Kennedy, DanielGuzina, BojanLabuz, Joseph2022-12-122022-12-122022-08https://hdl.handle.net/11299/250146The goal of the project is to establish a non-destructive field testing technique, including a data analysis algorithm, for determining in-place pile lengths by way of seismic waves. The length of each pile supporting a high-mast light tower (HMLT) will be identified through a systematic sensing approach that includes (i) collection and classification of the pertinent foundation designs and soil conditions; (ii) use of ground vibration waveforms captured by a seismic cone penetrometer; (iii) three-dimensional visco-elastodynamic finite element analysis (FEA) used as a tool to relate the sensory data to in situ pile length; (iv) use of machine learning (ML) algorithms, trained with the outputs of FEA simulations, to solve the germane inverse problem; (v) HMLT field testing; and (vi) analysis-driven data interpretation. Several hundred HMLTs throughout Minnesota have foundation systems, typically concrete-filled steel pipe piles or steel H-piles, with no construction documentation (e.g., pile lengths). Reviews of designs within current standards suggest that some of these foundations may have insufficient uplift capacity in the event of peak wind loads. Without knowledge of the in situ pile length, an expensive retrofit or replacement program would need to be conducted. Thus, developing a screening tool to determine in situ pile length - as compared to a bulk retrofit of all towers with unknown foundations - would provide significant cost savings.enNondestructive testsPiles (Supports)DepthMachine learningHigh mast lightingReport