Slope state monitoring system and method

Abstract

The invention provides a side slope state monitoring method and system for monitoring a side slope, relates to monitoring of the side slope state and / or early detection of side slope instability, and is used for taking prevention measures of side slope instability in time. The method comprises the following steps: acquiring volumetric water content from a plurality of sensors arranged on a slope; determining a safety factor of the slope using the volumetric water content based on a linear regression model obtained from the simulated volumetric water content corresponding to the humid meteorological conditions; and generating an alert in response to the safety factor being below a safety threshold, the alert indicating that a slope instability may occur. The system includes a controller arranged to perform the method and a sensor arranged on a selected slope and arranged to provide volumetric water content. The sensors can comprise osmometers arranged at the slope top and the slope toe of the selected slope and capacitive moisture sensors distributed at different elevations of the selected slope. According to the invention, a slope evaluation solution with relatively high cost effectiveness can be provided.

Description

Related applications

[0001] This application claims the benefit of priority to Singapore Patent Application No. 10202403291W, filed on October 23, 2024, the contents of which are incorporated herein by reference in their entirety for all purposes. Technical Field

[0002] This application relates to environmental monitoring, and more specifically to the monitoring of slope condition and / or early detection of slope instability. Background Technology

[0003] Climate change is leading to more volatile weather conditions. Prolonged drought-like weather can be followed by prolonged periods of rainfall. Rainfall conditions are becoming more unpredictable. Slopes may be more prone to instability when rainwater seeps into unsaturated areas (such as soil forming slopes and above the water table). In densely populated areas, slope instability can cause severe damage to buildings and other infrastructure. Slope instability can have catastrophic consequences, such as injury or death.

[0004] Traditionally, it has been believed that planted slopes, or the planting of vegetation on slopes, can mitigate slope instability. For example, theoretically, plant roots should help maintain soil cohesion and prevent landslides. However, unpredictable weather conditions caused by climate change can also affect plant health. After a period of drought, vegetation on a slope may become so vulnerable that it cannot provide sufficient support to maintain soil cohesion during the next deluge of rainwater. Therefore, there is an urgent need for a better method to monitor and / or detect slope instability so that preventative measures can be taken in a timely manner. Summary of the Invention

[0005] On one hand, this application discloses a slope condition monitoring method for monitoring slopes. The slope condition monitoring method includes acquiring volumetric water content from multiple sensors installed on the slope. The method also includes using the volumetric water content to determine a safety factor for the slope based on a linear regression model obtained from simulated volumetric water content corresponding to humid weather conditions. Furthermore, the method includes generating an alarm in response to the safety factor being determined to be below a safety threshold, indicating that slope instability may have occurred.

[0006] Slope condition monitoring methods may include using volumetric water content to determine plant health indicators of one or more plants on a slope, wherein an alarm is generated in response to any one and / or a combination of the following: a safety factor is determined to be below a safety threshold, and a plant health indicator is below a health threshold.

[0007] On the other hand, slope condition monitoring methods may also include: in response to rainfall, collecting multiple volumetric water contents at a first sampling frequency; and in response to the absence of rainfall, collecting multiple volumetric water contents at a second sampling frequency, wherein the first sampling frequency is higher than the second sampling frequency.

[0008] On the other hand, a slope condition monitoring system at least partially installed on a selected slope includes multiple sensors and a controller disposed on the selected slope. The multiple sensors are configured to provide the volumetric water content of the selected slope. The controller is configured to communicate with the multiple sensor signals. The controller is configured to execute a slope condition monitoring method according to any one or more of the above embodiments. The multiple sensors may include: multiple osmotic tensiometers disposed at the top and bottom of the selected slope; and multiple capacitive moisture sensors distributed at different elevations on the selected slope. Attached Figure Description

[0009] To aid understanding, various embodiments of this application will be described with reference to the following accompanying drawings:

[0010] Figure 1 This is a schematic diagram of a slope condition monitoring system according to an embodiment of this application.

[0011] Figure 2A and Figure 2B This is a schematic diagram of different areas under monitoring.

[0012] Figure 3 This is a schematic diagram of a test system equipped with the proposed slope condition monitoring system.

[0013] Figure 4A and Figure 4B This is a schematic diagram of the layout of the ground-based instruments used in the field research.

[0014] Figure 5 This is a schematic diagram of the field monitoring system architecture used in the field study, which features advanced sensing instruments including: (a) an IMKO PICO 32 time-domain reflectometry moisture sensor; (b) a MeterGroup TEROS 12 capacitive moisture sensor; (c) a SISGEO-P252R piezometer; and (d) an osmotic tensiometer.

[0015] Figure 6A and Figure 6BThe results of volumetric water content monitoring obtained using PICO 32 are shown from March to July 2023. Figure 6A The volumetric water content at 0.2 m is shown. Figure 6B The volumetric water content at 0.75 m is shown.

[0016] Figure 7A and Figure 7B The results of volumetric water content monitoring obtained using TEROS 12 during the period from March to July 2023 are shown. Figure 7A The volumetric water content at 0.2 m is shown. Figure 7B The volumetric water content at 0.75 m is shown.

[0017] Figure 8 The suction monitoring results were obtained from piezometers at depths of 0.2m and 0.75m.

[0018] Figure 9A and Figure 9B PICO 32 ( Figure 9A ) and TEROS 12 ( Figure 9B The results of the aggregated volumetric water content measured at the monitoring depth were compared.

[0019] Figure 10A and Figure 10B PICO 32 ( Figure 10A ) and TEROS 12 ( Figure 10B The volumetric water content results obtained at a depth of 0.2 m under the soil-water characteristic envelope were compared.

[0020] Figure 11A and Figure 11B PICO 32 ( Figure 11A ) and TEROS 12 ( Figure 11B The volumetric water content results obtained at a depth of 0.75m below the soil-moisture characteristic envelope were compared.

[0021] Figure 12 The slope model of the testbed in Rumah Tinggi is shown for SEEP / W analysis.

[0022] Figure 13A and Figure 13BThe soil characteristics used in the SEEP / W analysis are shown separately, and more specifically, the soil-water characteristic curves (SWCC) and the permeability function are shown separately.

[0023] Figures 14A to 14C The figures show the volumetric water content (obtained from sensor measurements) and SEEP / W analysis at depths of 0.05 m, 0.2 m, and 0.75 m, respectively.

[0024] Figure 15 This shows the changes in the groundwater table at the top and bottom of the slope.

[0025] Figure 16 This shows the change in groundwater level during the SEEP / W analysis (K2.97E-07m / s). s ).

[0026] Figure 17 This is a graph showing the actual rainfall inputs used for analysis during the period from June 11 to June 19.

[0027] Figures 18A to 18D The figures show the safety factor and volumetric water content results obtained by TEROS 12 at 0.2m (middle section), slope 1, slope 2 and slope 3.

