Dense Sensor Arrays Research Articles

Full-Field Modal Property Extraction from Motion Video Measurement: Considering Out-of-Plane Motions

Aging problems and potential damage of structures will increase the risks of structural collapse in the upcoming extreme loading events (i.e., earthquakes, hurricanes). Thus, structural health monitoring (SHM) is an essential technique to determine structural soundness through in-situ measurements. However, a successful SHM system requires a dense array of sensors that may be challenging in real-world applications. Alternatively, the structural response can be acquired through motion videos. This measurement method not only overcomes the disadvantages of the conventional SHM systems but also gives the opportunity to investigate more detailed dynamic behavior. In this study, a full-field modal property extraction method based on motion videos is proposed, in particular for out-of-plane motions. The measurements are first padded to reduce the distortion effect caused by the image pyramid. The responses are then compressed and decomposed into sub-bands using an image pyramid decomposition. The modal properties are subsequently extracted using the OMA approach known as frequency-domain stochastic subspace identification. The advantages and limitations of the proposed approach are illustrated numerically using a physics-based graphics model of a continuous beam. Subsequently, experimental validation is conducted for a 3-story model building using the Intel RealSenseTM D415 depth camera. Modal properties are shown to be determined quickly, with high accuracy and noise robustness.

Surface Gravity Wave Interferometry and Ocean Current Monitoring With Ocean‐Bottom DAS

AbstractThe cross‐correlation of a diffuse or random wavefield at two points has been demonstrated to recover an empirical estimate of the Green's function under a wide variety of source conditions. Over the past two decades, the practical development of this principle, termed ambient noise interferometry, has revolutionized the fields of seismology and acoustics. Yet, because of the spatial sparsity of conventional water column and seafloor instrumentation, such array‐based processing approaches have not been widely utilized in oceanography. Ocean‐bottom distributed acoustic sensing (OBDAS) repurposes pre‐existing optical fibers laid in seafloor cables as dense arrays of broadband strain sensors, which observe both seismic waves and ocean waves. The thousands of sensors in an OBDAS array make ambient noise interferometry of ocean waves straightforward for the first time. Here, we demonstrate the application of ambient noise interferometry to surface gravity waves observed on an OBDAS array near the Strait of Gibraltar. We focus particularly on a 3‐km segment of the array on the continental shelf, containing 300 channels at 10‐m spacing. By cross‐correlating the raw strain records, we compute empirical ocean surface gravity wave Green's functions for each pair of stations. We first apply beamforming to measure the time‐averaged dispersion relation along the cable. Then, we exploit the non‐reciprocity of waves propagating in a flow to recover the depth‐averaged current velocity as a function of time using a waveform stretching method. The result is a spatially continuous matrix of current velocity measurements with resolution <100 m and <1 hr.

Full-waveform centroid moment tensor inversion of passive seismic data acquired at the reservoir scale

SUMMARY Passive seismic inversion at the reservoir scale offers the advantages of low cost, negligible environmental impact and the ability to probe a target area with low-frequency energy not afforded by even the most modern active-source seismic technology. In order to build starting models suitable for full-waveform wave speed tomography, characterization of earthquake sources is an indispensable first step. We present a workflow for the centroid moment tensor (CMT) inversion of seismic events identified in a passive seismic data set acquired by a large and dense array of three-component broad-band seismic sensors in a mountainous setting in the Himalayan foothills. The data set comprised 256 instruments operating for 2×4 months over an area of 8000 km2. An initial 3-D wave speed model was determined for the region via the analysis of first-arriving traveltime picks. Of the 2607 identified seismic events that were well recorded at frequencies between 0.2–50 Hz, 86 with magnitudes 1.3 ≤ M ≤ 3.0 initially had their CMT focal mechanisms determined by a waveform fitting procedure built on a Green’s function approach in a 1-D layered average wave speed model, for stations within an offset of 10 km, in the frequency range 0.2–1.4 Hz. Here, we obtain updated CMT mechanisms for the 86 events in that catalogue via multicomponent full-waveform inversion in the 3-D wave speed model. Our workflow includes automated data- and model-driven data selection using a combination of different metrics derived from signal-to-noise considerations and waveform-fitting criteria, and relies upon spectral-element simulations of elastic wave propagation in the 3-D wave speed model, honouring topography. Starting from the initial CMT solutions, we seek improvement to the data fit within the frequency band 0.5–2.5 Hz by minimizing the waveform difference between observed and synthetic data, while accommodating wave speed-model errors by allowing for small time-shifts. We balance uneven data coverage and tune their contributions via data-space weighting functions. We quantify the improvements to the data fit in terms of different metrics. We summarize the changes to the CMT solutions, and present and analyse the resulting catalogue for the region, including their breakdown into double-couple and non-double couple components, and their relation to mapped faults.

