Assessing landslide velocity scales with acoustic emission active waveguides for early warning system

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Mahapatro, Sushil Kumar Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4891330/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 May, 2025 Read the published version in Natural Hazards → Version 1 posted 5 You are reading this latest preprint version Abstract This study proposes a strategy to predict the different landslide velocity scales of susceptible slopes by analysing the acoustic emission (AE) behaviour of the active waveguide system (AWS). Laboratory compression tests were conducted on models of AWS utilizing a universal testing machine to induce strain-induced interactions within the backfill material, resulting in the generation of AE signals. AE characteristics of AWS has been analysed at deformation rates ranging from slow (0.003 mm/min) to rapid (30.0 mm/min) rates of Varnes’ landslide velocity scales. Two intermediate scales (0.03 and 3.0 mm/min) have been introduced between slow, moderate, and rapid rates of landslide velocity scales. AE characteristics, including signal duration, counts, acoustic signal level, amplitude, signal strength, and their derivatives were meticulously analysed for each velocity scale. A strong proportionality relationship was observed between cumulative AE counts and deformation rate of AWS. Quadratic correlation was established between AE signal strength and amplitude. AE activity of the AWS for different velocity scales were also analysed. Significant results observed and correlations were validated using another different set of AWS. Additionally, one test consisting all the velocity scales in sequential order was conducted on the AWS and results were reverified. This study can significantly contribute to developing real-time landslide early warning systems that issue alerts based on varying landslide velocities and slope instability stages, as reflected in the AE data of AWS. Geotechnical engineering landslides deformation acoustic emission (AE) active waveguide system (AWS) landslide velocity scales soil slope instability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Landslides are the natural disasters that refers to release of a huge mass of a slope downwards under the driving gravitational force(Bobrowsky & Highland, 2013 ). Landslide disasters cause significant loss of life, destruction of infrastructure, disruption of transportation, economic losses, and environmental damages(Dai, Lee & Ngai, 2002 ). Unified global landslide database indicates the concerning number of landslide disasters throughout the globe(Gómez, García & Aristizábal, 2023 ). Bulk failure of a slope is influenced by a huge number of factors including rainfall, slope angle, nature of the rock underneath the slope, geographical conditions, human interventions etc.(McColl, 2022 ). In the view of minimization of landslide risks and losses, developing a robust and reliable landslide early warning system (LEWS) is the need of the hour. The primary function of a LEWS is to monitor a landslide prone slope for its deformation dynamics and generate alerts/ warnings when the risk of slope failure becomes critical(Segoni, Piciullo & Gariano, 2018b ). Since rainfall is the prime triggering factor for landslides, majority of LEWS are based on direct or indirect measurement of water content in the soil or rainfall threshold models(Segoni, Piciullo & Gariano, 2018a ). The major shortcoming of LEWS based solely on rainfall thresholds is that it does not provide real-time state of the slope in terms of slope deformation parameters(Guzzetti et al., 2020 ). Another disadvantage is that the rainfall threshold models are not universal but varies with site conditions. Additionally, factors like rainfall and water content are not instantaneously reflected in the slope instability. Hence, real-time monitoring of slope is necessary for assessing the instability of slopes and offers immense potential for designing such LEWS(Pecoraro, Calvello & Piciullo, 2019 ). Geotechnical monitoring of slopes has become mainstream method to assess the physical parameters of slope deformation dynamics(Thirugnanam et al., 2022 ). Displacement, velocity, and acceleration of slope deformation can be monitored using various geotechnical instruments like inclinometers, extensometers, time-domain reflectometry, accelerometers, etc. Based on the data of slope deformation dynamics, prediction of time of slope failure is calculated. However, there is one major problem that one encounters while designing a LEWS that there is no particular constant value of slope deformation rate at which the slope collapses. It differs from slope to slope depending on numerous site and geological factors as stated above. Hence, developing a standard LEWS corresponding to deformation rates of the slope has been a tedious task. The Varnes