[0028] Figure 19 This is a diagram of the factor of safety (FOS) determined using TEROS 12 sensor readings. Detailed Implementation

[0029] The following detailed description will be given with reference to the accompanying drawings, which illustrate details and embodiments of the present application for illustrative purposes. Features described in the context of one embodiment may be correspondingly applied to the same or similar features in other embodiments, even if not explicitly described in those other embodiments. The application and / or combination and / or substitution of features described in the context of one embodiment may be correspondingly applied to the same or similar features in other embodiments.

[0030] The word “exemplary” as used herein means “serving as an example, instance, or illustration.” Any “exemplary” embodiment described herein is not necessarily to be construed as superior to or more advantageous than other embodiments. The singular “a” and “an” as used herein may be understood to include the plural “one or more” unless the context otherwise requires. In the context of the various embodiments, the articles “a,” “an,” and “the” used with respect to features or elements include references to one or more features or elements.

[0031] As used herein, the term “and / or” includes any and all combinations of one or more of the related listed items.

[0032] In the context of the various embodiments, the term “about” or “approximately” applied to numerical values ​​covers exact values ​​and reasonable differences as generally understood in the relevant art, such as within 10% of a specified value.

[0033] Terms such as “first” and “second” used in the specification and claims are for the purpose of brevity and clarity only and do not necessarily imply priority or order unless the context requires it.

[0034] Some methods may be described in terms of steps, stages, periods, etc., for the purpose of aiding understanding and / or facilitating reference only. In this application, the division between one step and another is merely for convenience. It will be understood that in actual implementation, there may be no clear division or transition between one step and another subsequent step. Multiple steps may overlap to some extent, and / or multiple steps may occur or be executed simultaneously, unless the context otherwise requires.

[0035] Figure 1 This is a schematic diagram of a slope condition monitoring system 200 according to an embodiment of this application. For the avoidance of doubt, although embodiments of this application are applicable to slopes or sloping ground (such as...) Figure 2B As shown), the slope condition monitoring system 200 can also be used on relatively level ground (such as...). Figure 2A (As shown). Rainfall typically infiltrates the soil, which helps recharging the groundwater table and / or aids in water absorption by plant roots. When rainfall exceeds the maximum amount of water that can infiltrate the soil, runoff occurs. Water can be restored to a vapor state through evaporation and / or transpiration by plants. Other factors, such as, but not limited to, slope inclination, soil type, plant species, vegetation density, and meteorological conditions, further complicate slope condition analysis.

[0036] Refer again Figure 1 The slope condition monitoring system 200 may include a controller 240. The computing device may take the form of one or more processors or computing modules operatively communicating with one or more local or remote memories and / or memory, at least one of which is configured to store and / or execute machine-readable instructions. The controller may include or signal-communicate with a wireless communication module, wherein the wireless communication module is configured to receive signals from one or more sensors 210. The controller 240 may be a local controller, a remote controller, or a combination of both. The controller 240 may be configured to execute machine-readable instructions to perform the methods according to this application. The description of the slope condition monitoring system 200 being configured to perform operations may also be understood to include the controller 240 being configured to execute machine-readable instructions to extract and / or store signal communications and / or data, and / or perform calculations, acquire inputs, and / or provide outputs, etc.

[0037] The slope condition monitoring system 200 can be configured to periodically acquire signals from sensor 210 and determine whether there is rainfall (e.g., step 220). See also... Figure 2A and Figure 2B Sensor 210 may include a rain gauge 211. Sensor 210 may also include moisture sensors 212 disposed in soil 110, which includes soil on slope 150. In some examples, sensor 210 includes selected moisture sensors 212 disposed near one or more plants growing on slope 150.

[0038] The sensor may also include a moisture sensor 212 disposed on the slope 150. In one example, the moisture sensor 212 includes a plurality of moisture sensors 212 distributed along the gradient line of the slope 150. In another example, the plurality of moisture sensors 212 may be disposed at the top of the slope, the middle of the slope, and the toe of the slope. In yet another example, the plurality of moisture sensors 212 may be distributed between the top of the slope and the toe of the slope or spaced apart from each other.

[0039] According to an embodiment of this application, the slope condition monitoring system 200 is configured to responsively adjust the sampling frequency based on a first signal acquired from a rain gauge 211, wherein the sampling frequency is the frequency at which a second signal is acquired from a sensor 210. The sensor 210 may include a rain gauge 211 and / or a moisture sensor 212.

[0040] The sampling frequency can also be described alternatively as the frequency of measurements or readings acquired or extracted per unit time. Accordingly, the sampling interval can be described as the time interval between multiple consecutive measurements or readings.

[0041] The slope condition monitoring system 200 is configured to adjust the sampling frequency or sampling interval in real time and in response to rainfall or lack thereof. For example, in response to rainfall (e.g., it is raining when the signal is acquired), the slope monitoring system 200 is configured to periodically acquire signals (e.g., obtain a second signal from sensor 210) at shorter sampling intervals or higher sampling frequencies 223 / 224. This can mean that the loop of the signal acquisition algorithm is shortened or the iteration speed is increased, for example, acquiring a series of second signals at preset, shorter time intervals. In response to lack of rainfall (e.g., it is not raining when the signal is acquired), the slope monitoring system 200 is configured to periodically acquire signals (e.g., obtain a second signal from sensor 210) at longer sampling intervals or lower sampling frequencies 225 / 226. This can mean that the loop of the signal acquisition algorithm is lengthened or the iteration speed is slowed down, for example, acquiring a series of second signals at preset, longer time intervals. In other words, the controller can be configured to acquire sensor measurements at a first sampling frequency in response to rainfall, and at a second sampling frequency in response to the absence of rainfall, wherein the first sampling frequency is higher than the second sampling frequency.

[0042] In one example of the slope condition monitoring system 200, if the first rain gauge reading obtained at time = 0 indicates that it is raining (rainfall), the next or second rain gauge reading can be measured at time = 10 minutes, and a subsequent or third rain gauge reading can be measured at time = 20 minutes. For the same slope monitoring system located in the same physical or geographical location, if the first rain gauge reading obtained at time = 0 indicates that it is not raining (no rainfall), the next or second rain gauge reading can be measured at time = 3 hours or 180 minutes, and a subsequent or third rain gauge reading can be measured at time = 6 hours or 360 minutes.

[0043] In another example, the slope condition monitoring system 200 can be set to operate at a default longer sampling interval over a period of several hours until rainfall is detected, and in response to the detection of rainfall, shorten the sampling interval to less than one hour.

[0044] According to embodiments of this application, multiple sensors 210 are disposed in the soil on or near a slope. For the sake of brevity, the sensors 210 may be described as being disposed in one location. The description of sensors 210 being disposed on or along a slope can be understood as sensors 210 being deployed, installed, or embedded (partially or entirely) in the soil of the slope or in the soil near the slope.

