A nonparametric, data-driven methodology of monitoring for geotechnical structures subject to long-term environmental change is discussed. Avoiding physical assumptions or excessive simplification of the monitored structures, the nonparametric monitoring methodology presented in this paper provides reliable performance-related information particularly when the collection of sensor data is limited. For the validation of the nonparametric methodology, a field case study was performed using a full-scale retaining wall, which had been monitored for three years using three tilt gauges. Using the very limited sensor data, it is demonstrated that important performance-related information, such as drainage performance and sensor damage, could be disentangled from significant daily, seasonal and multiyear environmental variations. Extensive literature review on recent developments of parametric and nonparametric data processing techniques for geotechnical applications is also presented. 1. Introduction Restoring and improving urban infrastructure is recognized by the National Academy of Engineering as one of the fourteen grand challenges for engineering (NAE, [1]), and according to the 2009 ASCE Report Cards for Americas Civil Infrastructure, the current condition of U.S. infrastructure is rated “D” [2]. Aging civil infrastructure including bridges, levees, and dams in the US is calling for urgent measures focusing on maintenance, repair, and renovation. Geotechnical structures, compared to other types of civil infrastructure, are more vulnerable to nature and human-induced hazards. For example, Landslides in the Pacific Coast, the Rocky Mountains, the Appalachian Mountains, Hawaii, and Puerto Rico regions cause fatalities of 25 to 50 per year and direct/indirect economic losses up to $3 billion per year [3]. Structural health monitoring (SHM) is an emerging technique for the assessment of structural condition, hazards, and risks, consisting of three major components: sensing and instrumentation, data communication and archiving, and data analysis and interpretation. With the advent of todays powerful digital media and Internet, the needs for the first two components have been readily filled in many cases, but serious technical challenges still exist on the third component; how to process voluminous sensor data to obtain critical information for decision making? The research community is caught overwhelmed with the complex and extensive nature of field data associated with various factors of geotechnical phenomena. Some important challenges in processing field
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