Open AccessArticle

Evaluation of Proximity Sensors Applied to Local Pier Scouring Experiments

Pao-Ya Wu

Dong-Sin Shih

and

Keh-Chia Yeh

Department of Civil Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan

Author to whom correspondence should be addressed.

Water 2024, 16(24), 3659; https://doi.org/10.3390/w16243659

Submission received: 20 October 2024 / Revised: 16 November 2024 / Accepted: 16 December 2024 / Published: 19 December 2024

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Figure 1
VCNL4200 detailed block diagram. "> Figure 2
Local scour depth measuring instruments. "> Figure 3
Customized PCB board for a sensor group with 8 VCNL4200 sensors. "> Figure 4
Rotary mechanism for 8-dimension measurement. "> Figure 5
Cloud-based monitor framework. "> Figure 6
Schematic drawing of the flume. "> Figure 7
Distance PS data vs. data derived from 179 results. "> Figure 8
Scour hole during live-bed scour test. "> Figure 9
Time history plot of PS data (test 10). Note: PS_12~PS_15 and S_0~PS_3 are not shown in <a href="#water-16-03659-f009" class="html-fig">Figure 9</a> because their positions are out of the bed or scour hole. "> Figure 10
Time history plot of PS data (test 13). "> Figure 10 Cont.
Time history plot of PS data (test 13). "> Figure 11
Time history plot by PS data at position 0 vs. 4 (front vs. back of the pier). "> Figure 12
Contours of scour depths (tests 10 and 13). "> Figure 13
(a) Normalized scour depth (dse/D) versus flow intensity (V/Vc) from Sheppard and William (2006); (b) measured (dss/D) and proximity sensor (dse/D) versus V/Vc, note: There are only 9 points in (b) due to 3 pairs of measured (dss/D) (tests 1 and 2, 3 and 7, 9 and 12). "> Figure 14
Contours of scour depths with time from proximity sensors (tests 10 and 13). "> Figure 14 Cont.
Contours of scour depths with time from proximity sensors (tests 10 and 13). ">

Versions Notes

Abstract

Most pier scour monitoring methods cannot be carried out during floods, and data cannot be recorded in real-time. Since scour holes are often refilled by sediment after floods, the maximum scour depth may not be accurately recorded, making it difficult to derive the equilibrium scour depth. This study proposes a novel approach using 16 proximity sensors (VCNL4200), which are low-cost (less than USD 3 each) and low-power (380 µA in standby current mode), to monitor and record the pier scour depth at eight different positions in a flume as it varies with water flow rate. Based on the regression relationship between PS data and distance, the scour trend related to the equilibrium scour depth can be derived. Through the results of 13 local live-bed sediment scour experiments, this PS module was able to record not only the scour depth, but also the development and geometry of the scour under different water flows. Additionally, based on PS data readings, changes in the topography of the scour hole throughout the entire scouring process can be observed and recorded. Since the maximum scour depth can be accurately recorded and the scour trend can be used to estimate the equilibrium scour depth, observations from the experimental results suggest that the critical velocity derived by Melville and Coleman (2000) may have been underestimated. The experimental results have verified that, beyond achieving centimeter-level accuracy, this method also leverages the Internet of Things (IoT) for the long-term real-time observation, measurement, and recording of the formation, changes, and size of scour pits. In addition to further exploring scouring behavior in laboratory studies, this method is feasible and highly promising for future applications in on-site scour monitoring due to its simplicity and low cost. In future on-site applications, it is believed that the safety of bridge piers can be assessed more economically, precisely, and effectively.

Keywords:

proximity sensors; scour; scour monitoring; hydraulic and sediment transport model

