Conference Agenda

To incorporate climate change into civil engineering design, practitioners are currently updating environmental loads for standards to include future weather and climate conditions. The ASCE-NOAA partnership has published pilot data in Atlas 15 for Montana for extreme precipitation scenarios based on 1 to 3 degrees global warming. However, challenges remain for wind and snow load projections, as well as modeling extreme precipitation loads in other locations (outside Montana). Thus, this study aims to explore whether the best available climate model data is sufficient to plan for climate adaptation within infrastructure systems.

Using the best available downscaled climate model data for the state of California (3km x 3km grid with hourly resolution, 5+models), we calculate statistical values for extreme snow loads, hourly precipitation and wind speed. Furthermore, we assess the suitability of climate models to calculate projected loads by comparing climate uncertainty to projected mean changes, as well as output from multiple methods to estimated loads. Specifically, Generalized Extreme Value (GEV) distributions are used over various time periods (2015-2040, 2041-2070, 2071-2100) to look at trends for wind, snow and precipitation from model data. Additionally, historic case study data is used in combination with model data to explore if non-stationary probability distributions can convey trends over 150+ years (combined historic and future data).

Results show that non-stationary probability distribution fitting is not possible for wind and snow values, as trends are not distinctly nonstationary in contrast to precipitation values. Using raw climate model data generally poses a problem due to its high uncertainty and magnitude of calculation methods. The study indicates that even the best available projections alone are not suitable to inform climate change adaptation, and climate adaptation in civil engineering should employ resilience planning concepts such as adaptive pathways as well.

Infrastructure systems are being increasingly exposed to more complex environments. Besides growing internal complexities (e.g., interdependencies among infrastructures), climate non-stationarity challenges frameworks traditionally used to plan, design, and manage those systems. These frameworks are largely based on Fail-Safe (FS) principles, where failure (e.g., flooding, power outages, traffic interruption, water main break, etc.) is sought to be avoided by building robustness up to standardized design thresholds. In response, Safe-to-Fail (STF) has emerged as a design theory that (1) acknowledges higher chances of infrastructure failures, and (2) explores ways to attenuate the consequences from those failures. STF recognizes the complex relationships of infrastructures (e.g., power, stormwater, transportation, water supply) with their external environments and proactively incorporates consequences management into infrastructure design, which unlocks new prospects to accelerate adaptation efforts. However, framings and theories of STF remain in their early stages, thus limiting the operationalization of STF principles. Given this challenge, this work first combines STF with the Social-Ecological-Technological Systems (SETS) framework for infrastructure as a step towards enhancing the requisite complexity of different infrastructures; that is, expanding infrastructures’ portfolio of adaptation strategies able to respond to increasing environment complexity. We argue that the SETS framework creates possibilities to navigate context-specific complexities by identifying pathways of disruption across S, E, and T domains when infrastructure failures occur, then unveiling opportunities to operationalize STF design processes. This would mean building infrastructure capabilities across S, E, and T domains to strategically contain the consequences of failure, and amplifying the suite of strategies for adaptation. For instance, transportation STF strategies could vary from flexible working-from-home schedules to alleviate congestion (social capabilities), to the use of roads as activated floodways (technological capabilities). In turn, for power systems, STF principles could resonate with electric utility companies proactively engaging with community members to inform local responsive actions in the face of outages, as well as outlining demand-side regulations to enhance efficient energy use (social capabilities). We also stress the importance of creating proper governance environments within infrastructure agencies to facilitate the inclusion of STF and SETS into decision making.

