Year

2021

Season

Fall

Paper Type

Master's Thesis

College

College of Computing, Engineering & Construction

Degree Name

Master of Science in Civil Engineering (MSCE)

Department

Engineering

NACO controlled Corporate Body

University of North Florida. School of Engineering

First Advisor

Raphael Crowley, Ph.D., P.E.

Second Advisor

Ryan Shamet, Ph.D.

Third Advisor

James Gelsleichter, Ph.D.

Department Chair

Osama Jadaan, Ph.D.

College Dean

William Klosermeyer, Ph.D.

Abstract

In the past several years, there has been increasing concern about anthropogenic noise generated during marine pile driving. This concern is expected to increase concomitantly with increases in waterfront construction efforts associated with aging infrastructure and sea level rise. Several guidelines are available to help predict underwater noise transmission due to pile driving, but the issue with all these methods is that they require one to measure sound pressure levels at one locus or more from the driven pile. In the context of marine construction, adding specifications for underwater noise collection may be expensive or difficult because contractors typically have little experience making such measurements. A better solution would be to utilize data that are already regularly collected during pile driving noise to predict underwater sound levels. This thesis focused on investigating whether such a method could be developed using Pile Driving Analyzer (PDA) data since PDA data are always collected prior to production scale driving during roadway construction in the state of Florida. PDA data are often also collected throughout pile driving activities on all drives during roadway construction. Sound data were collected using a hydrophone-equipped buoy system at various sites across the state of Florida. Once sound data were collected, correlations were developed between the sound data and data from the PDAs. Results appeared to indicate that a correlation appears to exist between sound-level data and PDA results. This would appear to indicate that development of a noise-prediction method using PDA data may be possible in the future.

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