Distributed acoustic sensing (DAS) for geomechanics characterization: A concise review

Microseismic/acoustic emission monitoring is a powerful tool for geomechanics characterization. Recent advances in fiber-optic sensing enable the acquisition of broadband seismic wavefields by exploiting the phenomenon of Rayleigh backscattering that occurs naturally in the fiber-optic. This new, relatively inexpensive technology—distributed acoustic sensing (DAS)—repurposes pre-existing fiber-optic cables (e.g., unused telecom cables or so-called dark fiber) or fit-for-purpose cables as dense seismo-acoustic sensor arrays. Recent studies have shown that DAS can be employed to monitor microseismic activities during hydraulic fracturing, quasi-static geomechanical strains induced by well injection, and acoustic emission signatures during laboratory slope failure. In this review, we first briefly describe the measurement principle of DAS. Then, we present a concise overview of the state-of-the-art progress in DAS-based geomechanics studies, with particular emphasis on geomechanical responses associated with hydrofracturing and shallow geohazard triggering. We further discuss the current challenges of this emerging technology, including low signal-to-noise ratio, single-component sensitivity, large data volume, spatial distance uncertainty, and amplitude response complexity. We conclude by suggesting that DAS coupled to dark fiber networks presents a unique opportunity to detect and monitor microseismic/acoustic emission events and that it offers a new possibility to investigate fine-scale geomechanical processes across multi-kilometer distances.

Empirical evaluation of the spatial scale and detection process of camera trap surveys

BackgroundCamera traps present a valuable tool for monitoring animals but detect species imperfectly. Occupancy models are frequently used to address this, but it is unclear what spatial scale the data represent. Although individual cameras monitor animal activity within a small target window in front of the device, many practitioners use these data to infer animal presence over larger, vaguely-defined areas. Animal movement is generally presumed to link these scales, but fine-scale heterogeneity in animal space use could disrupt this relationship.MethodsWe deployed cameras at 10 m intervals across a 0.6 ha forest plot to create an unprecedentedly dense sensor array that allows us to compare animal detections at these two scales. Using time-stamped camera detections we reconstructed fine-scale movement paths of four mammal species and characterized (a) how well animal use of a single camera represented use of the surrounding plot, (b) how well cameras detected animals, and (c) how these processes affected overall detection probability, p. We used these observations to parameterize simulations that test the performance of occupancy models in realistic scenarios.ResultsWe document two important aspects of animal movement and how it affects sampling with passive detectors. First, animal space use is heterogeneous at the camera-trap scale, and data from a single camera may poorly represent activity in its surroundings. Second, cameras frequently (14–71%) fail to record passing animals. Our simulations show how this heterogeneity can introduce unmodeled variation into detection probability, biasing occupancy estimates for species with low p.ConclusionsOccupancy or population estimates with camera traps could be improved by increasing camera reliability to reduce missed detections, adding covariates to model heterogeneity in p, or increasing the area sampled by each camera through different sampling designs or technologies.

Three-Dimensional Arterial Pulse Signal Acquisition in Time Domain Using Flexible Pressure-Sensor Dense Arrays.

In this study, we developed a radial artery pulse acquisition system based on finger-worn dense pressure sensor arrays to enable three-dimensional pulse signals acquisition. The finger-worn dense pressure-sensor arrays were fabricated by packaging 18 ultra-small MEMS pressure sensors (0.4 mm × 0.4 mm × 0.2 mm each) with a pitch of 0.65 mm on flexible printed circuit boards. Pulse signals are measured and recorded simultaneously when traditional Chinese medicine practitioners wear the arrays on the fingers while palpating the radial pulse. Given that the pitches are much smaller than the diameter of the human radial artery, three-dimensional pulse envelope images can be measured with the system, as can the width and the dynamic width of the pulse signals. Furthermore, the array has an effective span of 11.6 mm—3–5 times the diameter of the radial artery—which enables easy and accurate positioning of the sensor array on the radial artery. This study also outlines proposed methods for measuring the pulse width and dynamic pulse width. The dynamic pulse widths of three volunteers were measured, and the dynamic pulse width measurements were consistent with those obtained by color Doppler ultrasound. The pulse wave velocity can also be measured with the system by measuring the pulse transit time between the pulse signals at the brachial and radial arteries using the finger-worn sensor arrays.