Wildfires are a growing threat to infrastructure, and roadways in particular, in Arizona. In addition to causing direct damage, they can destabilize soil and lead to post-fire debris flows when rainfall occurs over recently burned areas. Post-fire debris flows can damage pavement, block drains and culverts, and result in disruptions to roadway services. Wildfire frequency, wildfire intensity, and rainfall intensity are expected to evolve in response to climate change, which may lead to increased debris flow threats. Public agencies who manage roadways will need tools to support decision-making around where to expend resources for mitigation of fire and debris flow risk. In this work, we provide a statewide assessment of roadway vulnerability to wildfire and post-fire debris flow in Arizona under current and future climate conditions. We use a state-of-the-art regression-based model to estimate debris flow likelihood on each roadway segment in Arizona. Debris flow threat is based on terrain ruggedness, burn intensity, and soil characteristics. We then assess vulnerability of each roadway by overlaying debris flow threat, roadway criticality (measured using betweenness centrality), traffic (estimated using annual average daily traffic counts), and sociodemographic variables (including race/ethnicity, income level, and disadvantaged community status). We identify roadways that are most vulnerable in current conditions, and those that are expected to be most vulnerable in future climate conditions. We provide results both at the roadway segment level and at the Arizona Department of Transportation district level. We assess climate uncertainty by using a range of future scenarios covering multiple emissions pathways (RCP 4.5, in which emissions begin to decline in mid-century, and RCP 8.5, in which emissions continue to increase through the end of the twenty-first century) and using an ensemble of global climate models. Our results indicate that the roadways facing the highest debris flow threat are concentrated in mountainous Northern Arizona in the Four Forest Restoration Initiative (4FRI) Region, and in the rugged Southeastern mountains. We find that changes in future debris flow threat vary geographically and across climate scenarios, with some roadways expected to see an increase in debris flow likelihood, and other roadways expected to see a decrease. These results will provide guidance for agencies and decision-makers on where to focus their resources to mitigate the evolving threats of post-fire debris flows.

The construction industry’s increasing emphasis on sustainability has accelerated the use of Environmental Product Declarations (EPDs) to quantify the environmental impacts of building materials. However, challenges remain regarding the consistency and reliability of EPDs across similar materials, and the manual process of searching, downloading, and analyzing EPDs from diverse libraries is labor-intensive. This study examines 10,621 EPDs from 2016 to 2024 and introduces a machine learning (ML) pipeline that automates EPD data extraction from major databases, including EC3 and the International EPD Library. Through an API-driven approach, the pipeline retrieves essential material information—such as compressive strength and Global Warming Potential (GWP)—and processes it for large-scale analysis, offering a more efficient method for handling EPD data.

The ML pipeline utilizes the Interquartile Range (IQR) method for outlier detection, identifying 98 outliers with 68.3% originating from the United States. A geographic distribution analysis further revealed that 94% of EPDs in the dataset were sourced from North America, primarily the United States and Canada. Additionally, the study found a weak correlation (0.29) between compressive strength and GWP, suggesting that material intensity does not directly correlate with environmental impact. A notable increase in EPD submissions since 2021 reflects growing industry adoption, with most assessments utilizing TRACI 2.1 and EF 3.1 Life Cycle Impact Assessment (LCIA) methods. These findings highlight the need for standardized EPD practices to ensure consistent, comparable sustainability assessments across the industry.

This study also addresses the technical challenges of developing the API, particularly around incomplete product specifications and data inconsistencies across EPD sources. The AI/ML model demonstrated robust handling of missing data, enhancing data consistency for materials such as Ready-mix Concrete and Steel Concrete Reinforcing Bars. By integrating large language models (LLMs), the framework enables data enrichment and cross-verification from various sources, showcasing its potential to transform EPD management and analysis in the construction sector. This automation-driven approach to EPD analysis advances sustainability by enabling more efficient, scalable, and comprehensive material impact assessments, paving the way for improved sustainability practices within the industry.

Electric vehicle (EV) adoption is increasing across the U.S., driven by federal and state incentives to electrify vehicle fleets. As this transition accelerates, the strategic siting of electric vehicle charging infrastructure (EVCI) becomes critical. However, many existing EVCI installations have been concentrated within high-income and predominantly white neighborhoods, correlating with areas of higher EV adoption. Studies such as Hsu & Fingerman (2021) have highlighted significant disparities in public EVCI placement in California, showing that low-income and minority communities are often excluded from current siting practices. This research focuses on equitable siting of EVCI, utilizing equity criteria neglected during traditional siting.