classification system, developed by David J. Varnes in 1978, is the most widely used method for categorizing landslides (Hungr, Leroueil & Picarelli, 2014 ). It deals with landslide classifications based on type of materials and slope movements. It also encompasses different classes of landslide velocities along with their probable destructive significance. The landslide velocity scales consisted of 07 velocity classes ranging from extremely slow to extremely rapid rates of slope deformations along with their probable destructive significance (Fig. 1 ). Varnes’ classification of landslide velocity scales has been widely accepted throughout the scientific community for landslides. A LEWS oriented around Varnes’ classification of landslide velocity scales can be considered as a framework for a standard and more generalised LEWS. In this study, a potential soil slope deformation monitoring system based on Varnes’ landslide velocity scales has been discussed. Velocity class of slow, moderate, and rapid is of interest for a LEWS working on slope deformation assessments. A slope monitoring system that can detect and identify deformation rates corresponding to each velocity class can be instrumentalized to develop a standard LEWS working on Varnes’ landslide velocity scale. Amongst all the geotechnical methods to assess slope deformation dynamics, acoustic emission (AE) technology has emerged as a state-of-the-art for monitoring landslide prone slopes (Zaki et al., 2014 ; Mao, Hei & Yang, 2021 ). It is a non-destructive testing method used for structural health monitoring of various structures and materials(Gholizadeh, Leman & Baharudin, 2015 ). The principle of AE technology is to detect and capture ultrasonic mechanical waves generated by virtue of material deformations and process them for material evaluations(Anon, 2008). AE technology-based geotechnical monitoring of soil slopes has become quite mainstream in the past two decades(Mao, Hei & Yang, 2021 ). AE behaviour of a soil slope has been instrumentalized to assess the instability extent of the slope in variety of research articles(Zaki et al., 2014 ). Active waveguide system (AWS) is the key part of the entire instrumentation of AE technology-based soil slope monitoring(Dixon, Hill & Kavanagh, 2003 ). AWS deals with the problem of high attenuation coefficients of soil media for AE signals(Shiotani & Masayasu Ohtsu, 1999). Soil slope shear deformations deform the AWS and cause interactions among backfill material particles i.e., generally rough angular gravel particles. These interactions generate AE signals which are captured by AE sensors placed on the central metal waveguide. AE sensors transfer the signals to AE data acquisition and analysis system (DAAS) for signal conditioning and AE signal parameters extraction. This way, slope shear deformations are captured in terms of AE signals using AWS. Figure 2 represents the methodology of monitoring of a soil slope using AE technology. Slope deformation dynamics (displacement, velocity, and acceleration) has been both qualitatively and quantitatively correlated with AE signal parameters of AE signals arising from AWS(Kumar, Mahapatro & Singh, 2024a ; Zhang, Wang & Meng, 2024 ; Deng et al., 2021b , 2024b , 2021c ; Dixon et al., 2018 ; Smith, Dixon & Fowmes, 2017 ). At the time of invention of the concept of AWS, rate of ground deformation i.e., velocity was found to be related to the generated AE signals on an unstable soil slope(Dixon, Hill & Kavanagh, 2003 ). Generally, greater the extent of AE signals arising from the slope, higher is the instability extent of the slope and vice-versa. Laboratory testing of AWS on compression or shear testing machines and shear testing fixtures has been reported for correlation between AE characteristics and deformation parameters(Smith & Dixon, 2015 ; Deng et al., 2019 ; Dixon & Spriggs, 2007 ). Quantification of AE signal parameters with respect to slope deformation dynamics is also reported in these articles. Mathematical empirical relationships have been published displacement, velocity and various AE signal parameters like counts, energy, and amplitude etc. Deployment of active waveguides on real-time landslide prone slopes has been performed and the relationship between trends of rainfall, slope displacement parameters, and AE signal parameters are very promising (Berg et al., 2018 ; Dixon et al., 2015a , 2015b ; Smith et al., 2014a , 2014b ). In our recent studies, a potential LEWS at a threshold deformation rate of 1 mm/min(Kumar, Mahapatro & Singh, 2023 , 2024a ) and IoT based landslide detection systems(Vashista et al., 2024 ) has been discussed. In this study, the focus is more concentrated on a standard LEWS at Varnes’ landslide velocity scales. The primary step to evaluate slope deformation parameters from AE technology is to calibrate the AE behaviour of the AWS with respect to its deformation dynamics. Then this