[0045] According to some embodiments of this application, the slope condition monitoring system 200 may be configured to transmit measurements or readings (and / or results based on those measurements or readings) already acquired from the sensor 210 to a server (step 250).

[0046] According to some embodiments of this application, the slope condition monitoring system 200 may be configured to compare acquired measurements or readings with a threshold (step 260). The slope condition monitoring system 200 may be configured to generate or provide an alarm signal if the measurements or readings exceed the threshold (step 270). In some examples, the alarm signal may include a message that can be sent via a short message service (SMS).

[0047] Controller 240 can be configured to determine or acquire a threshold (step 280). This threshold can be determined based on volumetric moisture content. In some embodiments, the threshold can be a value calculated or determined based on an expected instability line, such as an instability line that may form between multiple wetting zones in the soil. In some embodiments, the threshold can be a value calculated or determined based on the dynamic change (development) of soil moisture over the rainfall duration. Alarm signals can be configured to include early alarm signals and / or final alarm signals.

[0048] Actual field tests were conducted to verify the feasibility and performance of the proposed slope condition monitoring system 200.

[0049] In field tests, a combination of piezometer 213 and capacitive moisture sensors was installed. For example, piezometer 213 was installed at the top and bottom of the slope, and capacitive moisture sensors were distributed at different elevations of the slope.

[0050] Figure 3 This is a schematic diagram of a test system equipped with the proposed slope condition monitoring system 200.

[0051] To aid understanding and without imposing limitations, the test system can be described in three parts.

[0052] Part A (also known as the development of advanced sensing systems) includes the development of advanced sensing systems with cloud communication capabilities for real-time data collection and analysis. Outputs are collected from the field under different meteorological conditions over time. Real-time volumetric water content (VWC) is determined based on the outputs obtained from the field. Based on the real-time volumetric water content, real-time factors of safety (FS) and real-time plant health indicators (PHI) are determined. Based on the technical description of this application, the terms "volumetric water content," "VWC," "VWC value," and "VWC data" are used interchangeably.

[0053] Since this was a test, real-time FS and real-time PHI were validated. It is understood that validation may be optional during the actual implementation of this system.

[0054] Part B (also known as the plant-soil study) involves monitoring the plants. During the testing process, soil-water characteristic curves (SWCCs) of the soils used to grow the plants under different water treatment protocols were obtained in the geotechnics laboratory. During the testing, the plants in the greenhouse were subjected to different water treatment protocols, and VWC and F were monitored. v / F m Ratio (the ratio of fluorescence to maximum fluorescence). F v / F m The ratio reflects the efficiency of photosynthetic energy conversion in plants. During the test, F... v / F m The ratio is used as one of the indicators of plant health (e.g., as a quantitative measure of plant stress or the impact of the environment on plant health). A higher F ratio can be considered... v / F m The ratio indicates that the plant is in good health, that is, its photosynthetic capacity is strong.

[0055] During the testing process, the plants monitored for plant health indicators were those in laboratory or greenhouse environments. It will be understood that, in practice, plants on slopes or in outdoor environments can also be the subjects of plant health monitoring.

[0056] Part C (also known as advanced data processing or predictive analytics) involves the development of a soil database that includes soil properties. In this example, the GEOtop model is used; GEOtop is a distributed model of the mass and energy balance of the hydrological cycle (https: / / geotopmodel.github.io / geotop / ). Other simulation models can be used instead of GEOtop. The simulation model is used to generate outputs based on inputs including soil properties of the monitored slope, such as simulated, calculated, or analytical VWC (driest or extremely dry weather conditions) under the driest weather conditions, and analytical VWC (wettest weather conditions) under the wettest weather conditions (or extremely wet weather conditions).

[0057] Scoops3D (Slope Change Optimization of Probability of Stability in 3D) is a 3D slope stability analysis software available from the United States Geological Survey (www.usgs.gov / software / scoops3d). It is used to establish the relationship between VWC (Village Wettest Water Content) and FS (Slope Stability), and more specifically, the relationship between VWC and FS. Traditionally, Scoops3D is used to assess slope stability on large digital elevation models (DEMs) by searching for the most critical slip surface. According to embodiments of this application, the input to the Scoops3D software is selected from VWC data corresponding to the VWC (Village Wettest Water Content).

[0058] Geographic information system (GIS) maps can be based on both PHI and FS simultaneously and presented as a real-time integrated output. On-site monitoring

[0059] Field monitoring was conducted on a slope in Rumah Tinggi, a residential area in central Singapore. A shallow slope with an average angle of 18 degrees was chosen because steeper slopes can lead to significant runoff, posing a challenge to sensor performance evaluation. During the approximately four-month monitoring period between March and July 2023, atmospheric temperatures at a 10m x 6m measurement site on the slope ranged from 25°C to 33°C, and the maximum daily rainfall was 76 mm.

[0060] The near-surface layer (0m to 0.5m) composed of clayey soil (SC) mainly consists of organic matter mixed with clayey sands and / or sand-clay mixtures. The shallow layer (0.5m to 4m) composed of low-plasticity clay (CL) mainly consists of low- to medium-plasticity inorganic clays. The CL layer represents the soil composition in the transition zone between the unsaturated and saturated zones, where the groundwater level is located. The deeper layers (4m to 8m) contain inorganic silts and (very) fine sands.

[0061] During drilling, soil sampling at the monitoring slope was conducted using the Unified Soil Classification System (USCS) to identify three stratified soil layers. Table 1 below lists the indexed soil properties, composition, and saturated permeability of the shallow soil layers from the field survey. These soil properties, particularly those of the shallow soil layers near the surface, are used to analyze soil-moisture properties and permeability functions. These properties and functions are needed to validate sensor measurements and to perform numerical calculations of soil moisture content in the field.

[0062] Two pressure gauges with a depth of 8 meters below the ground surface were installed at the top and bottom of the slope, and the readings showed the shallower groundwater level, about 2 meters below the ground surface.

[0063] Figure 4A A schematic diagram of the slope under study is shown, illustrating its geometric dimensions and the instrument set used for field monitoring. Figure 4B The instrument layout on the actual measurement site is shown. On the slope, three sets of moisture sensors and piezometers were installed at depths of 0.2m and 0.75m to monitor changes in soil moisture content above the groundwater level.

[0064] Figure 5This diagram illustrates the Internet of Things (IoT) architecture employed in the shallow landslide monitoring system. To this end, an advanced data logging system was developed. This system comprises multiple "slave" units and one "master" unit. The slave units (connected to a local sensor array via operable signal communication) and the master unit wirelessly receives data transmitted from the slave units. A 4G connection is used to further transmit the raw data to cloud storage. End users can download and plot different real-time datasets online or via the internet. The data logging system is protected within a sealed enclosure and powered by a solar panel on top of the enclosure. The data logger collects real-time measurements at a resolution of once every ten minutes and sends the data to cloud storage every hour. End users can then access, organize, and analyze the uploaded datasets online. Field instruments

[0065] The instruments used included osmometers and moisture sensors connected to the data acquisition equipment (such as the IMKO PICO 32 time-domain reflectometry moisture sensor and the Meter Group TEROS 12 sensor). The osmometers were, for example, available from Nanyang Technology University (NTU). Table 2 summarizes the instrument manufacturers, models, and relevant specifications. Commercially available osmometers can be used.