1. Introduction

The ability to estimate scour depth is a critical issue for bridge safety. A bridge pier, which is an obstruction to the approaching flow, causes a three-dimensional separation of flow, forming a vortex flow and a periodical vortex shedding downstream of the pier. Laboratory experiments have been conducted by many researchers to explore the relationship between scour depth and its influencing factors using prediction models (e.g., [1,2,3,4,5,6,7]). For a normalized equilibrium, local scour depth d_se/D, in which D is the pier diameter, for steady flow with uniform diameter and cohesionless sediment, can be expressed as a function of three dimensionless quantities: (1) flow intensity, V/V_c (upstream depth-averaged velocity divided by the critical depth-averaged velocity for the movement of bed material); (2) aspect ratio, y₀/D (water depth divided by pier diameter); and (3) pier diameter divided by the median sediment grain size, D/D₅₀. To measure the depth of bridge scour varying with floods, many researchers have proposed monitoring techniques during the service of a bridge. According to Wang et al. [8], these monitoring methods can be classified into three major categories by different objects in the measurement: reference target monitoring, soil–water interface monitoring, and structure monitoring. The advantages and disadvantages are summarized in Table 1. Since most monitoring methods cannot be carried out during floods, the data cannot be recorded continuously, and the scour holes tend to be re-filled by sediments after floods, so the maximum scour depth may not be accurately recorded, and the equilibrium scour depth cannot be derived. Although the bridge pier scour issue has been intensively studied, it is still necessary to enhance our understanding of this complex and nearly unexplored situation [9]. Therefore, this study proposes the use of proximity sensors to measure scour around bridge piers in order to explore this complex situation.

Proximity sensors were originally designed for personal electronic devices and presence detection in buildings. Matos et al. [10] used sensors for the continuous monitoring of turbidity and suspended particulate matter in marine environments by developing an automated sensor for the in situ the continuous monitoring of the streambed sediment height of a waterway. The capacitive sensors may be restricted to a short detection distance and suffer imperfect measurement accuracy, small interaction and location space, and disturbance from the surroundings [11]. However, the literature indicates that proximity sensors are integral to hydraulic monitoring, particularly in applications involving valve position detection and the assessment of hydraulic system conditions. Moreover, these sensors contribute to the reliability and efficiency of hydraulic systems by enabling real-time monitoring, facilitating timely maintenance actions, electrochemical biosensors, and the usage of wearable devices in detecting the issue in terms of healthcare, such as curing, monitoring, and detection of disease [12,13,14,15,16]. The use of sensors for monitoring bridge abutment scour is indeed widely applied in environmental monitoring, such as depth-sensing LiDAR. However, depth-sensing LiDAR is not inexpensive, typically costing over USD 3000 per unit, and it is prone to damage in harsh bridge scour environments, making it less economical. Although sensors have been widely applied in hydraulic scour monitoring, there are very few technologies capable of detecting scour with a resolution better than 10 cm while simultaneously distinguishing between redeposited soil and saturated soil. Currently, only dielectric probes are used in early-warning systems because they can record data during extreme events [17]. Therefore, this study attempts to apply a low-cost proximity sensor, the VCNL4200, which is a low-cost (less than USD 3each) and fully integrated proximity and ambient light sensor. Composed of 16 proximity sensors, with a maximum measuring length of 117.5 mm and a proximity distance of up to 1.5 m (in the air), it has the capability to record 8 positions of live-bed pier scour depth. By the results of 13 local live-bed sediment scour experiments, the PS module could record not only the depth of scour but also the process of scour development and the geometry of the scour hole at different water flows. Moreover, the maximum scour depth could accurately be recorded and the scour trend derived to estimate the equilibrium scour depth. Due to the low cost of these sensors and their ability to connect to the IoT, the proposed real-time PS monitoring system not only has potential applications in real-time observation and data analysis in the laboratory, but also could be further applied to on-site cases in the future.

2. Methods

2.1. Instrument Design

The scour monitoring module relies on the VCNL4200 long-range proximity sensor [18], which consists of a 940 nm infrared-emitting diode and photodiode receiver housing associated electronics in a small integrated circuit (IC) package (Figure 1). The VCNL4200 is a low-cost (less than USD 3 each) and fully integrated proximity and ambient light sensor. It combines an infrared emitter and photodiode for proximity measurement and integrates signal processing IC to enable data readout through a standard I2C bus serial digital interface. This stand-alone component simplifies the use and design of a PS in consumer and industrial applications, because the embedded IRED (infrared) and photodiode are exactly matched to each other. Table 2 shows the electrical specifications of VCNL 4200.

The PS module in this paper consists of 16 VCNL4200 proximity sensors (shown in Figure 2) placed in a vertical direction to monitor the scour change at different heights. Through proximity sensor readout, the distance from the scour sidewall to the PVC pipe at the sensor height can be monitored instantly.

As the scour depth change is relatively small, within a few centimeters in scale in the flume, proximity sensors need to be placed close enough to achieve the desired resolution. Hence, a customized PCB (printed circuit board) was developed to pack 16 proximity sensors inside a 15 cm length sensor module. The 16 proximity sensors were separated into two groups (Figure 3). Within each group, the sensor space was 0.75 cm. Between the two groups, the sensor space was 1.25 cm (Figure 2).

