Maintenance of wind turbine towers is currently a manual process that requires visual inspection and bolt tightening yearly. This process is costly to energy companies and its necessity is not well-defined. In this study, two Rayleigh-based distributed fiber optic sensing technologies are evaluated and compared for their ability to monitor the dynamic structural behavior of a model wind turbine tower subject to free and forced vibration. They are further tested for their ability to detect structural phenomena associated with loose bolts and material damage within the tower. The two technologies examined are optical frequency domain reflectometry (OFDR) and phase-based optical time domain reflectometry ($$\phi$$ ϕ -OTDR), which is a technology used in distributed acoustic sensing (DAS). OFDR is a tested and proven strain measurement technology commonly used for structural health monitoring but can only make strain measurements over short distances (10 s of meters). OFDR was used to validate the measurements made with $$\phi$$ ϕ -OTDR which can measure over much longer distances (several kilometers). Due to its sensing distance capability, $$\phi$$ ϕ -OTDR is a promising technology for monitoring many wind turbines networked together with a single fiber optic cable. This study presents a first-of-its-kind use of $$\phi$$ ϕ -OTDR for structural health monitoring to demonstrate its capabilities.

Distributed acoustic sensing (DAS) is a new technology that is being adopted widely in the geophysics and earth science communities to measure seismic signals propagating over 10's of kilometers using an optical fiber. DAS uses the technique of phase-coherent optical time domain reflectometry (φ-OTDR) to measure dynamic strain in an optical fiber as small as nε by examining interferences in scattered light. This technology is opening a new field of research of examining very small strains in infrastructure that can be indicative of performance and use level. In this study, a fiber optic strain sensing cable was embedded into an asphalt concrete test road and spatially distributed dynamic road strain was measured during different types of loading. It is demonstrated that φ-OTDR can be used to quantitatively measure strain in roads associated with events as small as a dog walking on the surface. Optical frequency domain reflectometry (OFDR), a widely implemented but less accurate distributed fiber optic strain monitoring technology, is used along with traditional pavement strain gauges and 3D finite element modeling to validate the φ-OTDR pavement strain measurements. After validation, φ-OTDR strain measurements from various events are presented including a vehicle, pedestrian, runner, cyclist and finally a dog moving along the road. This study serves to demonstrate the deployment of φ-OTDR to monitor roadway systems.

Distributed acoustic sensing (DAS) is a rapidly expanding tool to sense wave propagation and system deformations in many engineering applications. In terms of site characterization, DAS presents the ability to make static and dynamic strain measurements at scales (e.g., kilometers) and spatial resolutions (e.g., meters) that were previously unattainable with traditional measurement technologies. In this study, we rigorously assess the potential for extracting high-resolution, multi-mode surface wave dispersion data from DAS measurements using active-source multichannel analysis of surface waves (MASW). We have utilized both highly-controlled, broadband vibroseis shaker trucks and more-variable, narrow-band sledgehammer sources to excite the near surface, and compare the DAS-derived dispersion data obtained from both source types directly with concurrently acquired traditional geophone-derived dispersion data. We find that the differences between the two sensing approaches (i.e., DAS and geophones) are minimal and well within the dispersion uncertainty bounds associated with each individual measurement type when the following conditions are met for DAS: (a) a tight-buffered or strain-sensing fiber-optic cable is used, (b) the cable is buried in a shallow trench to enhance coupling, and (c) short gauge lengths and small channel separations are used. We also show that frequency-dependent normalization of the dispersion image following MASW processing removes the effects of scaling, integration, and differentiation on the measured waveforms, thereby allowing nearly identical dispersion data to be extracted from geophone waveforms (proportional to velocity) and DAS waveforms (proportional to strain) without requiring them to first be converted into equivalent units. We provide evidence that the short wavelength (high frequency) DAS dispersion measurements are limited by both the gauge length and the more commonly considered channel separation. We further show that it is possible to extract essentially equivalent surface wave dispersion data from seismic measurements made using a traditional geophone array or two different DAS cables. Finally, we show that shear wave velocity profiles recovered from the DAS data using an uncertainty-consistent, multi-mode inversion agree favorably with cone penetration tests performed at the site. This study demonstrates that DAS, when appropriate considerations are made, can be used in-lieu of traditional sensors (i.e., geophones) for making high-resolution, multi-mode measurements of surface wave dispersion data using the MASW technique.

Green infrastructure is a stormwater management technique that can be used to mitigate urban floods and heat islands. However, proactive monitoring and control is required to ensure its smooth operation. In particular, determining evapotranspiration, an essential process in biosphere-atmosphere interactions in cities that maintain cultivated and irrigated landscapes, is challenging. Understanding activities that govern evapotranspiration in a wide range of shallow soils is useful for planning and operation of green spaces. Recently, distributed fiber-optic sensors for monitoring civil structures and infrastructure have opened up new possibilities compared with conventional sensor systems. They operate based on the principle that strain variation in the soil is linked to environmental factors such as temperature and soil moisture changes. In this research, we examined the relationship between strain and temperature/soil moisture changes. By embedding fiber-optic strain sensors and other sensors in the soil tank, we investigated the feasibility of the sensors in a simulated soil environment.

Full waveform inversion (FWI) and distributed acoustic sensing (DAS) are powerful tools with potential to improve how seismic site characterization is performed. FWI is able to provide true 2D or 3D images of the subsurface by inverting stress wave recordings collected over a wide variety of scales. DAS can be used to efficiently collect high-resolution stress wave recordings from long and complex fiber optic arrays and is well-suited for large-scale site characterization projects. Due to the relative novelty of combining FWI and DAS, there is presently little published literature regarding the application of FWI to DAS data for near-surface (depths < 30 m) site characterization. We perform 2D FWI on DAS data collected at a well-characterized site using four different, site-specific 1D and 2D starting models. We discuss the unique benefits and challenges associated with inverting DAS data compared to traditional geophone data. We examine the impacts of using the various starting models on the final 2D subsurface images. We demonstrate that while the inversions performed using all four starting models are able to fit the major features of the DAS waveforms with similar misfit values, the final subsurface images can be quite different from one another at depths greater than about 10 m. As such, the best representation(s) of the subsurface are evaluated based on: (1) their agreement with borehole lithology logs that were not used in the development of the starting models, and (2) consistency at shallow depths between the final inverted images derived from multiple starting models. Our results demonstrate that FWI applied to DAS data has significant potential as a tool for near-surface site characterization while also emphasizing the significant impact that starting model selection can have on FWI results.