Civil and Environmental Engineering

Editors' Note Volume 9 Issue 2

We examine electric vehicle (EV) charging location preferences by investigating how drivers allocate monthly charging sessions across home, workplace, and public locations. In contrast to earlier studies that treat charging location as a one-time, discrete choice, we examine preferences for combinations of all three locations over a monthly period. We employ a multiple discrete–continuous extreme value (MDCEV) model using stated preference data from 881 employed EV drivers across the United States. Results show that 88.9% of charging decisions involve multiple locations. Home charging is preferred overall, followed by workplace and public. Drivers with private driveways, single-family homes, or solar panels, as well as older adults have higher home charging preferences, though battery storage moderates the pull of solar toward home. Urban residents rely more on public charging, while workplace charging is highly price-elastic, offering employers a demand management lever. Our study underscores the importance of examining charging behavior as a portfolio allocation decision.

Since 2000, wildfires in the western United States have increased in both frequency and intensity due to hydro-meteorological shifts, prolonged drought, and expanded human activity. Although geostationary systems enable rapid detection and moderate-resolution sensors offer broad coverage, a gap persists for high-spatial-resolution thermal observations that can assess fine-scale fire behavior. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) provides 70-meter land surface temperature (LST) observations with 1–5 days average revisit intervals, providing enhanced spatial detail for active-fire analysis. In this study, we evaluate the capability of ECOSTRESS Level 2 LST data, which uses 5 thermal bands, to characterize wildfire behavior across four California fires: Carr (2018), Kincade (2019), August Complex (2020), and Dixie (2021). We developed a consistent framework to identify hotspots (LST ≥ 60 ° C ), estimate a satellite-derived rate-of-spread (ROS) proxy using the 95th percentile radial expansion from ignition, and assess relationships between mean active-fire temperature and mean post-fire burn severity (dNBR). Across all fires, median hotspot temperatures ranged from 62 to 77 °C, while 95th percentile values ranged from 117 to 179 °C, indicating right-skewed radiometric distributions. The ROS proxy showed directional variability, with median values typically between 0.05 to 3 km day−1 and substantial heterogeneity among quadrants. Regression indicates consistent positive relationships between mean active-fire temperature and mean burn severity, with R 2 values from 0.51 to 0.88. These relationships were stronger in smaller, short-duration fires. Additionally, the ECOSTRESS derived hotspots were validated against Fire Radiative Power (FRP) data derived from VIIRS, and it was demonstrated that ECOSTRESS-derived thermal anomalies are spatially coherent with independently derived FRP intensity patterns. Our findings indicate that ECOSTRESS provides valuable high-spatial-resolution thermal observations that can resolve fire growth patterns and link active-fire thermal dynamics to subsequent burn severity.

Seismic resilience (SR) has emerged as a critical focus in earthquake engineering to evaluate the ability of structures to endure, recover from, and adapt to seismic events. This study presents an entropy-based multi-criteria approach for selecting optimal intensity measures (IMs) to assess SR of structures. Eight representative IMs, derived from time histories and response spectrum are evaluated. Incremental dynamic analysis is conducted on a reinforced concrete structure, using engineering demand parameters such as the maximum inter-story drift and floor acceleration to generate fragility curves via a probabilistic seismic demand model. The optimal IMs are identified through a multi-criteria decision-making process, with scores calculated using the entropy weight method to incorporate factors such as efficiency, proficiency, and uncertainty based on information entropy. An effective SR framework is derived from fragility results. The findings indicate that peak ground velocity and spectral IMs are the most effective, while energy-related IMs underestimate SR. The study highlights the importance of optimizing IMs for more accurate seismic resilience assessments. The proposed entropy-based multi-criteria approach is shown to be both reliable and effective for selecting optimal IMs in this context.

Characterizing the physical and dynamic meteorology of wildland fires has obvious socioeconomic importance and is necessary to develop not only firefighting but also mitigation strategies such as prescribed burns and effective fuel management practices such as forest thinning. However, despite significant progress over a century, there are shortcomings in our understanding of the physical processes governing wildland fire behavior. Although some research progress has been made in understanding how fires spread on grasslands, several aspects of fire behavior within the forest canopy environment are still not well-understood. This review is an attempt to organize the fluid mechanics of the mass, momentum, and energy transfer during wildland fire events through the lens of vegetation canopy turbulence. The structure, organization, and progress of the flame front and the buoyant plume through the canopy are shown to be intricately related to the coherent structures associated with fire–vegetation–atmosphere interaction, and potential future research directions are identified.