A rotary mechanical structure was designed to enable the PS module to measure the scour sidewall surface at an arbitrary angle and driven by a stepper motor. In this study, the sensor rotator will spin continuously during the experiments with a speed of 4 rpm. For each revolution, 8 measures were taken for 8 dimensions separated by 45 degrees (Figure 4).

Considering a future field study with sizable sensors set up at different locations, a cloud-based monitor framework has been developed, including a sensor controller and cloud server. An ESP32-based development board is used to control the PS module and stepper motor of the sensor rotator (Figure 5). The monitoring task can be triggered remotely through a web interface and the measured data instantly transferred to the cloud server. The ESP32 was chosen due to its maturity and popularity for IoT usage. The cloud server consists of several key components:

Grafana: An open-source web framework to allow users to query and visualize the stored data with flexible dashboards.
Influxdb: An open-source database engine optimized for time series data.
Mosquitto: An open-source message broker that implements the MQTT protocol, which has been used for data communication between cloud servers and remote sensor controllers.

2.2. Experimental Set Up

The maximum scour depth under clear-water conditions is independent of D/D₅₀ for values larger than 25 [19,20], and local scour depth is influenced by sediment size when D/D₅₀ > 50 [21]. The reduction in equilibrium scour depth with increasing values of D/D₅₀ has a major impact on scour depth predictions for larger structures in relatively fine sediments [22]. Therefore, in this experiment, using one uniform cohesionless sediment diameter and a circular pile with a diameter of 0.048 m, local clear-water and live-bed scour tests were performed for three water depths (0.07 m, 0.1 m, 0.14 m) and flow velocities, with velocity ratio (V/V_c) values as high as 3. This is near the velocity where the peak live-bed scour occurs for the sediment and flow conditions. The movable bed was filled with uniform cohesionless sand with median size D₅₀ = 0.53 mm, and D/D₅₀ = 90.6. The size distributions of sediments were nearly uniform with standard deviations (

σ = \sqrt{{D_{84} / D}_{16}}

) of 1.5. The circular pier with the proximity sensors was located 6.0 m downstream of the flume entrance. The proximity sensors monitored and recorded 8 positions of live-bed pier scour depth varying with water flow rate. A plan and side drawing of the flume used for these experiments is shown in Figure 6. The experiments were conducted in a tilting flume (0.6 m wide, 0.7 m deep, and 15 m long) located at the River Sediment Transport Laboratory at National Yang Ming Chiao Tung University, Taiwan.

The instrumentation used in this study is as below:

Electromagnetic flow meter: Before the tests, numerous flow discharge values were measured and integrated to determine the flow discharge as a function of pump rpm. During the tests, the sectional averaged velocity was calculated from the pump rpm and water depth at the section. To verify the flow velocity, an electromagnetic flow meter was set up at 1.0 m upstream of the pier model to measure flow velocity.
Laser rangefinder: Measures flow bed before the tests and scour depth after the tests.
Camera: The remote monitoring Mi camera was used to monitor the process of the scour hole in the flow.
Pier model: A transparent PVC pipe with 4.8 cm diameter was used to emulate the bridge pier. A PS module was installed inside the PVC pipe to monitor the scour development during the experiment.
Proximity sensor (PS): A PS module consists of 16 VCNL4200 proximity sensors was placed in a vertical direction to monitor the scour change in different heights.
A dedicated PCB (printed circuit board) was developed to pack 16 proximity sensors inside a 15 cm length sensor module. Sixteen proximity sensors were separated into two groups. Within a group, the sensor space is 0.75 cm. Between two groups, the sensor space is 1.25 cm.
Sensor rotator: In order to monitor 3D scour surface, a rotary mechanical structure had been designed to enable the PS module to measure the scour sidewall surface at an arbitrary angle and driven by a stepper motor. In this study, the sensor rotator would spin continuously during experiments with the speed of 4 rpm. For each revolution, 8 measurements were taken for 8 different dimensions separated by 45 degrees.

2.3. Experimental Procedure

The experimental procedure is outlined below:

Install the pier model in the flume and compact and level the bed.
Assemble optical sensors in the pier model. Take photographs and check all instrumentation.
Turn on the water injection pump and inject water into the flume slowly until the test depth is reached. Level the bed again and check that all the values of the optical sensors are the same.
Adjust the rotating speed of the gate. Measure the velocity and water depth and monitor bed forms during the live-bed tests.
In the end of experiment T, turn off the pump and remove the optical sensors. Take post-experiment photographs and measure the scour hole with the laser rangefinder.

