Infrastructure decisions today significantly impact future generations' quality of life, especially as severe storms and rising sea levels increasingly threaten communities across the United States. Decision-makers at all levels, including those involved in disaster prevention and response, must address climate change. However, those in infrastructure design and construction often prioritize immediate gains over long-term resilience. Engineering teams typically employ decision tools to manage complexity, but these methods may not highlight the long-term consequences of their choices. This study tests whether integrating Virtual Reality (VR) technology with engineering decision-making tools, can enhance engineers’ ability to visualize the future impacts of their infrastructure projects. A group of engineering students (n = 56) was given a case study on updating roadwork systems in Norfolk, Virginia, an area vulnerable to flooding. The control group viewed images depicting current and future conditions of sea-level rise (year 2100), while the intervention group experienced the same information but through an immersive VR scenario. Participants selected achievement levels for each credit using the Envision system, with total points reflecting the resilience and sustainability of decisions. The results indicate that participants who engaged with the VR experience, achieved higher scores than those who received the images. The results also indicate a variation in VR's impact based on political affiliation, with Democrats responding more positively to the VR experience than Republicans. The findings highlight that integrating VR into decision-making tools can be a crucial advancement in infrastructure planning, enabling engineers to make more informed, sustainable choices for a resilient future.

Urban communities in environmentally sensitive areas face escalating challenges due to climate change and inadequate infrastructural support, particularly in underserved regions like southside Norfolk, Virginia. This area, characterized by its vulnerability to flooding and a predominantly low-income population, lacks equitable inclusion in broader urban flood protection plans. This research focuses on the development of community-centered resilience strategies through active engagement and collaboration with local residents. The methodology centered around building trust and understanding within the community through a series of interactions and events. This approach facilitated a two-way exchange of information, enabling the research team to gather crucial insights on community-valued assets, prevalent flooding issues, and preferred flood mitigation solutions. The engagement revealed a significant increase in community knowledge regarding climate change, sea level rise, and stormwater management. Residents expressed a strong preference for green infrastructure solutions, including rain gardens, permeable pavements, and living shorelines, alongside concerns about pollution and the need for infrastructure redesign. The outcomes of this community engagement have initiated plans to develop tailored, nature-based flooding solutions. These results are set to inform future urban planning and policy, offering insights to the City of Norfolk and the United States Army Corps of Engineers for potential redesigns of flood intervention strategies that are more inclusive and effective. A template for participatory research to inform coastal hazard management includes cross-sector collaboration, a long-term engagement commitment, and education and surveying opportunities to align solutions to lived, local experiences. This template allows for community trust building, which is especially important in environmental justice communities. The study highlights the importance of community involvement in urban resilience planning, demonstrating that local engagement is essential in shaping community-centric solutions and equitable environmental policies.

Managing waste tires presents a significant challenge globally, particularly in regions experiencing high temperatures and shortage of landfill sites. This issue is affecting countries like Kuwait, where the abundance of waste tires is a major source of environmental and safety risks, particularly during the intensely hot summer months. This extreme heat has sparked numerous fires, leading to substantial air pollution due to thick black smoke. Given the limited disposal options, recycling waste tires and finding practical applications for ground tire rubber (GTR) is essential. To address the challenge, a comprehensive laboratory testing program was conducted, using locally produced rubber aggregates as an additive to Kuwait's local surface sands. Two new variables were examined namely the size and gradation of the GTR, and the density of the compacted mixes. For this purpose, two different sizes of rubber aggregates, fine and coarse produced locally, were utilized, and the impact of relative compaction on the strength and compressibility of the mixtures was investigated by testing samples compacted to the maximum density and to 95% of the maximum density. The results suggest that combining local surface sands with rubber tire additives results in a lighter, more permeable, and compressible material, contributing significantly to sustainable waste management. With 20% rubber additive the maximum dry density decreased by nearly 20%, and the permeability increased by 1.74–3.11 times and the compression index increased by 6.15 and 3.8 times with fine and coarse rubber respectively. The angle of friction remained unchanged at 36° with the addition of coarse rubber and decreased by 3°–4° with fine rubber. The change in behavior although not an increase in the strength and stiffness, offers a range of suitable practical applications in civil engineering and environmental management, including use as a drainage layer, embankment construction on soft ground, earth fill around retaining walls, an additive in asphalt mixes, and in manufacturing compressible tiles for sports facilities.

In many developing cities, the scarcity of adequate observed precipitation stations, due to constraints such as limited space, urban growth, and maintenance challenges, compromises data reliability. This study explores the use of satellite-based precipitation products (SbPPs) as a solution to supplement missing data over the long term, thereby enabling more accurate environmental analysis and decision-making. Specifically, the effectiveness of SbPPs in Norfolk, Virginia, is assessed by comparing them with observed precipitation data from Norfolk International Airport (NIA) using common bias adjustment methods. The study applies three different methods to correct biases caused by sensor limitations and calibration discrepancies and then identifies the most effective methods based on statistical indicators, detection capability indices, and graphical methods. Bias adjustment methods include additive bias correction (ABC), which subtracts systematic errors; multiplicative bias correction (MBC), which scales satellite data to match observed data; and distribution transformation normalization (DTN), which aligns the statistical distribution of satellite data with observations. Additionally, the study addresses the uncertainties in SbPPs for estimating precipitation, preparing practitioners for the challenges in practical applications. The additive bias correction (ABC) method overestimated mean monthly precipitation, while the PERSIANN-Cloud Classification System (CCS), adjusted with multiplicative bias correction (MBC), was found to be the most accurate bias-adjusted model. The MBC method resulted in slight PBias adjustments of 0.09% (CCS), 0.10% (CDR), and 0.15% (PERSIANN) in mean monthly precipitation estimates, while the DTN method produced larger adjustments of 21.36% (CCS), 31.74% (CDR), and 19.27% (PERSIANN), with CCS, when bias corrected using MBC, identified as the most accurate SbPP for Norfolk, Virginia. This case study not only provides insights into the technical processes but also serves as a guideline for integrating advanced hydrological modeling and urban resilience strategies, contributing to improved strategies for climate change adaptation and disaster preparedness.