James Martin

Applications that involve subsurface characterization and monitoring such as landslide detection or geotechnical condition monitoring typically require continuous and high-quality geoscience data to be collected from remote areas where sampling frequency and power consumption pose major challenges. New advanced platforms are required which seamlessly blend accuracy, affordability, and practicality in sensor data collection and transfer. Here we introduce and evaluate an agnostic platform, tailored for proficient data acquisition via its 4-channel differential analog-to-digital (ADC) converters, thereby enabling the integration of four sensors with differential outputs for comprehensive data collection. Utilizing the ESP32 Wemos board as its cornerstone CPU, the platform presents a dual-faceted capability to efficiently stream geoscience data to the cloud where signal strength is sufficient, safeguarding data integrity and providing near-real-time accessibility, and to store it locally when the site does not allow connectivity. Surpassing current systems, this platform delivers a sampling rate of 3.2 kHz samples per second (800 samples per second per channel) and provides a robust bandwidth response critical for geotechnical monitoring, while maintaining a highly cost-effective production framework for scalable monitoring. The platform’s utility is validated through its application in subsurface ground characterization, employing the Nakamura method to determine initial layer depths and then monitoring through time to analyze property changes related to ground saturation, a primary catalyst for slope instabilities. The findings demonstrate the devised platform as a potent, economically viable tool, promising substantial applicability across various research domains necessitating precise data capture and transmission.

The formation of shaped charge jets is traditionally studied experimentally using flash X-ray and high-speed camera images. Sequential images obtained through these methods provide insight into the overall properties of the jet, such as jet tip velocity and outline shape. Numerical methods are validated using experimental results of the macroscopic properties by taking absolute velocity contours through the jet and comparing the outline shape. These methods do not describe where, and in which order, discrete parts of the shaped charge liner are located in the jet after formation. Shaped charge jet formation is studied here numerically by tracking the velocity in the flow field using stationary gauges and by placing dynamic gauges on the shaped charge liner. This method provides insight into the physical mechanism of jet formation. It is generally assumed that the shaped charge jet forms from liner inversion, where the liner tip becomes the jet tip that first penetrates the target. This work shows that the liner tip does not form part of the jet but is rather absorbed into the slug. Similarly, PER theory assumes that the outer part of the liner (next to the explosive) flows into the slug and the inner part flows into the jet. It is shown here that this assumption is not valid in all cases. This study is a proof of concept to highlight the advantages of using the method of simultaneous liner and flow field velocity tracking to aid shaped charge optimization activities.

Ground-penetrating radar (GPR) systems with multiconcurrent sampling receivers can rapidly acquire dense multioffset GPR data, which are not feasible using typical common-offset (CO) GPR systems with a single fixed offset transmitter-receiver pair. Multioffset GPR data from these new multiconcurrent receiver systems have the potential to be used to create detailed subsurface velocity models and enhanced reflection sections. These are important features that can improve qualitative and quantitative interpretation of GPR data. To realize these benefits and to deal with the large amount of multioffset data generated by these new systems, we have developed an automated and customized data processing workflow. There are three key algorithms that we have developed as part of our workflow, which is crucial for processing large volume, multioffset GPR data so as: first, to efficiently correct and manage time misalignments from multiconcurrent receivers; second, to carry out trace balancing of common-midpoint data for semblance analysis; and third, to automate the velocity analysis step. We showcase our processing workflow using two field data sets acquired using a multiconcurrent sampling receiver GPR system consisting of one transmitter and seven receivers. The field data were collected at two different locations: a site using a system with a 500 MHz center frequency and another site using a system with a 1000 MHz center frequency. We have determined, with both data sets, that our processing workflow could produce automated stacking velocity fields and enhanced zero-offset reflection cross sections. These benefits increase the information that can be used for interpretation (compared with conventional CO data) and can form the basis of further processing steps such as migration. As the cost of these multiconcurrent sampling receiver systems decreases over time, we anticipate their use, and the acquisition of dense multioffset GPR data, to become much more commonplace.

Rapid shallow landslides are a significant hillslope erosion mechanism and limited understanding of their initiation and development results in persistent risk to infrastructure. Here, we analyse the slope above the strategic A83 Rest and be Thankful road in the west of Scotland. An inventory of 70 landslides (2003–2020) shows three types of shallow landslide, debris flows, creep deformation, and debris falls. Debris flows dominate and account for 5,350 m³ (98%) of shallow-landslide source volume across the site. We use novel time-lapse vector tracking to detect and quantify slope instabilities, whilst seismometers demonstrate the potential for live detection and location of debris flows. Using on-slope rainfall data, we show that shallow-landslides are typically triggered by abrupt changes in the rainfall trend, characterised by high-intensity, long duration rainstorms, sometimes part of larger seasonal rainfall changes. We derive empirical antecedent precipitation (>62 mm) and intensity-duration (>10 h) thresholds over which shallow-landslides occur. Analysis shows the new thresholds are more effective at raising hazard alerts than the current management plan. The low-cost sensors provide vital notification of increasing hazard, the initiation of movement, and final failure. This approach offers considerable advances to support operational decision-making for infrastructure threatened by complex slope hazards.