behaviour is expected to replicate up to some extent on real-time landslide prone slopes. Experimental methodology Laboratory testing of AWS to analyse the AE behaviour in response to various deformation dynamics has been quite efficient and reliable in monitoring a real-time landslide prone soil slope. It prominently involves confined laboratory compression testing (Kumar, Mahapatro & Singh, 2024a ; Dixon & Spriggs, 2007 ; Smith & Dixon, 2015 ; Zhang, Wang & Meng, 2024 ; Deng et al., 2024a ) and shear testing of AWS(Dixon et al., 2018 ; Deng et al., 2021a ; Smith, Dixon & Fowmes, 2017 ; Deng et al., 2021b , 2019 , 2021c ). However, it is noteworthy that the stress states of AWS in laboratory confined compression testing and real-time field testing are not identical. The main objective of compression testing of AWS is to study the interactions among backfill material i.e., gravel particles in this study at different deformation dynamics. The gravel interactions are in stimulus to the alternating deformation rates and not the static loads. The first step of monitoring soil slope deformations using AWS is to calibrate the AE behavior of the AWS with respect to different velocities. This is done by the compression testing, 03-point bend testing, or shear testing of AWS in the laboratory. The results obtained in the laboratory deformation testing are very useful as they provide critical insights about the trends of AE signal parameters with actual slope deformation parameters like displacement, velocity, and acceleration. In this study, a universal testing machine has been used to calibrate the AE behaviour of AWS with respect to different landslide velocity scales. For the calibration, AWS is subjected to various deformation processes under a confined and controlled laboratory compressive test. Simultaneous data from deformation dynamics of AWS is collected on the UTM and corresponding AE data is recorded on AE data acquisition and analysis system (DAAS). Based on the analysis of AE signal parameters, each landslide velocity scale is characterized. The aim of the study is to conceptualize a potential LEWS based on multi-AE parameter assessment of landslide velocity scale and slope instability state. Materials Based on the optimized active waveguide components in our previous studies(Kumar, Mahapatro & Singh, 2023 , 2024a ), indigenous broadband AE sensor placed on the top of an aluminium central waveguide is used. The central waveguide has 2000 mm length, 50 mm outer diameters, and 4–5 mm wall thickness. Indigenous AE sensor (Fig. 3 (a) ) has a wideband frequency response in 20–80 kHz frequency domain. Sensor Highway-III system made by Physical Acoustics Corporation; USA (Fig. 3 (b) ) is used as AE data acquisition and analysis system (DAAS). Based on the findings of (Kumar, Mahapatro & Singh, 2024b ), backfill material and outer flexible membrane is selected. Rough angular gravel particles with average size 10 mm (Fig. 3 (c) ) are used as backfill material. Double wall corrugated high density polyethylene pipe with 1500 mm length and 160/ 200 mm outer diameter is the outer flexible membranes used to encapsulate the central waveguide and gravels. Universal testing machine (UTM) with 100 kN capacity is used for compression testing on AWS. Experimental set-up and procedure Five scales of landslide velocity I-V (slow, intermediate, moderate, critical intermediate, and rapid respectively) are taken as input deformation rates for AWS at UTM. Deformation rates as per Varnes’ landslide velocity scale are assigned to each scale (0.0035, 0.03, 0.3, 3.0, and 30.0 mm/min respectively). Keeping the limitations of UTM into consideration, slowest rate of deformation is kept at 0.0035 mm/min instead of 0.003 mm/min. Total deformations produced in the AWS are approximately 20–30 mm and test duration varied accordingly. Table 1 lists the landslide velocity scales with their description and deformation rates for testing of AWS on UTM. Table 1 Landslide velocity scales and corresponding deformation rates for testing of AWS on UTM. S. No. Landslide velocity scale Deformation rate (mm/min) Test duration (mins) Slope instability state 1. Slow (I) 0.0035 5550 Stable slope 2. Intermediate (II) 0.03 720 Intermediate state 3. Moderate (III) 0.3 70 Unstable slope 4. Critical intermediate (IV) 3.0 07 Critically unstable 5. Rapid (V) 30.0 01 Danger AWS is placed horizontally on the UTM as represented in Fig. 4 . Load is applied at centre point of outer flexible membrane. Constant deformation rates are applied using UTM and corresponding generated AE activities in the backfill material of AWS is captured and collected for post-analysis using AE DAAS unit. The prime focus of the study is to analyse the interaction of gravel particles in terms of AE signal parameters with respect to different deformation rates or landslide velocity scales. Following that, establishing a method to distinguish and