[0066] All piezometers and moisture sensors underwent laboratory calibration prior to field deployment. Soil moisture sensors were calibrated using saturated volumetric water content determined from in-situ soil samples at the target installation depth, employing a linear relationship between voltage output and water content readings. Piezometers were saturated and monitored in the laboratory to identify maximum osmotic pressure, which served as a benchmark for resolving soil suction measurements. Pressure fine-tuning of the field dataset was performed based on the effects of soil temperature and pressure decay to calculate real-time in-situ soil suction. These calibration procedures and data analysis techniques effectively improved the accuracy and consistency of sensor readings. Soil moisture content measurement

[0067] like Figure 4A and Figure 4BAs shown, two sets of moisture sensors, an IMKOPICO 32 (time domain reflectometry moisture sensor, TDR) and a Meter Group TEROS 12 (capacitive moisture sensor), were installed at the upper, middle, and lower sections of the slope, respectively. Each location had exactly two independently deployed sensors, positioned at depths of 0.2 m and 0.75 m, respectively, serving as target layers to assess slope stability at shallower depths. Both TDR and capacitive sensors utilize the soil dielectric constant to measure volumetric water content and are widely used in field slope monitoring. The TDR sensor's internal generator emits electromagnetic pulses from the rods, which then propagate and reflect within the rods. Volumetric water content is accurately determined by calculating the soil dielectric constant based on the propagation time. For the capacitive sensor, the charging time of the medium is measured using two electrodes extending from a metal probe, and the oscillating voltage correlates the dielectric constant with the volumetric water content. Recent advancements in capacitive sensors have minimized the impact of salinity by utilizing high-frequency technology, and their low cost and low power consumption are environmentally beneficial, enabling the application of large-scale sensor networks. This application also evaluates the field performance of two sensors based on monitoring results obtained from actual field measurements. Soil suction measurement

[0068] After distributing soil moisture sensors, piezometers were installed at the upper, middle, and lower sections of the slope. For comparison of measurements, the piezometers were strategically positioned midway between two sets of sensors. The piezometer consists of a flat-sloped ceramic disc embedded in a steeply sloping stainless steel cup and connected to a piezoresistive pressure sensor (model PT35X, Keller-Druck). The sensor has a measurement range of -0.1 MPa to 2.5 MPa and a reading accuracy of 0.01% of full scale. The pressure sensor uses O-rings (0.2 mm thick) to seal the gap and prevent polymer leakage between the stainless steel cup and the sensor.

[0069] Crosslinked sodium polyacrylate was chosen as the filler polymer material to achieve high and stable expansion pressure, minimal loss, and fast response time. High osmotic pressure (1 MPa to 2 MPa, depending on the polymer concentration in the mixture) was achieved using distilled water-saturated superabsorbent sodium polyacrylate, and the piezometer was calibrated using a reference osmotic pressure to resolve soil suction. The piezometer used is independent of linear extrapolation over negative pressure ranges and eliminates drawbacks in sensor calibration and field applications; the piezometer has demonstrated reliable performance in previous monitoring. Soil suction measurements were used to further evaluate the soil moisture sensor within the soil-moisture characterization envelope. Changes in soil moisture content

[0070] Daily temperature changes and precipitation events directly affect the changes in suction and volumetric water content of unsaturated soil on the monitored slope. Figure 6A and Figure 6B The diagram shows the changes in volumetric water content at depths of 0.2 m and 0.75 m from March to July, as measured by an IMKO PICO 32 TDR moisture sensor at the actual site. The total water content at the 0.2 m depth ranges from 30% to 40%, and the total water content at the 0.75 m depth ranges from 35% to 55%. Figure 7A and Figure 7B The image shows the changes in volumetric water content at depths of 0.2 m and 0.75 m from March to July, as measured by a Meter Group TEROS 12 moisture sensor at the field site. Total water content at 0.2 m depth ranged from 35% to 55%, and at 0.75 m depth from 40% to 55%. This four-month monitoring period was essentially a dry period, and rainfall events were infrequent based on weather station records. Both the PICO 32 and TEROS 12 moisture sensors responded well to rainfall, showing distinct spikes or one or more spike increases in volumetric water content readings at the onset of rainfall, and gradually decreasing after the rainfall event ended. This demonstrates the feasibility of detecting immediate precipitation events by obtaining volumetric water content responses from soil moisture sensors.

[0071] During these dry months, soil suction changes significantly following sudden rainfall events. Figure 8The average suction at depths of 0.2 m and 0.75 m measured by piezometers was summarized. Monitoring results showed a significant decrease in near-surface suction due to rainfall events. Although no soil suction greater than 100 kPa was observed during the recording period, it can be confirmed that drought periods generally lead to an increase in field suction. This indicates that changes in near-surface suction can be used to determine the occurrence of rainfall events. Piezometer deployment can effectively collect larger suction measurements in later monitoring phases.

[0072] Figure 9A and Figure 9B Further comparisons were made of the combined volumetric water content reported by the two sets of moisture sensors in soil profiling. The volumetric water content change reported by the PICO 32 sensor at a depth of 0.75 m was unexpectedly greater than that at a depth of 0.2 m. In contrast, the water content change reported by the TEROS 12 sensor at a depth of 0.2 m near the surface was as expected, likely due to strong infiltration and evaporation during precipitation events. The average water content measured by the PICO 32 sensor was lower, at 37% at 0.2 m and 40% at 0.75 m, compared to 41% at 0.2 m and 45% at 0.75 m measured by the TEROS 12 sensor. Using the development of soil-moisture characteristic curves constructed from laboratory measurements, the water content recorded by the TEROS 12 showed good agreement with the volumetric water content measured in the field within the suction range corresponding to the soil-moisture characteristic curves. While both moisture sensor technologies measure the dielectric constant of the surrounding medium, the TEROS 12, after specific soil calibration, offers shorter measurement times and higher accuracy compared to the PICO 32. Leveraging its high installation consistency and low sensor-to-sensor variability, the TEROS 12 provides a more accurate field moisture content range and higher reliability. This comparison does not preclude the use of other types of moisture sensors besides capacitive moisture sensors (e.g., TDR moisture sensors), but rather verifies through testing that the selected moisture sensor can provide sufficiently accurate and consistent readings for use in the proposed slope condition monitoring system 200. Soil-moisture characteristics analysis

[0073] Piezometers are preferred because field studies have validated their ability to detect real-time transfer of soil moisture under weather-changing conditions and extend their suction measurement capabilities beyond conventional water-based tensiometers. A significant decrease in suction was observed immediately after rainfall events, and negative pore-water pressure slowly accumulated during drought periods. In-situ soil samples were collected from shallow layers (0 m to 1 m), and soil-moisture characteristic curves (SWCCs) were constructed in the laboratory. Centrifugation tests (low suction range, 0 kPa to 250 kPa) and WP4C potentiometers (high suction range, >250 kPa), aided by the best-fit equation, helped establish the SWCCs of the measured residual soil. Soil volumetric water content measured from TDR and capacitive moisture sensors, along with suction results derived from piezometer readings, were collected and plotted within the same framework of the soil moisture characteristic envelope, as shown below. Figures 10A to 11B As shown.