3. Results and Discussion

Before running the experiments, serval pre-procedures need to be prepared, for instance, to obtain the grain size distribution curve, the water level–flow rate curve, and the correlations between the horizontal distance of the scour hole (distance) versus the value of the PS. Three tests were applied to measure the horizontal distance between the pier and the scour hole with the laser rangefinder before the live-bed local pier scour experiments were performed. The data presented in Figure 7 were obtained from 179 test results. They show the correlations between the distance of the scour sidewall and PS data from the PS configured with 16 VCNL 4200. The distance (z) and PS (x) data exhibited statistically significant correlations, z = 10,090x, with R² = 0.92. The scattering may be due to actual differences in sediment density present at the live-bed flume, the different scattering regimes employed by the instruments, the type of optical components, and the proximity distance up to 1.5 m in the air, which could be shorter in water.

In total, 13 local scour data and bed form data points were gathered during this experiment as shown in Figure 8. Most of the tests were performed in the live-bed scour range, and a few tests were conducted in the clear-water scour range. Scour depth develops asymptotically with time, which is rapid during the initial stages and slows down after a few hours of scour activity. The development of a scour hole before, during, and after live-bed tests is shown in Figure 8. The values of PS were recorded continuously in each test at eight positions. The structure, sediments, flow conditions, and scour depth data are given in Table 3.

The scour depths derived from the sensor (d_ss) in column 12, and the scour depths measured from Laser rangefinder (d_se) in column 13, are consistent. The threshold velocity (V_c) can be calculated as follows [6]: V_*c is critical shear velocity determined from the Shields’ diagram, and y is the water depth.

\frac{V_{c}}{V_{* c}} = 5.7 * l o g (5.53 * (\frac{y}{D_{50}}))

(1)

V_{* c} = \{\begin{matrix} 0.0115 + 0.0125 {D_{50}}^{1.4}, 0.1 mm < D_{50} < 1 mm \\ 0.0305 {D_{50}}^{\frac{1}{2}} - 0.0065 {D_{50}}^{- 1}, 1 mm < D_{50} < 100 mm \end{matrix}

(2)

The customized PCB (printed circuit board) was developed to accommodate 16 proximity sensors within a 15 cm long sensor module. The 16 proximity sensors are divided into two groups (Figure 3). Within each group, the sensor spacing is 0.75 cm, while the spacing between the two groups is 1.25 cm (Figure 2). Since the spacing between each sensor is 0.75 cm, the precision is 0.75 cm for all sensors except for sensors 8 and 9, which have a precision of 1.25 cm.

After the experiment begins, the light sensitivity values of the proximity sensors buried in sand and those exposed due to scour change significantly (from 65,536 to 64). By observing the changes in light sensitivity values from all 16 sensors from bottom to top, the variation in scour depth can be determined. Since the distance between the lowest and highest points of this sensor module is 11.75 cm, the maximum sensitivity range of this sensor module system is up to 11.75 cm. From observations of 13 test trials, the light sensitivity values of sensors 1 to 4 were all above 60,000, indicating that they were below the sand surface and had not been exposed; sensors 12 to 16 had values below 2000, indicating that they were above the sand surface; and the values of sensors 5 to 11 varied according to the scour conditions. Since the sensor spacing was 0.75 cm, the precision was 0.75 cm for all sensors except for sensors 8 and 9, which have a spacing of 1.25 cm. The sand surface was controlled between sensors 11 and 12, and before starting the experiment, it was confirmed that the light sensitivity value of sensor 11 (expected to be between 30,000 and 65,535) was within a reasonable range.

A time history plot of the proximity data at position 0 (in the front of the pier) and position 4 (in the back of the pier) of test 10 is shown in Figure 9, and that of test 13 is shown in Figure 10. From the regression relation of PS data and distance, the scour trend can be derived related to the equilibrium scour depth (Figure 11). Figure 12 shows the contours of the scour depths measured using a laser rangefinder after tests 10 and 13. It is clear to see that the contours of test 10 are less symmetric than those of test 13. The contours of tests 4, 5, and 6 are the same. Factors affecting bed contours include water depth, slope, density, size of bed material, gradation of bed material, flow velocity, channel cross-sectional shape, etc. The primary variable is velocity. However, due to V/V_c in tests 4, 5, 6, and 10 being less than 1.2, i.e., the flow velocity being less than the critical velocity, the bed contours are not affected primarily by velocity, but by size of bed material and gradation of bed material. Owing to the compacting and leveling of the bed being unique, it is difficult to make bed layers homogeneous.

