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The development of data processing algorithms that enhance pattern detectability for civil infrastructure systems exposed to the environment is critical in various monitoring applications for construction, operation, maintenance, and hazard detection. For example, the precise detection of snow/ice forming on road pavement surface is essential for transportation safety. Another example is monitoring precipitation effects for the structural safety of retaining walls. Ad hoc analysis of streamed data involves processing complicated, non-stationary and nonlinear multi-physics behaviors of coupled interactions between civil systems and various surrounding factors. However, it is sometimes impossible to measure all the significant factors that influence the system’s behavior. In addition, monitoring costs can be exorbitant, limiting the amount of resources used. Therefore, the modeling of these coupled interactions is usually very difficult. The Auto Modulating Pattern Detection Algorithm (AMP) is a novel data processing algorithm that extends the original EMD-HHT method to detect a “small” but important intermittent event of interest that is usually masked by “dominant” environmental disturbances in various monitoring applications. With AMP, higher detectability can be achieved by: (1) amplifying the amplitude of the pattern-changing event’s frequency characteristics in the time-frequency domain, (2) reducing the baseline frequency fluctuation in the time-frequency domain, and (3) increasing the temporal resolution of the energy-time-frequency domain signal. This study demonstrates AMP’s applicability to various monitoring applications in operation and maintenance: monitoring structural safety for retaining walls and monitoring meteorological hazards on road pavement surface under field conditions for traffic safety.
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