The literature on optimal EVCI siting has primarily focused on criteria such as economic viability, proximity to freeways (corridor stations), and existing EV uptake. This approach, however, tends to prioritize communities already able to afford EVs while neglecting disadvantaged populations who should benefit from transportation electrification incentives at both federal and state levels. This exclusion risks deepening existing disparities between marginalized and higher-income communities, emphasizing the need for equity-focused criteria in EVCI planning decisions.

To address these disparities, tools like the Geospatial Energy Mapper (GEM) and the EVI-Equity Tool have been developed. For example, the GEM tool includes multi-criteria decision analysis capabilities that allow for customizable criteria weighting and the inclusion of equity considerations. However, the impact of varying these criteria weights on siting recommendations has not been thoroughly explored.

This research aims to fill that gap by analyzing the significance of weighting adjustments in equity-focused EVCI siting, with an initial focus in California. Using ArcGIS Pro's Suitability Modeler, I will replicate the GEM model while incorporating additional equity layers, such as health equity and pollution burden data that are yet to be analyzed. By experimenting with different weighting schemes and expanding the criteria set, this work will provide decision-makers and planners with a broader understanding and framework for more equity-informed decisions related to EVCI siting, ensuring that the benefits of transportation electrification reach underserved communities.

Climate change poses a significant threat to agricultural productivity, particularly in Sub-Saharan Africa (SSA), where agriculture is the backbone of many economies. In semi-arid regions like Nigeria's Sahel, changes in climate, land use, and vegetation dynamics can substantially affect food security and economic stability. This study aims to provide a spatiotemporal analysis of vegetation and growing season patterns in Nigeria's Sahel from 2000 to 2022, using remotely sensed MODIS-derived NDVI data. The primary motivation for this research is to address the knowledge gap regarding long-term climate impacts on agricultural systems, which are essential for formulating effective adaptation strategies in SSA.

Our study focuses on key seasonality metrics, including the Start of Season (SOS), Peak of Season (POS), End of Season (EOS), and Length of Season (LOS). These metrics provide insight into vegetation health and productivity, which directly influences agricultural performance. By employing advanced statistical methods like the adjusted Mann-Kendall test and Sen’s slope estimator, this research quantifies trends in these metrics, capturing the variability in vegetation dynamics across different states in Nigeria’s Sahel. Our findings show substantial spatial heterogeneity in vegetation trends, with some states like Kebbi and Borno displaying positive NDVI trends that suggest enhanced vegetation productivity, while other states, such as Bauchi and Gombe, exhibit declining trends indicative of environmental stress and unsustainable land management practices.

Technically, the study integrates a combination of time series analysis, trend quantification, and remote sensing data processing. Data preprocessing includes resampling MODIS NDVI data to a daily temporal resolution, gap-filling using linear interpolation, and smoothing the time series using Savitzky-Golay filters. These techniques ensure the robustness of the data for detecting trends and seasonality metrics. The adjusted Mann-Kendall test is particularly well-suited for this study, as it accounts for autocorrelation in the time series data, providing reliable trend detection even with irregular data points.

The results offer critical insights into the varying impacts of climate change on vegetation across Nigeria's Sahel states, underscoring the importance of tailored adaptation strategies for different regions. States with extended growing seasons, such as Kebbi, may benefit from increased agricultural productivity, while areas like Yobe, facing shorter growing seasons and vegetation stress, require drought-resistant crop varieties and water conservation practices. These findings can inform both policy interventions and practical land management strategies aimed at enhancing the resilience of agricultural systems in the face of ongoing climate variability.

This research contributes to filling the empirical data gap regarding climate impacts on agriculture in SSA, offering a framework for future studies and climate adaptation efforts in the region. The anticipated results of this work will be essential for guiding sustainable land management practices and improving the overall resilience of agricultural systems in semi-arid environments.