[0074] Figure 10A and Figure 10B Soil-moisture characterization analysis is presented based on combined measurements of water content and soil suction obtained from PICO 32 and TEROS 12 at a depth of 0.2 m. The dataset at 0.2 m depth is lower than the baseline SWCC due to the lower volumetric water content measurements obtained from the TDR sensor. The trends related to SWCC in the dataset acquired from PICO 32 differ from those obtained from the TEROS 12 sensor.

[0075] Figure 11A and Figure 11B The field datasets for the SWCC package line, PICO 32, and TEROS 12 at a depth of 0.75 m are shown. Although some scattered points from the PICO 32 were incorrectly reported as having large deviations from the baseline, most measurements conform to the SWCC. The groundwater level is close to the installation depth of 0.75 m. The field water content measurement range is much smaller compared to the dataset collected from 0.2 m shown in the TEROS 12 results. The average standard deviation among multiple volumetric water contents measured by multiple PICO 32 sensors is 24%, while the combined dataset from the TEROS 12 is more consistent and accurate, with an average standard deviation of 15%. The combination of piezometers and capacitive moisture sensors (such as the Meter Group TEROS 12 moisture sensor) demonstrates good performance.

[0076] Compared to the conventionally understood range of field suction and volumetric water content readings, the proposed slope monitoring system 200 is configured to provide a wider range of field suction and volumetric water content readings. More specifically, according to embodiments of this application, the detectable range is configured to cover, with high precision, the saturated zone (before the air-entry point) and the unsaturated zone below the soil-moisture characteristic envelope. Two-dimensional seepage analysis

[0077] Two-dimensional seepage analysis was performed using GeoStudio SEEP / W software available from Seequent (www.seequent.com). The volumetric water content measured at the Ruma Tingyi slope (from moisture sensors) was compared with the value determined through analysis (obtained through numerical analysis). The period from June 11th to June 19th was chosen because of the alternating wet and dry weather conditions during this time.

[0078] Figure 12 The slope model of the Ruma Tingyi site used in the SEEP / W analysis is shown. Based on pressure gauge measurements taken at 00:00 on June 11th, the groundwater levels at the slope crest and toe were modeled at depths of 8.4 m and 5.5 m, respectively. Rainfall recorded by the nearest weather station, Alexandra Road (0.6 km from the site), was used for the numerical analysis. The rainfall was applied as a unit water flux, and the potential seepage face was evaluated. Following standard slope geometry, the side boundaries were modeled as three times the slope height to minimize their impact on the slope seepage analysis. A zero-flux condition was applied to the side boundaries above the groundwater level, while a constant total head was applied below. A mesh size of 0.25 m was used for the seepage analysis.

[0079] Soil properties obtained at a depth of 0.2m were used for the first soil layer, which had a thickness of 0.5m. Soil properties obtained at a depth of 0.75m were used for the second soil layer. Figure 13A and Figure 13B Soil-moisture characteristic curves and permeability functions are shown respectively. Comparison of soil volumetric water content by numerical analysis and sensor measurement

[0080] The volumetric water content measurements obtained from the PICO 32 and TEROS 12 sensors at depths of 0.2 m and 0.75 m are plotted against the volumetric water content results obtained from SEEP / W analysis at depths of 0.05 m, 0.2 m, and 0.75 m, as shown in the figure. Figures 14A to 14C As shown. The SEEP / W results obtained from the upper, middle, and lower sections are relatively similar and do not show significant differences compared to the results observed from the moisture sensor. Therefore, Figures 14A to 14C Only the volumetric water content of the middle section was plotted to facilitate comparison with the moisture sensor measurements.

[0081] Soil properties (saturated permeability, K) obtained from laboratory tests were used. s The volumetric water content obtained from SEEP / W analysis at a depth of 2.97E-08 m / s did not correlate well with rainfall at depths of 0.2 m and 0.75 m, while moisture sensor measurements showed an increase in volumetric water content during rainfall events. Figure 14B and Figure 14C During the heavy rainfall events of June 12 and June 18, the SEEP / W volumetric water content at a depth of 0.2 m gradually increased over time, while moisture sensor readings showed a significant increase in volumetric water content. The SEEP / W volumetric water content at a near-surface (0.05 m) depth corresponded well to the rainfall, as an increase in volumetric water content was observed during the low-intensity rainfall period between June 14 and June 16. The increase in volumetric water content was smaller compared to moisture sensor readings.

[0082] Compared to moisture sensor measurements, the change in volumetric water content observed in the SEEP / W analysis was smaller, one possible reason being the low saturated permeability used in the analysis. The soil's low saturated permeability K... s The velocity (2.97E-08 m / s) likely limited rainfall infiltration and resulted in a small change in volumetric water content in the SEEP / W analysis. K at 2.97E-08 m / s s This was obtained by conducting constant head permeability tests on undisturbed soil samples obtained from the field using a triaxial cell. The samples may have been affected by alterations to in-situ soil properties, leading to inaccuracies in laboratory tests and resulting in lower saturation permeability.

[0083] To address potential measurement errors during laboratory testing, an additional SEEP / W analysis was performed as a sensitivity analysis by increasing the saturated permeability by an order of magnitude to 2.97E-07 m / s. This additional SEEP / W analysis was also used to assess whether the previous SEEP / W analysis using a higher saturated permeability could yield results similar to those obtained from field measurements by the moisture sensor. Figure 13B Showing higher K s The permeability function.

[0084] Using K, which is one order of magnitude higher, at 2.97E-07 m / s s SEEP / W analysis showed a significant difference in the trend of volumetric water content as soil permeability increased. With increasing permeability, an increase in volumetric water content at a depth of 0.2 m was observed during the heavy rainfall events of June 12th and June 18th. Figure 14B Furthermore, the SEEP / W results at a depth of 0.05 m showed a significant increase in volumetric water content during all rainfall events, with a rapid rate of decrease after each rainfall event. The observed trend is similar to the moisture sensor measurements from the TEROS 12 sensor.