A novel T-shaped sensor cluster for acoustic source localization

This study proposes a new sensor cluster configuration for localizing an acoustic source in a plate using uniform linear array beamforming and T-shaped sensor clusters. This technique requires neither the properties of the plate material nor a dense array of sensors to find the direction of arrival of the acoustic source. It functions by placing four sensors in a cluster in the shape of the letter “‘T” over a small region of the plate. Uniform linear array beamforming-based source localization is carried out by the constructive interference of different sensor signals. However, this approach has the disadvantage of a very low resolution when the direction of arrival approaches certain values. The L-shaped sensor clusters use the information from the time difference of arrival between the sensors to estimate the direction of arrival, which has a high resolution in all directions except for the direction that is very close to vertical to the cluster. In this study, we numerically and experimentally demonstrate that the proposed T-shaped sensor cluster can accurately localize the acoustic source with no blind area. We also detail its superior performance compared to both uniform linear array beamforming and L-shaped clusters. In the experimental investigation, the maximum deviation of impact source localization was reduced significantly, from 54° to 4° for an aluminum plate, and from 42° to 3° for a composite plate. Furthermore, this novel combined sensor array layout requires only a few sensors, which can significantly reduce the cost of structural health monitoring practice.

Preliminary assessment of ship detection and trajectory evaluation using distributed acoustic sensing on an optical fiber telecom cable.

Distributed acoustic sensing (DAS) is a recent instrumental approach allowing the conversion of fiber-optic cables into dense arrays of acoustic sensors. This technology is attractive in marine environments where instrumentation is difficult to implement. A promising application is the monitoring of environmental and anthropic noise, leveraging existing telecommunication cables on the seafloor. We assess the ability of DAS to monitor such noise using a 41.5 km-long cable offshore of Toulon, France, focusing on a known and localized source. We analyze the noise emitted by the same tanker cruising above the cable, first 5.8 km offshore in 85 m deep bathymetry, and then 20 km offshore, where the seafloor is at a depth of 2000 m. The spectral analysis, the Doppler shift, and the apparent velocity of the acoustic waves striking the fiber allow us to separate the ship radiated noise from other noise. At 85 m water depth, the signal-to-noise ratio is high, and the trajectory of the boat is recovered with beamforming analysis. At 2000 m water depth, although the acoustic signal of the ship is more attenuated, signals below 50 Hz are detected. These results confirm the potential of DAS applied to seafloor cables for remote monitoring of acoustic noise even at intermediate depth.

Seismic Observations of Four Thunderstorms Using an Underground Fiber-Optic Array

Abstract Thunderstorms are a common atmospheric phenomenon that cause abundant acoustic disturbances, which can interact with the ground surface, creating a link between atmospheric and solid Earth processes. This article reports seismological observations of four thunderstorms through the spring and summer of 2019, as recorded by the distributed acoustic sensing fiber-optic array (4.9 km) on the Penn State campus in State College, Pennsylvania. With a dense sensor array in the local region, we are able to construct the seismic full waveform response of the thunderstorm events (hereafter referred to as thunderquakes) and track the wave propagation across the array. We use a time-domain grid search to obtain the back azimuth and slowness of the waves, and a modified Geiger’s method to pinpoint source locations of the thunderquakes. Correlated with the time of the recorded signal, this data allows reconstruction of thunderstorm movement as well as offering measurements of the seismic velocity.

Large‐Scale Fracture Systems Are Permeable Pathways for Fault Activation During Hydraulic Fracturing

AbstractInduced seismicity due to fluid injection, including hydraulic fracturing, is an increasingly common phenomenon worldwide; yet, the mechanisms by which hydraulic fracturing causes fault activation remain unclear. Here we show that preexisting fracture networks are instrumental in transferring fluid pressures to larger faults on which dynamic rupture occurs. Studies of hydraulic fracturing‐induced seismicity in North America have often used observations from regional seismograph networks at distances of 10s of km, and as such lack the resolution to answer some of the key questions about triggering mechanisms. To carry out a more detailed analysis of the mechanisms of fault activation, we use data from a dense sensor array located at a hydraulic‐fracturing site in Alberta, Canada. The spatiotemporal distribution of event hypocenters, coupled with measurements of seismic anisotropy, reveal the presence of preexisting fracture corridors that allowed communication of fluid‐pressure perturbations to larger faults, over distances of 1 km or more. The presence of preexisting permeable fracture networks can significantly increase the volume of rock affected by the pore‐pressure increase, thereby increasing the probability of induced seismicity. This study demonstrates the importance of understanding the connectivity of preexisting natural fractures for assessing potential seismic hazards associated with hydraulic fracturing of shale formations and offers a detailed case exposition of induced seismicity due to hydraulic fracturing.