The scour development of the clear-water test (test 10) is rapid during the initial stages and becomes almost static for scour activity, except for the proximity value of the 10th sensor affected by the wake vortex. Scour depth was developed asymptotically with time on test 13. Figure 9, Figure 10, Figure 11 and Figure 12 show that the scour depth at position 0 (in the front of the pier) was bigger than at position 4 (in the back of the pier). The development of the scour depth with time was more significant at position 4. Figure 11b shows that the scour depth at the end of test 13 was 5 cm, which is the same as the scour depth in the time history plot of PS data at position 0 (Figure 12b). However, in Figure 11b, the maximum scour depth was shown to be 6 cm, which was not measured at the end of the test. Both Figure 9 and Figure 10 show that the PS data increased rapidly at the 8th hour; in other words, scour depth decreased rapidly at that time. As the flow velocity is larger than the threshold velocity, the sediment is continuously supplied to the scour hole, and the equilibrium depth is the result of a balance between the sediment supplied to and washed out of the scour hole. As the flow velocity rises higher and higher, the massive sediment transport from upstream to downstream drives the scour hole side slope to collapse, while the scour depth decreases rapidly at the same time.

Most of the tests were performed in the live-bed scour range except for two tests (5 and 6) conducted in the clear-water scour range. From [22], it can be seen that the behavior of the normalized scour depth (d_se/D) is a function of flow intensity (V/V_c), shown by the solid line in the schematic diagram in Figure 13a. Comparisons between the PS depth and the measured values of normalized scour depths are given in Figure 13b. It shows a peak in the equilibrium scour depth at the transition from clear-water to live-bed conditions, a decrease in equilibrium scour depth beyond the peak, and a second peak in the live-bed scour range, while the V/V_c of the clear-water peak is about 1.2. By observation, the conditions in tests 1, 7, and 11, whose V/V_c > 1.2, were in the clear-water stage, so the critical velocity from Melville and Coleman (2000) could be low estimates (Figure 13).

Figure 14 shows the contours of a scour hole with the time measured using the proximity sensors in tests 10 and 13. The scour development of the clear-water test (test 10) is rapid at the 15th second. After more than 30 s, the scour of test 10 is almost static; on the contrary, the scour of test 13 continuously becomes bigger and deeper. Until the 8th hour, the scour depth decreases, which is consistent with the time history plot of the PS data. Theoretically, the scour hole should be symmetric if the flow velocity and flume bed are homogenous. However, both the contours of tests 10 and 13 are asymmetric due to the non-homogeneous bed layers created by compaction and leveling, as well as the stochastic characteristics of flow intensity and sediment transport.

4. Conclusions

The bridge pier scour issue may have been intensively studied, but it is still necessary to enhance our understanding of this complex and nearly unexplored situation [9]. Current in situ scour monitoring typically utilizes more precise technologies, such as depth radar, sonar, laser scanning, and electromagnetic sensors, to obtain high-precision distance and depth data. However, due to their high cost, these technologies are not easily applied extensively in harsh bridge scour environments.

This study proposes a novel approach by using 16 proximity sensors, which are low-cost (less than USD 3 each) and low-power (380 µA in standby Current mode) optical instruments used to monitor and record pier scour depth varying with water flow rate in the flume at eight positions. The data collected from the experiment are consistent with the theory, further affirming that the real-time PS monitoring system has the potential for further applications in the field. First and foremost, the maximum scour depth can accurately be recorded, and from the proximity sensor data, the scour depth of the proximity sensors in the front of the pier model was greater than in the back of the pier, which is consistent with the local pier scouring theory. The tests showed that as the flow velocity is larger than the threshold velocity, the sediment is continuously supplied to the scour hole, and the equilibrium depth is the result of a balance between the sediment supplied to and washed out of the scour hole. As the flow velocity rises higher, the massive sediment transport from upstream to downstream drives the scour hole side slope to collapse and the scour depth to decrease rapidly at that time. The development of the scour depth with time is more significant in the back of the pier by the oscillation amplitude of proximity sensors, which is consistent with the theory. Moreover, the scour trend could be derived from the correlation between the distance (z) of the scour hole and PS data (1/x), z = 10,090x from this study. Due to the existence of non-homogeneous bed layers, the manual compaction and leveling of the bed, different types of proximity sensors, and the stochastic characteristics of flow and sediment transport, the development of contours of the scour hole with time from proximity sensors needs further future investigation.