[0085] The TEROS 12 sensor, located at a depth of 0.2m in the lower part of the slope, was most sensitive to rainfall. This is likely due to changes in the groundwater level at the toe of the slope. Figure 15 During the heavy rainfall event, the groundwater level at the toe of the slope rose to the surface, and during the analysis, the groundwater level at the toe changed significantly between depths of 0 m and 5 m. However, the groundwater level at the top of the slope changed only between depths of 4 m and 6 m below the surface. Therefore, due to the rise in groundwater level, a significant change in volumetric water content was observed in sensors installed on the lower section of the slope. However, this change was not observed in the SEEP / W results because the change in groundwater level depth in the analysis was small compared to the actual groundwater level depth measured by manometers on the slope. Figure 16The difference between analytical and field groundwater depths can be a significant reason for discrepancies between the volumetric water content measured by sensors on slopes and that determined by seepage analysis. Measurements obtained from the TEROS 12 sensor showed a similar range of volumetric water content to those obtained from SEEP / W analysis. The PICO 32 sensor at a depth of 0.2 m showed a relatively low range of volumetric water content (e.g., between 0.27 and 0.4). Even during periods of heavy rainfall, the maximum volumetric water content measured by the PICO 32 sensor was less than 0.4, while the TEROS 12 sensor measurement was closer to the saturated volumetric water content of the soil. In other words, compared to conventional purely analytical methods, the proposed slope condition monitoring system 200, which responds to meteorological changes and is at least partially based on real-time measurements, achieves higher accuracy in predicting impending or potential slope instability. Real-time slope warning system

[0086] The real-time slope instability warning system proposed in this application can be used to predict slope instability, thereby allowing engineers to take intervention measures and mitigate the potential consequences of slope instability.

[0087] According to embodiments of this application, a linear relationship or linear regression model between the safety factor and the volumetric water content is proposed to determine the safety factor, wherein the volumetric water content is the volumetric water content measured in real time (e.g., the volumetric water content measured by a moisture sensor).

[0088] In some embodiments, the linear relationship or linear regression model for a local area (e.g., the Rumadingyi slope used in the test) is obtained from the best fit results of regional water balance and slope stability analysis. Regional analysis used to establish the relationship between safety factor and volumetric water content.

[0089] First, regional water balance analysis can be performed using GEOtop software or any three-dimensional water balance model. This model can analyze groundwater flow, surface runoff, subsurface flow, and rainfall infiltration into saturated / unsaturated soil.

[0090] Soil properties used in regional water balance analysis can be obtained from known soil databases. In tests conducted in Singapore, the soil database used was developed and published by Li et al. (2022) (e.g., see Li, Yangyang & Rahardjo, Harianto & Satyanaga, Alfrendo & Rangarajan, Saranya & Lee, Daryl. (2022). “Soil database development with the application of machine learning methods in soil property prediction”. Engineering Geology Vol. 306, 106769, 10.1016 / j.enggeo.2022.106769). This soil database was developed for Singapore Island, which is divided into 97 zones, each with similar soil properties. The database includes the saturated permeability, saturated and unsaturated shear strength parameters, and the average SWCC for each zone.

[0091] The Ruma Tingyi slope is located within the JF20 region of the database of Li et al. (2022). One limitation of the GEOtop model is that the selected SWCC calculation formula may not be suitable for describing the conditions of a real slope. An extreme rainfall intensity of 22.2 mm / h was applied at a constant rate for 24 hours. Subsequently, a 24-hour rainfall-free period was applied in the analysis to assess the safety factor and the recovery of volumetric water content. Pore-water pressures and volumetric water content results obtained from the water balance analysis were used as inputs for the three-dimensional slope stability analysis at three key time steps: hour 1, hour 24, and hour 48. Slope stability analysis was performed using Scoops3D software to calculate the safety factor.

[0092] For three time steps (hour 1, hour 24, and hour 48), a linear regression model was used to best fit the results for the average volumetric water content and safety factor at depths of 0 m to 2 m. Since many slope instabilities observed in Singapore have shallow slip surfaces, the volumetric water content at depths of 0 m to 2 m was used. Additional analyses were performed to validate the appropriateness of using a linear regression model to represent the relationship between the safety factor and volumetric water content.

[0093] Table 3 summarizes the additional analyses performed to verify the linear relationship. The two types of additional analyses performed were extreme rainfall analysis and actual rainfall analysis. For extreme rainfall, additional time steps were analyzed in addition to the 1st, 24th, and 48th hours to compare the actual safety factor obtained from the Scoops3D software with the safety factor calculated from the linear regression model. Secondly, actual rainfall conditions were applied to the Rumadingyi slope to assess the volumetric water content and safety factor under actual rainfall conditions. Figure 17 This shows the rainfall intensity and time step considered in the actual rainfall analysis.

[0094] Figures 18A to 18D Linear regression lines for four different slopes within the JF20 region are shown. These linear regression lines were derived by best fitting data points at hours 1, 24, and 48. These slopes have R-values ​​exceeding 0.96. 2 The values ​​indicate that the linear regression line fits the data points well. The results of the additional analysis shown in Table 3 reveal a slightly different trend from the linear regression line. For all four slopes, the safety factor remained almost constant before the first hour of extreme rainfall (i.e., 15 minutes, 30 minutes, and 45 minutes). Therefore, the results suggest that the safety factor remains relatively constant during the first hour of rainfall, despite the observed increase in volumetric water content. For lower volumetric water content ranges, using the linear regression line to calculate the safety factor may lead to an overestimation of the safety factor. A safety factor cutoff was set, for example, limiting the maximum safety factor to the safety factor obtained from the analysis of the first hour of extreme rainfall. The best-fit results from the first hour, 24-hour, and 48-hour results obtained from the extreme rainfall analysis were used to represent the relationship between the safety factor and the volumetric water content ranging from the volumetric water content of the first-hour extreme rainfall to the soil saturation volumetric water content.

[0095] For volumetric water content ranging between the extreme rainfall analysis data points of the 1st and 48th hours, other data points obtained from supplementary analysis tend to be above the linear regression line. Therefore, the safety factor calculated from the linear regression line is relatively conservative.

[0096] For volumetric water content ranging between 24 and 48 hours, the actual safety factor is very close to the linear regression line. This indicates that the calculated safety factor is close to the actual safety factor.

[0097] The results from the analysis indicate some deviations from the linear regression line, particularly for the lower volumetric water content range. However, the results also demonstrate that the linear regression model with a safety factor cutoff is a feasible and practical method. A linear regression model can be developed by performing regional water balance and slope stability analyses at three key time steps: the first hour represents the initial safety factor, the 24th hour represents the minimum safety factor at the end of rainfall, and the 48th hour represents the recovery of the safety factor after rainfall. Performing additional analyses can help improve the accuracy of the predicted safety factor, although these additional analyses are considered unnecessary or computationally too costly. Case Study of Slope Alarm Supported by Real-Time Monitoring Based on the Internet of Things

[0098] By establishing a linear relationship between the safety factor and volumetric water content, a safety factor can be calculated for any volumetric water content measured by moisture sensors installed on the slope. The calculated safety factor can then be used to implement a real-time alarm system. The controller can be configured to immediately issue an alarm once the safety factor drops below a safety threshold, or once the safety factor reaches a low value (e.g., 1.5), indicating that the slope is about to become unstable, and allowing for timely slope stabilization measures to be taken. Calculate the safety factor using linear regression.