Deep Residual Networks for Sleep Posture Recognition With Unobtrusive Miniature Scale Smart Mat System.

Sleep posture, as a crucial index for sleep quality assessment, has been widely studied in sleep analysis. In this paper, an unobtrusive smart mat system based on a dense flexible sensor array and printed electrodes along with an algorithmic framework for sleep posture recognition is proposed. With the dense flexible sensor array, the system offers a comfortable and high-resolution solution for long-term pressure sensing. Meanwhile, compared to other methods, it reduces production costs and computational complexity with a smaller area of the mat and improves portability with fewer sensors. To distinguish the sleep posture, the algorithmic framework that includes preprocessing and Deep Residual Networks (ResNet) is developed. With the ResNet, the proposed system can omit the complex hand-crafted feature extraction process and provide compelling performance. The feasibility and reliability of the proposed system were evaluated on seventeen subjects. Experimental results exhibit that the accuracy of the short-term test is up to 95.08% and the overnight sleep study is up to 86.35% for four categories (supine, prone, right, and left) classification, which outperform the most of state-of-the-art studies. With the promising results, the proposed system showed great potential in applications like sleep studies, prevention of pressure ulcers, etc.

Fiber-Optic Seismology

Distributed acoustic sensing (DAS) is an emerging technology that repurposes a fiber-optic cable as a dense array of strain sensors. This technology repeatedly pings a fiber with laser pulses, measuring optical phase changes in Rayleigh backscattered light. DAS is beneficial for studies of fine-scale processes over multi-kilometer distances, long-term time-lapse monitoring, and deployment in logistically challenging areas (e.g., high temperatures, power limitations, land access barriers). These benefits have motivated a decade of applications in subsurface imaging and microseismicity monitoring for energy production and carbon sequestration. DAS arrays have recorded microearthquakes, regional earthquakes, teleseisms, and infrastructure signals. Analysis of these wavefields is enabling earthquake seismology where traditional sensors were sparse, as well as structural and near-surface seismology. These studies improved understanding of DAS instrument response through comparison with traditional seismometers. More recently, DAS has been used to study cryosphere systems, marine geophysics, geodesy, and volcanology. Further advancement of geoscience using DAS requires several community efforts related to instrument access, training, outreach, and cyberinfrastructure. ▪ DAS is a seismic acquisition technology repurposing fiber optics as arrays of dynamic strain sensors at 1- to 10-m spacing over kilometers. ▪ Easy DAS installations have availed time-lapse geophysical sensing in formerly inaccessible sites: urban, icy, and offshore areas. ▪ High-frequency wavefields recorded by DAS are analyzed with array-based methods to characterize seismic sources and image the subsurface. ▪ DAS has shown low-frequency sensitivity in the laboratory and field, for slow hydrodynamic and geodynamic processes.

Scalar Magnetometry Below 100 fT/Hz1/2 in a Microfabricated Cell.

Zero-field optically-pumped magnetometers are a room-temperature alternative to traditionally used super-conducting sensors detecting extremely weak magnetic fields. They offer certain advantages such as small size, flexible arrangement, reduced sensitivity in ambient fields offering the possibility for telemetry. Devices based on microfabricated technology are nowadays commercially available. The limited dynamic range and vector nature of the zero-field magnetometers restricts their use to environments heavily shielded against magnetic noise. Total-field (or scalar) magnetometers based on microfabricated cells have demonstrated subpicotesla sensitivities only recently. This work demonstrates a scalar magnetometer based on a single optical axis, 18 (3 × 3 × 2) mm3 microfabricated cell, with a noise floor of 70 fT/Hz1/2. The magnetometer operates in a large static magnetic field range, and and is based on a simple optical and electronic configuration that allows the development of dense sensor arrays. Different methods of magnetometer interrogation are demonstrated. The features of this magnetic field sensor hold promise for applications of miniature sensors in nonzero field environments such as unshielded magnetoencephalography (MEG) and brain-computer interfaces (BCI).

