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.
The issue of sustainable development in building engineering has been discussed since the early 90’s. The current research seeks to aid in this endeavor by reducing the heating and cooling loads on a building through its envelope, more specifically the wall material. The problem as viewed by most researchers is that the most common building materials, such as concrete and steel, allow for easy heat and mass transfer into buildings. Researchers now look to earth-based materials as passive building materials for increased thermal regulation. The building envelope of earth-based materials is an important buffer for heat and mass transfer into the building environment, but is a part of a bigger picture, which includes hygrothermal loads from the occupants and other facets of the indoor environment, as well as the mechanisms that regulate the indoor environment. This research looks at the soil-based building materials in different light. Our premise is that an understanding of the analogies between thermoregulatory systems in skin, plant, and soils, would inspire us to use soils as intelligent materials in stabilized earth construction with their pore geometries engineered based on these analogies. This biomimetic approach of developing “geodermis” can be broken into two smaller problems: (1) “sensory/nervous systems” to collect and process surrounding hygrothermal data, and (2) “motor system” for semi-active hygrothermal control with the combination of passive regulation by soil and active regulation based on the information from the sensory/nervous system. The Auto Modulating Pattern Detection Algorithm (AMP) is a novel bio-inspired model-free data processing technique that extends the Hilbert-Huang Transform 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.
Various identification methods are compared for full-scale nonlinear viscous dampers, including a parametric
approach using a simplified design model (SDM), the non-parametric Restoring Force Method (RFM), and the
non-parametric Artificial Neural Network (ANN) approach. Advantages and disadvantages of each method are
discussed for monitoring purposes. In the comparison, it is shown that the RFM is superior to other methods
in regard to the following aspects: (1) no assumption is needed on the nature of the monitored systems; (2)
the method is applicable to a wide range of nonlinear system types; (3) the same identification model can be
used for the unknown system changes, including the change of system type as well as the change of system
parameter values; and (4) physical interpretation of system changes are possible, using the identified values
of the series expansion coefficients. A set of experiments was also conducted using magneto-rheological (MR)
dampers to validate the feasibility of system change detection. For small changes in the magnetic field strength,
the corresponding changes in the dynamic characteristics of the MR damper were detected, using the identified
Monitoring chloride concentration and transport in concrete structures susceptible to corrosion of embedded steel reinforcement is a challenge as difficult as it is important. An embedded sensor based on nuclear magnetic resonance (NMR) would be a good solution to the problem because it would make a non-destructive atom-specific measurement of the presence and concentration of chloride. The important question is the scale of the device required to detect the chloride. Laboratory experiments to detect chloride in a cement matrix using pulse-NMR were conducted to assess the potential of this application; they provided a basis for projecting the scale of a device that would have a good chance of success. The coils were cm-scale and the magnetic field was 2.35 T. NMR signals were obtained from both aqueous chloride solution and samples of both regular and white portland cement. The experiments demonstrated that the signal-to-noise ratio (SNR) for a cm-scale cement sample volume is so small, even after averaging, that sample volumes much lower than that are unlikely to produce measurable signals at fields of 1 T or below. Thus the potential for realizing an embedded NMR-based sensor including the magnet is low. Parametric studies identify feasible alternative coil diameters and magnetic field strengths for detecting chloride ion concentrations in hardened concrete.