[0099] Figure 19 This diagram illustrates a safety factor calculated or determined based on TEROS 12 sensor readings. In some embodiments, because the change in volumetric water content recorded by the TEROS 12 sensor is greater than that recorded by PICO 32, the real-time safety factor for the slope can be calculated using the volumetric water content measured by the TEROS 12 sensor. Therefore, the safety factor calculated using the volumetric water content obtained from TEROS 12 will be lower than the safety factor calculated using the volumetric water content obtained from PICO 32. Due to differences in soil characteristics, such as saturated volumetric water content, the volumetric water content measured by the sensor will differ from the volumetric water content obtained from a soil database used in extreme rainfall analysis. For example, the saturated volumetric water content obtained from the database used in regional analysis has a value of 0.456, while some volumetric water contents measured from moisture sensors spike above 0.456 during heavy rainfall events. Therefore, a measurement of 0.456 for volumetric water content obtained by the sensor does not indicate that the soil is fully saturated, because the actual saturated volumetric water content is higher than 0.456. Therefore, substituting the original sensor measurements into the linear regression equation can lead to inaccurate calculations of the safety factor, because it assumes that the soil is fully saturated, which is not the case.

[0100] According to embodiments of this application, a correlation was established between the volumetric water content of the soil database and the actual soil volumetric water content. This solves the inaccuracies caused by simply substituting raw sensor measurement data into a linear regression model.

[0101] For soil characteristics obtained from databases used in regional analysis, the relevant factor c f It is introduced to calculate the actual soil saturation. This is achieved by using saturated VWC. 传感器 and saturated VWC 数据库 Two terms are used to calculate the correlation factor. Saturated VWC 数据库 Saturated volumetric water content (VWC) refers to the water content obtained from the database used to generate linear regression lines from extreme rainfall analysis. 传感器 This refers to the laboratory saturated volumetric water content (e.g., the saturated volumetric water content obtained from laboratory tests). If the field saturated volumetric water content measured by sensors placed on the slope exceeds the laboratory saturated volumetric water content, then the saturated VWC is considered to be higher. 传感器 The value is considered as the actual saturated volumetric water content. The following calculation formula (1) shows the saturated VWC (saturated VWC) based on the database. 数据库 ) and sensor saturated VWC (saturated VWC) 传感器 Determine the relevant factor c f The method.

[0102] The volumetric water content results obtained from the TEROS 12 sensor have a maximum value that exceeds the saturated volumetric water content obtained from laboratory tests. Volumetric water content readings were limited to maximum values ​​of 0.478 at a depth of 0.2 m and 0.520 at a depth of 1.75 m. The correlation coefficients calculated using Equation 1 for the 0.2 m and 0.75 m depths were 95.5% and 87.8%, respectively. These correlation coefficients were then applied to the upper limit volumetric water content readings obtained from each sensor. Subsequently, the average volumetric water content obtained from the 0.2 m and 0.75 m sensors was calculated for the upper, middle, and lower sections of the slope, and these values ​​were substituted into a linear regression equation to calculate the slope safety factor. Figure 19 The minimum safety factors are shown for the upper, middle, and lower sections of the slope. During rainfall events, the calculated safety factors decreased significantly due to a substantial increase in volumetric water content recorded by sensors. The recovery of the safety factors was very slow.

[0103] A field study conducted in Ruma Tinggi, Singapore, from March to July 2023 successfully demonstrated the feasibility and efficiency of real-time monitoring of unsaturated slopes using Internet of Things (IoT)-based technologies. By integrating advanced moisture sensors and piezometers into a comprehensive IoT architecture, a novel method for continuously monitoring the hydrological characteristics of slopes under varying meteorological conditions was experimentally validated. The proposed slope condition monitoring system has proven useful for assessing or predicting slope stability, particularly in cases of shallow slope instability induced by climate change and potential rainfall, extending its application beyond basic field monitoring and safety factor calculations.

[0104] Based on a field performance comparison of the PICO 32 and TEROS 12 moisture sensors measured in Ruma Dingyi, the results obtained from the TEROS 12 showed more consistent measurement results. During the monitoring period, the average value at a depth of 0.2m was 41%, and the average value at a depth of 0.75m was 45%. In contrast, the PICO 32 reported an unexpectedly large change in volumetric water content at a depth of 0.75m, with a lower average water content. The average water content at 0.2m was 37%, and the average water content at 0.75m was 40%, which deviated from the soil-water characteristic curve. The average standard deviation among the multiple volumetric water contents measured by the PICO 32 sensors was 24%, while the average standard deviation of the TEROS 12, which showed significantly lower inter-sensor variability, was 15%. Therefore, the slope condition monitoring system proposed in this paper has been verified to significantly improve the accurate acquisition of soil-water dynamics, thereby providing information for slope stability analysis.

[0105] The proposed linear regression model provides a practical indicator of the relationship between volumetric water content and safety factor, utilizing results from regional water balance and slope stability analyses under extreme rainfall conditions. The proposed system allows for the calculation of safety factors for slope stability analysis using in-situ volumetric water content and provides real-time assessments of potentially hazardous slopes at the same 10-minute interval resolution as the moisture sensor response. Safety factors calculated from field measurements in Ruma Tingyi ranged from 3 to 5, with two major downward peaks recorded during two heavy rainfall events in June 2023. Real-time recovery of the slope safety factor was also observed during the drought period following the rainfall events. The ability to calculate safety factors in real time using the developed linear regression model marks a crucial step towards implementing proactive landslide risk management strategies.

[0106] It is anticipated that the proposed slope condition monitoring system can be applied to various climatic and soil conditions. Furthermore, it is anticipated that expanding the sensor network to cover a wider range of soil types and topography, and integrating more sophisticated machine learning algorithms, will improve the accuracy of slope instability event prediction.

[0107] The proposed slope condition monitoring system, integrated with IoT technology, provides a transformative tool for geotechnical engineering and slope condition monitoring practices, paving the way for building more resilient infrastructure in the face of increasing climate risks. Compared to conventional methods, the proposed system offers significant improvements, providing higher-resolution data, real-time analysis capabilities, and a more cost-effective solution for long-term slope stability assessment. This application contributes to advancing slope stability analysis and, to some extent, realizes the application potential of IoT in a wider range of geotechnical engineering and environmental monitoring fields.