Influence of fiber-optic cable structure, sand density, and acoustic frequency on DAS amplitude response: Preliminary results

Distributed acoustic sensing (DAS) is an emerging fiber-optic technology that can turn tens of kilometers long fiber-optic cables into dense acoustic sensor arrays. This technology is expected to bring new opportunities for geologic hazard characterization and mitigation. The cable structure and installation method are two critical factors affecting the performance of a DAS system. Here, we report laboratory tests performed in a sandbox to evaluate the acoustic response of different types of cables at variable sand densities and acoustic frequencies. Our initial results indicated that complex cable packaging structures had unfavorable impacts on the DAS amplitude response whereas the influence of sand density was dependent on cable structure and acoustic frequency. Further tests with refined measurement setup will improve the understanding of the combined effects of cable type, medium density, and acoustic frequency on DAS recordings.

Quantifying discrete seismic responses to mining

Applying analysis techniques developed for naturally occurring earthquakes to mine seismicity is common practice; however, these methodologies rarely consider the influence of blasting on the dynamic rock mass failure processes observed in mines. Due to the complex nature of bulk orebody extraction at depth, quantifying discrete seismic responses to mining can be challenging. This paper identifies seismic responses to mining by pairing single-link clustering with finite temporal windows bound by mine blasting practices. A methodology is presented to quantify the space−time characteristics of these responses using four seismic response parameters (SRPs): distance to blast, distance to centroid, time after blast, and time between events. Using SRPs, seismic responses to mining can be quantitatively classified as induced, complex, or triggered (with respect to discrete mine blasting). Because these response parameters do not require an extensive and (or) triaxial dense sensor array, they are applicable to a variety of underground mining operations. In this work, SRPs are applied to 189 discrete seismic responses occurring over 2 months of active mining, and a 2 week shutdown period, at Agnico Eagle’s LaRonde Mine.

An inverse elastic method of crack identification based on sparse strain sensing sheet

Reliable damage detection over large areas of structures can be achieved by spatially quasi-continuous structural health monitoring enabled by two-dimensional sensing sheets. They contain dense arrays of short-gauge sensors, which increases the probability to have sensors in direct contact with damage (e.g. crack opening) and thus identify (i.e. detect, localize, and quantify) it at an early stage. This approach in damage identification is called direct sensing. Although the sensing sheet is a reliable and low-cost technology, the overall structural health monitoring system that is using it might become complex due to large number of sensors. Hence, intentional reduction in number of sensors might be desirable. In addition, malfunction of sensors can occur in real-life settings, which results in unintentional reduction in the number of functioning sensors. In both cases, reduction in the number of (functioning) sensors may lead to lack of performance of sensing sheet. Therefore, it is important to explore the performance of sparse arrays of sensors, in the cases where sensors are not necessarily in direct contact with damage (indirect sensing). The aim of this research is to create a method for optimizing the design of arrays of sensors, that is, to find the smallest number of sensors while maintaining a satisfactory reliability of crack detection and accuracy of damage localization and quantification. To achieve that goal, we first built a phase field finite element model of cracked structure verified by the analytical model to determine the crack existence (detection), and then we used the algorithm of inverse elastostatic problem combined with phase field finite element model to determine the crack length (quantification) and location (localization) by minimizing the difference between the sensor measurements and the phase field finite element model results. In addition, we experimentally validated the method by means of a reduced-scale laboratory test and assessed the accuracy and reliability of indirect sensing.

Portable Magnetometry for Detection of Biomagnetism in Ambient Environments

We present a method of optical magnetometry with parts-per-billion resolution that is able to detect biomagnetic signals generated from the human brain and heart in Earth's ambient environment. Our magnetically silent sensors measure the total magnetic field by detecting the free-precession frequency of highly spin-polarized alkali metal vapor. A first-order gradiometer is formed from two magnetometers that are separated by a 3 cm baseline. Our gradiometer operates from a laptop consuming 5 W over a USB port, enabled by state-of-the-art micro-fabricated alkali vapor cells, advanced thermal insulation, custom electronics, and laser packages within the sensor head. The gradiometer obtains a sensitivity of 16 fT/cm/Hz$^{1/2}$ outdoors, which we use to detect neuronal electrical currents and magnetic cardiography signals. Recording of neuronal magnetic fields is one of a few available methods for non-invasive functional brain imaging that usually requires extensive magnetic shielding and other infractructure. This work demonstrates the possibility of a dense array of portable biomagnetic sensors that are deployable in a variety of natural environments.