identify each landslide velocity scale based on AE parametric analysis alone. The AE data was acquired at a threshold amplitude value of 40 dB. For data processing and analysis, the AE data was filtered for AE signals higher than or equal to 50 dB to eliminate low strength and unwanted AE signals. Results The first AE signal parameter analysed was signal duration. AE signal duration is the total time elapsed between the first threshold crossing and last threshold crossing of AE signal. AE signal duration of AE signals captured was plotted against time for each scale (I-V) (Fig. 5 ). Figure 5 (a) has 04 individual graphs: one for each day of test at 0.0035 mm/min. A clear distinction was observed for landslide velocity scale V (Fig. 5 (e) ) that duration of AE signals was saturated to 1,000,000 µsec (1000 ms). This is the maximum duration for capture of AE signals, kept in AE DAAS for signal acquisition. Additionally, there was substantial increment in the signal durations of AE signals with increasing deformation rate. Also, along the time axis, density of AE signals increased with increasing rate. AE counts represent the number of times AE signal waveform crosses the predefined threshold amplitude value. AE counts per minute was calculated throughout the test duration and then cumulative AE counts were plotted against time (mins). Polynomial curves with excellent R 2 values of 0.98–0.99, 95% confidence band, and 95% prediction band were fitted on the graphs (Fig. 6 ). For each polynomial fit, the intercept was kept zero, since at time t = 0, there are no AE counts. The equation for best fit polynomial curve with zero intercept was: $$\:\varvec{y}=\varvec{\beta\:}{\varvec{x}}^{2}+\varvec{\alpha\:}\varvec{x}\:$$ In the polynomial curve equation, coefficient of x 2 (denoted by β) was found to be correlated with the actual deformation rate on AWS. The velocity or deformation rate of AWS was directly proportional to sqrt (β). $$\:\varvec{D}\varvec{e}\varvec{f}\varvec{o}\varvec{r}\varvec{m}\varvec{a}\varvec{t}\varvec{i}\varvec{o}\varvec{n}\:\varvec{r}\varvec{a}\varvec{t}\varvec{e}\propto\:\surd\:\varvec{\beta\:}$$ $$\:\varvec{D}\varvec{e}\varvec{f}\varvec{o}\varvec{r}\varvec{m}\varvec{a}\varvec{t}\varvec{i}\varvec{o}\varvec{n}\:\varvec{r}\varvec{a}\varvec{t}\varvec{e}=\varvec{c}\:\surd\:\varvec{\beta\:}$$ The constant of proportionality (c) can be found by preliminary calibration of AWS prior to deployment on landslide prone field site. In case of 200 mm AWS discussed here, the constant of proportionality was found to be 0.05 empirically. Deformation rate was calculated using this proportionality relationship and compared with actual rate of deformation. Values of actual and calculated deformation rates were quite close as indicated by Table 2 . Table 2 Actual and AE calculated deformation rate of 200 mm AWS. S. No. Actual deformation rate (mm/min) Coefficient of x 2 (β) \(\:\surd\:\varvec{\beta\:}\) c AE calculated deformation rate (mm/min) (= \(\:\varvec{c}\:\surd\:\varvec{\beta\:}\) ) 1. 0.0035 (I) 0.005 0.0707 0.05 0.00354 2. 0.03 (II) 0.407 0.636 0.05 0.0318 3. 0.3 (III) 63.49 7.968 0.05 0.398 4. 3.0 (IV) 6126.9 78.275 0.05 3.914 To check the validity of this proportionality relationship between deformation rate and β, another AWS was studied. A new 160 mm AWS with same backfill material and central waveguide assembly was subjected to same deformation rates under UTM. Figure 7 contains the graphs for cumulative AE counts vs. time at different landslide velocity scales along with their best fit polynomial curves. Best fit polynomial curves had excellent R 2 values of 0.98–0.99. In the case of 160 mm AWS, the constant of proportionality with was empirically found to be approximately 0.04. Values of actual and AE calculated deformation rates in case of 160 mm AWS are stated in Table 3 . Table 3 Actual and AE calculated deformation rate of 160 mm AWS. S. No. Actual deformation rate (mm/min) Coefficient of x 2 (β) \(\:\surd\:\varvec{\beta\:}\) c AE calculated deformation rate (mm/min) (= \(\:\varvec{c}\:\surd\:\varvec{\beta\:}\) ) 1. 0.0035 (I) 0.0043 0.0656 0.04 0.00262 2. 0.03 (II) 0.652 0.807 0.04 0.0323 3. 0.3 (III) 53.92 7.343 0.04 0.294 4. 