[0108] According to various embodiments of this application, a slope condition monitoring method includes acquiring volumetric water content from multiple sensors installed on the slope. The method includes determining a safety factor for the slope using the volumetric water content based on a linear regression model obtained from simulated volumetric water content corresponding to humid weather conditions. The method also includes generating an alarm in response to the safety factor being determined to be below a safety threshold, indicating potential slope instability.

[0109] Slope condition monitoring methods may include using volumetric water content to determine plant health indicators of one or more plants on a slope, wherein an alarm is generated in response to any of the following: a safety factor is determined to be below a safety threshold, or a plant health indicator is below a health threshold.

[0110] Slope condition monitoring methods may include: using volumetric water content to determine the plant health index of one or more plants on the slope, wherein an alarm is generated in response to a combination of the following: the safety factor is determined to be below a safety threshold, and the plant health index is below a health threshold.

[0111] The following combinations can be considered as signs of potential slope instability: (i) a safety factor below the safety threshold; and (ii) plant health indicators below the health threshold.

[0112] Plant health indicators for slope condition monitoring methods can include the ratio of altered fluorescence to maximum fluorescence.

[0113] Slope condition monitoring methods may include determining the relationship between plant health indicators and volumetric water content.

[0114] Determining the safety factor of a slope may involve applying a correlation factor to the volumetric water content, which is the ratio of the saturated volumetric water content in the database to the saturated volumetric water content in the sensor.

[0115] The sensor saturated volumetric water content can be obtained from one of the following: (i) laboratory saturated volumetric water content, and (ii) field saturated volumetric water content measured by the sensor at a selected slope.

[0116] The saturated volumetric water content of the sensor can be selected from the larger of the laboratory saturated volumetric water content and the field saturated volumetric water content measured by the sensor at a selected slope.

[0117] The safety threshold in slope condition monitoring methods can be a relatively low value, such as preferably 1.5.

[0118] The slope condition monitoring method may also include: acquiring multiple volumetric water contents at a first sampling frequency in response to rainfall; and acquiring multiple volumetric water contents at a second sampling frequency in response to the absence of rainfall, wherein the first sampling frequency is higher than the second sampling frequency.

[0119] The slope condition monitoring method may also include: determining the change from no rainfall to rainfall in response to sensing a peak increase in volumetric water content over a period of time; and shortening the sampling interval between multiple consecutive measurements of volumetric water content in response to determining the change from no rainfall to rainfall.

[0120] The slope condition monitoring method may also include: determining the change from rainfall to no rainfall in response to sensing a decrease in volumetric water content over a subsequent period of time; and extending the sampling interval between consecutive measurements of volumetric water content in response to determining the change from rainfall to no rainfall.

[0121] A slope condition monitoring system, at least partially installed on a selected slope, includes multiple sensors and a controller disposed on the selected slope. The multiple sensors are configured to provide the volumetric water content of the selected slope. The controller is configured to communicate with the multiple sensor signals. The controller is configured to perform a slope condition monitoring method according to one or more embodiments described above.

[0122] Multiple sensors may include: multiple piezometers located at the top and bottom of the selected slope; and multiple capacitive moisture sensors distributed at different elevations on the selected slope.

[0123] All examples described herein, whether apparatus, methods, materials, or products, are presented for illustrative and comprehension purposes only and are not intended to be limiting or exhaustive. Modifications can be made by those skilled in the art without departing from the scope of the claims of this application.

Claims

1. A slope condition monitoring method for monitoring slopes, comprising: Volumetric water content is obtained from multiple sensors installed on the slope; The safety factor of the slope is determined using the volumetric water content based on a linear regression model obtained from the simulated volumetric water content corresponding to humid weather conditions. An alarm is generated in response to the safety factor being determined to be below a safety threshold, indicating that slope instability may occur.

2. The slope condition monitoring method according to claim 1, comprising: The volumetric water content is used to determine a plant health indicator for one or more plants on the slope, wherein the alarm is generated in response to any of the following: The safety factor is determined to be lower than the safety threshold; and The plant health indicators were below the health threshold.

3. The slope condition monitoring method according to claim 1, comprising: The volumetric water content is used to determine a plant health indicator for one or more plants on the slope, wherein the alarm is generated in response to a combination of the following: The safety factor is determined to be lower than the safety threshold; and The plant health indicators were below the health threshold.

4. The slope condition monitoring method according to claim 3, wherein a combination of the following is used as an indication of possible slope instability: (i) The safety factor is lower than the safety threshold; and (ii) The plant health index is below the health threshold.

5. The slope condition monitoring method according to any one of claims 2 to 4, wherein the plant health indicator includes the ratio of altered fluorescence to maximum fluorescence.

6. The slope condition monitoring method according to any one of claims 2 to 5, comprising: Determine the relationship between the plant health index and the volumetric water content.

7. The slope condition monitoring method according to any one of claims 2 to 5, wherein determining the safety factor of the slope includes: The relevant factor is applied to the volumetric water content, which is the ratio of the database saturated volumetric water content to the sensor saturated volumetric water content.

8. The slope condition monitoring method according to claim 7, wherein the sensor saturated volumetric water content is obtained by one of the following: (i) laboratory saturated volumetric water content; and (ii) field saturated volumetric water content measured by the sensor on the selected slope.

9. The slope condition monitoring method according to claim 7, wherein the sensor saturated volumetric water content is selected from the larger of the laboratory saturated volumetric water content and the field saturated volumetric water content measured by the sensor on the selected slope.

10. The slope condition monitoring method according to claim 1, wherein the safety threshold is 1.

5.

11. The slope condition monitoring method according to any one of claims 2 to 10, further comprising: In response to rainfall, water content was collected at multiple volumes using the first sampling frequency; as well as In response to the absence of rainfall, multiple volumetric water content samples are collected at a second sampling frequency, where the first sampling frequency is higher than the second sampling frequency.

12. The slope condition monitoring method according to claim 11 further includes: The peak increase in volumetric water content over a period of time was used to determine the change from no rainfall to rainfall. as well as In response to the determination of a change from no rainfall to rainfall, the sampling interval between multiple consecutive measurements of volumetric water content is shortened.

13. The slope condition monitoring method according to claim 11 further includes: The change from precipitation to no precipitation was determined in response to a subsequent decrease in volumetric water content over a period of time; as well as In response to determining the change from rainfall to no rainfall, the sampling interval between multiple consecutive measurements of volumetric water content is extended.

14. A slope condition monitoring system at least partially installed on a selected slope, the slope condition monitoring system comprising: Multiple sensors are installed on the selected slope, and the multiple sensors are configured to provide the volumetric water content of the selected slope; as well as A controller configured to communicate with the plurality of sensor signals and to perform a slope condition monitoring method according to any one of the preceding claims.

15. The slope condition monitoring system according to claim 14, wherein the plurality of sensors comprises: Multiple piezometers are installed at the top and bottom of the selected slope; as well as Multiple capacitive moisture sensors are distributed at different elevations on the selected slope.

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