Browsing by Author "Kennedy, Daniel"
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Item Detecting Foundation Pile Length of High-Mast Light Towers(Minnesota Department of Transportation, 2022-08) Kennedy, Daniel; Guzina, Bojan; Labuz, JosephThe 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.Item Nondestructive detection of foundation pile embedment depths for high mast light towers(2022-11) Kennedy, DanielRecords of pile lengths are not available for several hundred high mast light towers (HMLTs) throughout Minnesota. The foundation systems, typically steel H-piles or concrete-filled steel pipe piles connected to a triangular concrete pile cap, risk overturning in the event of peak wind loadings if the foundation piles are not sufficiently deep to provide the designed uplift capacity. Without prior knowledge of the in situ pile lengths, an expensive tower foundation replacement effort would need to be undertaken. However, the development of a non-destructive screening tool to determine the in situ pile length – compared to replacing or retrofitting all towers with unknown foundation geometries – would provide significant cost savings. The aim of the research is to establish non-destructive field-testing techniques, including data analysis algorithms, for determining in situ pile embedment depths by way of seismic waves. The embedment depth of each pile supporting an HMLT is 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 (SCP); (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. In principle, the scarcity of existing HMLT configurations with known pile depth creates an absence of field data needed to adequately train an ML algorithm. However, this work demonstrates that the use of FEA simulations as proxy training data for the ML algorithms leads to a robust data interpretation scheme.