The experimental results have verified that, in addition to achieving centimeter-level accuracy, the Internet of Things (IoT) enables real-time observation, measurement, and recording of the formation, changes, and size of scour pits over extended periods. Due to its simplicity in analysis and low cost, this approach is feasible and promising for future applications in on-site scour monitoring. Through the method proposed in this study, the safety of bridge piers can be assessed more economically, precisely, and effectively in the future.

Author Contributions

Conceptualization, P.-Y.W., D.-S.S. and K.-C.Y.; methodology, P.-Y.W. instrumentation, P.-Y.W.; validation, P.-Y.W.; writing—original draft preparation, P.-Y.W.; writing—review and editing, D.-S.S.; supervision, K.-C.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Acknowledgments

The authors gratefully acknowledge the experimental facility support by the Department of Civil Engineering, National Yang Ming Chiao Tung University.

Conflicts of Interest

The authors declare no conflicts of interest.

Notation

The following symbols are used in this paper:

D	circular pile diameter;
D₁₆	sediment size for which 16% of bed material is finer;
D₅₀	median sediment grain diameter;
D₈₄	sediment size for which 84% of bed material is finer;
σ	standard deviation of sediment particle size distribution;
d_s	scour depth at end of experiment;
d_se	equilibrium scour depth;
d_ss	sensor scour depth;
V	depth-averaged velocity;
V_c	depth-averaged velocity at threshold condition for sediment motion (sediment critical velocity);
y	water depth;
y₀	approach water depth.

