Nondestructive detection of foundation pile embedment depths for high mast light towers

Abstract

Records 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.