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There have been several advances in Structural Health Monitoring (SHM) throughout the last two decades. Among these advances is that sensors and data acquisition have become smaller in size while wireless technologies have been making wireless communication and data accessing easier. These advances create cost effective sensing solutions for communities where flooding and wildfires put their members and infrastructure at risk. Therefore, with higher community involvement in understanding and utilizing new sensing technologies, there is more to be gained in preparing for and mitigating the effects of natural hazards. Low-cost easily deployable sensors will make sensor technology more popular and easier for communities to utilize and give them the ability to make decisions during natural hazards. LEWIS, a Low-cost Efficient Wireless Intelligent Sensor platform, is created by the Smart Management of Infrastructure Laboratory (SMILab) at the University of New Mexico (UNM) at Albuquerque for such a purpose: to give communities the ability to create innovative monitoring solutions, including combating climate change. This paper briefly discusses the LEWIS platform, their use for communities to combat natural hazards to make quick decisions to improve public safety, training and education components, and community (from student to industry professional) engagement efforts. 

This paper addresses the need for infrastructure protection in Ohkay Owingeh, a tribal community located in a high desert region with a pronounced monsoon season. The extended dry period of 8-9 months makes the area susceptible to flooding during the monsoon season, leading to significant disruptions in transportation, infrastructure damage, and the displacement of tribal members. To mitigate these challenges, the adoption of smart sensing sonar LEWIS technology is proposed. The LEWIS sonar system will enable the detection of flood activity by measuring water level fluctuations. This valuable information will provide tribal members with an alert system to monitor and respond to flood events promptly. Moreover, the data gathered by the LEWIS Sonar will empower the tribal community of Ohkay Owingeh to take control of the current situation and make informed decisions for future flood prevention measures. 

Abstract In‐field visual inspections have inherent challenges associated with humans such as low accuracy, excessive cost and time, and safety. To overcome these barriers, researchers and industry leaders have developed image‐based methods for automatic structural crack detection. More recently, researchers have proposed using augmented reality (AR) to interface human visual inspection with automatic image‐based crack detection. However, to date, AR crack detection is limited because: (1) it is not available in real time and (2) it requires an external processing device. This paper describes a new AR methodology that addresses both problems enabling a standalone real‐time crack detection system for field inspection. A Canny algorithm is transformed into the single‐dimensional mathematical environment of the AR headset digital platform. Then, the algorithm is simplified based on the limited headset processing capacity toward lower processing time. The test of the AR crack‐detection method eliminates AR image‐processing dependence on external processors and has practical real‐time image‐processing.