior is key to discovering innovative applications in any field. Regardless of the exciting mechanical properties of polyurea, there has been a limited effort in studying the use of polyurea for structural retrofit and strengthening applications. This study aims to understand the behavior of polyurea under different tensile loading conditions to provide critical information towards enabling the future use of polyurea in structural applications. Several standard coupons are tested under various tensile loading conditions to understand the mechanical behavior of eight different commercial polyureas. The study provides the full stress-strain characteristic curves that can be used for constitutive modeling purposes. The results show that polyurea has a wide range of properties from low strength flexible nature to high strength rigid nature. All tested polyureas displayed some level of rate dependency, i.e., ultimate strength is a function of loading rates. The high-strength polyureas tested only show slight rate dependency and good strength retention under cyclic and fatigue tensile loading, suggesting that polyureas have promising mechanical properties for potential structural applications.