SkinSim: A Design and Simulation Tool for Robot Skin With Closed-Loop pHRI Controllers

The importance of tactile sensing for physical human–robot interaction (pHRI) and dexterous manipulation is well known. SkinSim is a novel simulation environment for tactile robot skins in which one can study design tradeoffs involved in deploying whole-body, dense sensor arrays. In this article, scalable modeling approaches are presented for simulating pressure-sensitive robot skin patches, with simultaneous consideration of sensing element geometry and mechanical structure, signal quality, data processing, and closed-loop force controller performance. The open-source simulation architecture of SkinSim is compatible with Gazebo and robot operating system (ROS) programming environments and supports both robot skin dynamic models, as well as tactile sensing element models. An experimentally validated force dispersion model was introduced for the simulation of sensors embedded in a mechanical damping layer. Simulation examples of robot skin with different tactile resolutions are presented using parameter values extracted from an experimental testbed. Thus, simulation results were experimentally validated and the skin sensor density impact on a simple pHRI controller performance was evaluated. Performance measures include center of pressure (COP) estimation error and control signal settling time, overshoot, and steady-state errors. Results suggest that while COP errors decrease in denser sensor skins, controller performance also deteriorates. Therefore, optimal robot skin designs will have to consider application-dependent tradeoffs. Similar results were confirmed in simulation with a larger skin patch containing approximately 4000 tactels and deployed on the end-effector of a collaborative mobile manipulator. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by the expensive and time-consuming process of designing tactile skin for robots. When designing pressure-sensitive whole-body sensor arrays for physical human–robot interaction, there are tradeoffs related to sensor resolution, accuracy, and response time, among others. Instead of building prototypes and evaluating them experimentally, the SkinSim simulation environment allows automatic testing of skins with simultaneous consideration of sensing element geometry and mechanical structure, signal quality, data processing, and closed-loop force controller performance. The user can specify several configurations to test and thereby explore the best tradeoffs before actually prototyping any hardware.

Eikonal Tomography Using Coherent Surface Waves Extracted From Ambient Noise by Iterative Matched Filtering—Application to the Large‐N Maupasacq Array

AbstractStandard ambient noise tomography relies on cross‐correlation of noise records between pairs of sensors to estimate empirical Green's functions. This approach is challenging if the distribution of noise sources is heterogeneous and can get computationally intensive for large‐N seismic arrays. Here, we propose an iterative matched filtering method to isolate and extract coherent wave fronts that travel across a dense array of seismic sensors. The method can separate interfering wave trains coming from different directions, to provide amplitude and travel time fields for each detected wave front. We use the eikonal equation to derive phase velocity maps from the gradient of these travel time fields. Artifacts originating from scattered waves are removed by azimuthal averaging and spatial smoothing. The method is validated on a synthetic test and then applied to the data of the Maupasacq experiment. Rayleigh wave phase velocity maps are obtained for periods between 2 and 9 s. These maps correlate with surface geology at short period (T&lt;3 s) and reveal the deep architecture of the Arzacq and Mauleon basins at longer periods (T&gt;4 s).

Large-Area Resistive Strain Sensing Sheet for Structural Health Monitoring.

Damage significantly influences response of a strain sensor only if it occurs in the proximity of the sensor. Thus, two-dimensional (2D) sensing sheets covering large areas offer reliable early-stage damage detection for structural health monitoring (SHM) applications. This paper presents a scalable sensing sheet design consisting of a dense array of thin-film resistive strain sensors. The sensing sheet is fabricated using flexible printed circuit board (Flex-PCB) manufacturing process which enables low-cost and high-volume sensors that can cover large areas. The lab tests on an aluminum beam showed the sheet has a gauge factor of 2.1 and has a low drift of 1.5 . The field test on a pedestrian bridge showed the sheet is sensitive enough to track strain induced by the bridge’s temperature variations. The strain measured by the sheet had a root-mean-square (RMS) error of 7 compared to a reference strain on the surface, extrapolated from fiber-optic sensors embedded within the bridge structure. The field tests on an existing crack showed that the sensing sheet can track the early-stage damage growth, where it sensed 600 peak strain, whereas the nearby sensors on a damage-free surface did not observe significant strain change.