7 Jan 2026

Evaluation of Seven Bridges at Fort Hunter Liggett, California, for Eligibility to the National Register of Historic Places

Abstract: The US Congress codified the National Historic Preservation Act of 1966 (NHPA), the nation’s most effective cultural resources legislation to date, mostly through establishing the National Register of Historic Places (NRHP). The NHPA requires federal agencies to address their cultural re-sources, which are defined as any prehistoric or historic district, site, building, structure, or object. Section 110 of the NHPA requires federal agencies to inventory and evaluate their cultural resources, and Section 106 requires them to determine the effect of federal undertakings on those potentially eligible for the NRHP. Fort Hunter Liggett is in central California, within Monterey County. It was first established as the Hunter Liggett Military Reservation in 1941. The post was renamed Fort Hunter Liggett in 1975. This report provides a determination of eligibility for the NRHP for seven properties (Bridges 749, 750, 753, 760, 767, 779, and 781) constructed between 1965 and 2010 and recommends that none are eligible under the NRHP and the California Register of Historic Resources (CRHR) criteria.

9 Sep 2024

Leveraging Artificial Intelligence and Machine Learning (AI/ML) for Levee Culvert Inspections in USACE Flood Control Systems (FCS)

Abstract: Levee inspections are essential in preventing flooding within populated regions. Risk assessments of structures are performed to identify potential failure modes to maintain the safety and health of the structure. The data collection and defect coding parts of the inspection process can be labor-intensive and time-consuming. The integration of machine learning (ML) and artificial intelligence (AI) techniques may increase accuracy of assessments and reduce time and cost. To develop a foundation for a fully autonomous inspection process, this research investigates methods to gather information for levees, structures, and culverts as well as methods to identify indicators of future failures using AI and ML techniques. Robotic plat-form and instrumentation options that can be used in the data collection process are also explored, and a platform-agnostic solution is proposed.