References

Melville, B.W.; Raudkivi, A.J. Flow Characteristics in local scour at bridge piers. J. Hydraul. Res. 1977, 15, 373–380. [Google Scholar] [CrossRef]
Melville, B.W.; Sutherland, A.J. Design method for local scour at bridge piers. J. Hydraul. Eng. 1988, 114, 1210–1226. [Google Scholar] [CrossRef]
Dargahi, B. Controlling mechanism of local scouring. J. Hydraul. Eng. 1990, 116, 1197–1214. [Google Scholar] [CrossRef]
Dey, S.; Bose, S.K.; Sastry, G.L.N. Clear-water scour at circular piers: A model. J. Hydraul. Eng. 1995, 121, 869–876. [Google Scholar] [CrossRef]
Melville, B.W.; Raudkivi, A.J. Effects of foundation geometry on bridge pier scour. J. Hydraul. Eng. 1996, 122, 203–209. [Google Scholar] [CrossRef]
Melville, B.W.; Coleman, S.E. Bridge Scour; Water Resources Publications, LLC.: Highlands Ranch, CO, USA, 2000. [Google Scholar]
Sheppard, D.M.; Odeh, M.; Glasser, T. Large scale clear-water local pier scour experiments. J. Hydraul. Eng. 2004, 130, 957–963. [Google Scholar] [CrossRef]
Wang, C.; Yu, X.; Liang, F. A review of bridge scour: Mechanism, estimation, monitoring and countermeasures. Nat. Hazards 2017, 87, 1881–1906. [Google Scholar] [CrossRef]
Martin, C.S.; Rifo, C.; Guerra, M.; Ettmer, B.; Link, O. Monitoring Scour at Bridge Piers in Rivers with Supercritical Flows. Hydrology 2023, 10, 147. [Google Scholar] [CrossRef]
Matos, T.; Rocha, J.L.; Faria, C.L.; Martins, M.S.; Renato, L.M.; Henriques, G. Development of an automated sensor for in-situ continuous monitoring of streambed sediment height of a waterway. Sci. Total Environ. 2022, 808, 152164. [Google Scholar] [CrossRef] [PubMed]
Ye, Y.; Zhang, C.; He, C.; Wang, X.; Huang, J.; Deng, J. A review on applications of capacitive displacement sensing for capacitive proximity sensor. IEEE Access 2020, 8, 45325–45342. [Google Scholar] [CrossRef]
Valentín, D.; Presas, A.; Egusquiza, M.; Valero, C.; Egusquiza, E. Detection of hydraulic phenomena in Francis turbines with different sensors. Sensors 2018, 19, 4053. [Google Scholar] [CrossRef] [PubMed]
Yadav, A.K.; Verma, D.; Kumar, A.; Kumar, P.; Solanki, P.R. The perspectives of biomarker-based electrochemical immunosensors, artificial intelligence and the Internet of Medical Things toward COVID-19 diagnosis and management. Mater. Today Chem. 2021, 20, 100443. [Google Scholar] [CrossRef]
Verma, D.; Singh, K.R.; Yadav, A.K.; Nayak, V.; Singh, J.; Solanki, P.R.; Singh, R.P. Internet of things (IoT) in nano-integrated wearable biosensor devices for healthcare applications. Biosens. Bioelectron. X 2022, 11, 100153. [Google Scholar] [CrossRef]
Liu, J.; Yuan, C.; Matias, L.; Bowen, C.; Dhokia, V.; Pan, M.; Roscow, J. Sensor technologies for hydraulic valve and system performance monitoring: Challenges and perspectives. Adv. Sens. Res. 2024, 3, 2300130. [Google Scholar] [CrossRef]
Matos, T.; Faria, C.L.; Martins, M.S.; Henriques, R.; Gomes, P.A.; Goncalves, L.M. Design of a multipoint cost-effective optical instrument for continuous in-situ monitoring of turbidity and sediment. Sensors 2020, 20, 3194. [Google Scholar] [CrossRef] [PubMed]
Andrea, M.; Enrico, T.; Neil, F.; Alessandro, T.; Hazel, M.; Daniele, Z. Electromagnetic Sensors for Underwater Scour Monitoring. Sensors 2020, 20, 4096. [Google Scholar] [CrossRef] [PubMed]
Schaar, R. Designing the VCNL4200 into an Application. Vishay Semiconductors, Application Note, No. 84327. 2020. Available online: https://www.vishay.com/docs/84327/designingvcnl4200.pdf (accessed on 28 November 2024).
Raudkivi, A.J.; Ettema, R. Clear-water scour at cylindrical piers. J. Hydraul. Eng. 1983, 109, 338–350. [Google Scholar] [CrossRef]
Breusers, H.N.C.; Raudkivi, A.J. Scouring, Hydraulic Structures Design Manual; IAHR, AA Balkema: Rotterdam, The Netherlands, 1991; Volume 2. [Google Scholar]
Melville, B.W.; Chiew, Y.M. Time scale for local scour at bridge piers. J. Hydraul. Eng. 1999, 125, 59–65. [Google Scholar] [CrossRef]
Sheppard, D.M.; William, M.J. Live-Bed Local Pier Scour Experiments. J. Hydraul. Eng. 2006, 132, 635–642. [Google Scholar] [CrossRef]

Figure 1. VCNL4200 detailed block diagram.

Figure 2. Local scour depth measuring instruments.

Figure 3. Customized PCB board for a sensor group with 8 VCNL4200 sensors.

Figure 4. Rotary mechanism for 8-dimension measurement.

Figure 5. Cloud-based monitor framework.

Figure 6. Schematic drawing of the flume.

Figure 7. Distance PS data vs. data derived from 179 results.

Figure 8. Scour hole during live-bed scour test.

Figure 9. Time history plot of PS data (test 10). Note: PS_12~PS_15 and S_0~PS_3 are not shown in Figure 9 because their positions are out of the bed or scour hole.

Figure 10. Time history plot of PS data (test 13).

Figure 11. Time history plot by PS data at position 0 vs. 4 (front vs. back of the pier).

Figure 12. Contours of scour depths (tests 10 and 13).

Figure 13. (a) Normalized scour depth (d_se/D) versus flow intensity (V/V_c) from Sheppard and William (2006); (b) measured (d_ss/D) and proximity sensor (d_se/D) versus V/V_c, note: There are only 9 points in (b) due to 3 pairs of measured (d_ss/D) (tests 1 and 2, 3 and 7, 9 and 12).

Figure 14. Contours of scour depths with time from proximity sensors (tests 10 and 13).

Table 1. Comparison of monitoring methods of bridge scour (paraphrased from Wang [8], with permission).