Texture is required on road pavements for safe vehicle braking and manoeuvres. This paper provides a unique analysis of long-term texture obtained using traffic speed condition survey (TRACS) data from 14 sites, located along a north to south transect spanning the longest highway in the UK. A total of 19 years of sensor measured texture depth (SMTD) data have been analyzed using spatial filtering techniques and compared with meteorological and traffic datasets. The results for hot rolled asphalt (HRA) surfaces reveal that changes to SMTD follow a linearly increasing trend with time. The “rate of change” is influenced by the order of magnitude of annual average daily traffic (AADT), when factored for the percentage of heavy goods vehicles. This linear trend is disrupted by environmental parameters, such as rainfall events and seasonal conditioning. In the summer, this signal is evident as a transient peak in the “rate of change” of texture greater than 0.04 mm, and in the winter as a reduction. The transient changes in texture corresponded to above average rainfall occurring in the week prior to SMTD measurement. The signal observed demonstrates an inverse pattern to the classically understood seasonal variation of skid resistance in the UK, where values are low in the summer and high in the winter. The findings demonstrate for the first time that texture measurements experience a seasonal signal, and provide compelling evidence pointing toward surface processes (such as polishing and the wetting and drying of surface contaminants) causing changes to texture that are affecting seasonal variation in skid resistance.

Solution blow spinning (SBS) is gaining popularity for producing fibres for smart textiles and energy harvesting due to its operational simplicity and high throughput. The whole SBS process is significantly dependent on the characteristics of the attenuation force, i.e., compressed air. Although variation in the fibre morphology with varying air input pressure has been widely investigated, there is no available literature on the experimentally determined flow characteristics. Here, we have experimentally measured and calculated airflow parameters, namely, output air pressure and velocity in the nozzle wake at 12 different pressure values between 1 and 6 bar and 11 different positions (retracted 5 mm to 30 mm) along the centreline. The results obtained in this work will answer many critical questions about optimum protrusion length for the polymer solution syringe and approximate mean fibre diameter for polyvinylidene fluoride (PVDF) at given output air pressure and velocity. The highest output air pressure and velocity were achieved at a distance of 3–5 mm away from the nozzle wake and should be an ideal location for the apex of the polymer solution syringe. We achieved 250 nm PVDF fibres when output air pressure and velocity were 123 kPa and 387 m/s, respectively.

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.

Cracking in concrete structures can significantly affect their structural integrity and eventually lead to catastrophic failure if undetected. Recent advances in sensor technology for structural health monitoring techniques have led to the development of new and improved sensors for real-time detection and monitoring of cracks in various applications, from laboratory tests to large structures. In this study, triaxial accelerometers have been employed to detect and locate micro- and macrocrack formation in plain self-compacting concrete (SCC) and steel-fibre-reinforced SCC (SFRSCC) beams under three-point bending. Experiments were carried out with triaxial accelerometers mounted on the surface of the beams. The experimental results revealed that triaxial accelerometers could be used to identify the locations of cracks and provide a greater quantity of useful data for more accurate measurement and interpretation. The study sheds light on the structural monitoring capability of triaxial acceleration measurements for SFRSCC structural elements that can act as an early warning system for structural failure.

Identifying precursor events that allow the timely forecasting of landslides, thereby enabling risk reduction, is inherently difficult. Here we present a novel, low cost, flow visualization technique using time-lapsed imagery (TLI) that allows real time analysis of slope movement. This approach is applied to the Rest and Be Thankful slope, Argyle, Scotland, where past debris flows have blocked the A83 or forced preemptive closure. TLI of the Rest and Be Thankful are taken from a fixed station, 28 mm lens, time lapse camera every 15 min. Imagery is filtered to counter the effects of misalignment from wind induced vibration of the camera, asymmetric lighting, and fog. Particle image velocimetry (PIV) algorithms are then run to produce slope movement velocity vectors. PIV generated vectors are automatically post-processed to separate vectors generated by slope movement from false positives generated by harsh environmental conditions. Results for images over a 20-day period indicated precursor slope movement initiated by a rainfall event, a period of quiescence for 10 days, followed by a large landslide failure during proceeding rainfall where over 3000 tons of sediment reached the road. Results suggest low cost, live streamed TLI and this novel PIV approach correctly detect and, importantly, report precursor slope movement, allowing early warning, effective management and landslide impact mitigation. Future applications of this technique will allow the development of an effective decision-making tool for asset management of the A83, reducing the risk to life of motorists. The technique can also be applied to other critical infrastructure sites, allowing hazard risk reduction.

The Leidenfrost effect allows droplets to be transported on a virtually frictionless layer of vapor above a superheated substrate. The substrates are normally topographically structured using subtractive techniques to produce saw-tooth, herringbone, and other patterns and bulk heated, leading to significant challenges in energy consumption and controlled operation. Here, we propose a planar lithographic approach to levitate and propel droplets using temperature profiles, which can be spatially patterned and controlled in time. We show that micro-patterned electrodes can be heated and provide control of the pressure profile and the vapor flow. Using these almost featureless planar substrates, we achieve self-directed motion of droplets, with velocities of approximately 30 mms⁻¹, without topographically structuring the substrate or introducing physical walls. Our approach has the potential to be integrated into applications, such as digital microfluidics, where frictionless and contactless droplet transport may be advantageous.