Category	Instrument	Advantages	Disadvantages	Economic
Reference target	Smart rock (SR)	Easy to install and operate; large region	Cannot collect data continuously; cannot monitor the refill process	Good
	Magnetic sliding collar (MSC)	Easy to install and operate; can be used during floods	Cannot collect data continuously; limited region	Good
Soil–water interface	TDR	Easy to install and operate; can monitor the refill process; real-time monitoring	limited monitoring region; excavation required	Good
	Fiber Bragg grating sensor	Continuous monitoring	Special training required; easily destroyed	Poor
Structure monitoring	Tilt sensor	Easy to analyze and interpret	Influenced by traffic, temperature, wind and hydraulic factors	Excellent
	Modal parameter	Environmentally friendly; easy to operate	Special training required; influenced by traffic, piers, and hydraulic factors	Excellent

Table 2. VCNL4200 electrical specification.

Parameter	Value
VCNL4200 dimensions	8 mm × 3 mm × 1.8 mm
IR emitter wavelength	940 nm
Proximity distance	Up to 1.5 m (air)
Proximity measure time	304 ms
Temperature range	−40 °C to 80°C
Communication protocol	I2C
Operation voltage	2.5 V~3.6 V
Standby current	350 μA
IRED driving current	Up to 800 mA

Table 3. Nondimensional parameters, and measured and sensor scour depths (position 0, front of the pier).

No.	Pile Diameter D (m)	Sediment D₅₀ (mm)	Sediment s	Water Depth y₀ (m)	Flow Velocity V (m/s)	Critical Velocity V_c (m/s)	Test Duration t (min)	y₀/D	V/V_c	D/D₅₀	Sensor Scour Depth d_ss (cm)	Measured Scour Depth d_se (cm)	d_ss/D	d_se/D
(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)
1	0.048	0.53	1.54	0.07	0.327	0.27	180	1.458	1.21	90.57	3.25	3.1	0.68	0.65
2	0.048	0.53	1.54	0.07	0.414	0.27	180	1.458	1.53	90.57	3.25	3.5	0.68	0.73
3	0.048	0.53	1.54	0.07	0.524	0.27	180	1.458	1.94	90.57	4	4.6	0.83	0.96
4	0.048	0.53	1.54	0.07	0.743	0.27	180	1.458	2.75	90.57	4.75	5	0.99	1.04
5	0.048	0.53	1.54	0.1	0.213	0.289	180	2.083	0.74	90.57	1	1	0.21	0.21
6	0.048	0.53	1.54	0.1	0.290	0.289	180	2.083	1.00	90.57	2	2.5	0.42	0.52
7	0.048	0.53	1.54	0.1	0.367	0.289	180	2.083	1.27	90.57	4	4	0.83	0.83
8	0.048	0.53	1.54	0.1	0.520	0.289	30	2.083	1.80	90.57	4.5	4.5	0.94	0.94
9	0.048	0.53	1.54	0.1	0.857	0.289	420	2.083	2.97	90.57	5.5	5.5	1.15	1.15
10	0.048	0.53	1.54	0.14	0.332	0.303	1040	2.917	1.10	90.57	3.3	3	0.69	0.63
11	0.048	0.53	1.54	0.14	0.426	0.303	180	2.917	1.41	90.57	3	2.5	0.63	0.52
12	0.048	0.53	1.54	0.14	0.495	0.303	360	2.917	1.63	90.57	5.5	5.5	1.15	1.15
13	0.048	0.53	1.54	0.14	0.580	0.303	750	2.917	1.91	90.57	5	5	1.04	1.04

Note: The values of d_ss/D in column (14) (tests 1 and 2) are the same, and 3 and 7, and 9 and 12 are the same, too.

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Wu, P.-Y.; Shih, D.-S.; Yeh, K.-C. Evaluation of Proximity Sensors Applied to Local Pier Scouring Experiments. Water 2024, 16, 3659. https://doi.org/10.3390/w16243659

AMA Style

Wu P-Y, Shih D-S, Yeh K-C. Evaluation of Proximity Sensors Applied to Local Pier Scouring Experiments. Water. 2024; 16(24):3659. https://doi.org/10.3390/w16243659

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Wu, Pao-Ya, Dong-Sin Shih, and Keh-Chia Yeh. 2024. "Evaluation of Proximity Sensors Applied to Local Pier Scouring Experiments" Water 16, no. 24: 3659. https://doi.org/10.3390/w16243659

APA Style

Wu, P. -Y., Shih, D. -S., & Yeh, K. -C. (2024). Evaluation of Proximity Sensors Applied to Local Pier Scouring Experiments. Water, 16(24), 3659. https://doi.org/10.3390/w16243659

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