3.0 (IV) 10233.26 101.159 0.04 4.046 Acoustic Signal Level (ASL) is a statistical measure calculated as the average of the amplitude of AE signals. ASL considers the AE amplitude on a logarithmic scale, it is typically reported in decibels (dB). ASL values for each AE signal at different landslide velocity scales (I-V) are plotted in Fig. 8 . The base line for ASL for landslide velocity scales I, II, and III was below 30, whereas for landslide velocity scales IV and V. it was over 35 and 40 respectively. While plotting ASL values, it was observed that total number of AE signals are increasing from landslide velocity scale I to IV. It was interesting that landslide velocity scale V has least number of AE signals while having the maximum duration of captured AE activities as discussed earlier. It was because approximately each AE signal at landslide velocity scale V was having maximum duration of 1000 ms. Now the focus is on landslide velocity scales I-IV. To normalize the comparison, AE signals per minute was calculated by dividing total number of AE signals by total test duration in minutes. The value of AE signals per minute for landslide velocity scales I, II, III, and IV was calculated to be 0.18, 2.08, 30.94, and 645.29 respectively. Table 4 Order of number of AE signals per minute for different landslide velocity scale. This observation was verified with 160 mm AWS. Table 5 comprises of the results regarding Number of AE signals captured in case of 160 mm AWS. S. No. Landslide velocity scale AE signals per minute Scientific notation Order of number of AE signals per minute 1. I 0.18 1.8E-1 -1 2. II 2.08 2.08E0 0 3. III 30.94 3.09E + 1 + 1 4. IV 645.29 6.45E + 2 + 2 Table 5 Number of AE signals per minute for different landslide velocity scale in case of 160 mm AWS. S. No. Landslide velocity scale Total AE signals Test duration (mins) AE signals per minute Order of number of AE signals per minute 1. I 868 5550 0.156 -1 2. II 1716 720 2.383 0 3. III 1368 70 19.543 + 1 4. IV 2684 7 383.429 + 2 Amplitude is the maximum peak voltage level reached by AE signal waveform for a burst AE signal. It is generally recorded in units of decibel (dB) which is the logarithmic scale of voltage value with respect to reference voltage of 1 µV. The parameter of AE signal strength represents the value of time integral of the absolute signal voltage. Its unit is pV-s in Sensor Highway-III system. AE signal strength of AE signals was plotted against amplitude for each landslide velocity scale (Fig. 9 ). A quadratic relationship was found between the two AE signal parameters represented by the best fit curves of the graphs. For the best fit curve equation, the values of intercept, coefficient of x and x 2 increased with increasing deformation rate. Following the individual testing of AWS at different constant deformation rates, single test consisting of all the velocity scales in sequential order was conducted on the AWS and results were reverified. This test also helped to check the sensitivity of this approach for real-time accelerating slope movements. The 200 mm AWS was subjected to successive deformation rates of 0.0035, 0.03, 0.3, 3.0 and 30.0 mm/min in continuation one after the other. The description of test deformation rates and their respective time durations are provided in Table 6 . However, the AE DAAS was paused to unload the AWS from UTM and test 30.0 mm/min from thereon. AE DAAS resumed with UTM deforming AWS at 30.0 mm/ min. So, in terms of AE data, the rates were consecutively one after the other. Table 6 Landslide velocity scales and corresponding deformation rates for testing of AWS in context of accelerating slope deformations. S. No. Landslide velocity scale Deformation rate (mm/min) Test duration (mins) Deformation produced (mm) 1. Slow (I) 0.0035 1440 05 2. Intermediate (II) 0.03 180 5.4 3. Moderate (III) 0.3 20 06 4. Critical intermediate (IV) 3.0 05 15 5. Rapid (V) 30.0 01 30 First observation was that abrupt changes in AE signal parameters were expected at 1441, 1621, 1641, and 1646 mins of test respectively indicating the change of landside velocity scale. Plot of cumulative AE counts vs. time (mins) indicated these prominent changes as shown in Fig. 10 . As stated at the start of results section, AE signal duration was used to detect the landslide velocity scale V with saturation of AE signals at 1000 ms (10 6 µs) as shown in Fig. 11 . Order of AE signals per minute for each landslide velocity scale was also validated in the analysis of accelerating deformations of AWS. Each velocity scale was distinguishable and identifiable based on the order of AE signals per minute (Table 7 ). Proportionality relationship between deformation rate and sqrt (β) was also verified for each scale separately. Table 7 Landslide velocity scales and corresponding deformation rates for testing of AWS in context of accelerating slope deformations. S. No. Landslide velocity scale Deformation rate (mm/min) Test duration (mins) Total no. of AE signals captured Order of AE signals per minute 1. Slow (I) 0.0035 1440 186 -1 2. Intermediate (II) 0.03 180 839 0 3. Moderate (III) 0.3 20 1836 + 1 4. Critical intermediate (IV) 3.0 05 4395 + 2 Discussion This study explores the potential of utilizing acoustic emission (AE) data from soil slopes to design a generalized landslide early warning system based on Varnes' landslide velocity scales. Laboratory testing of active waveguide system (AWS) has proven efficient and reliable for analysing the AE behaviour in response to various deformation dynamics in landslide-prone soil slopes. Key methods include confined compression and shear testing of AWS, as detailed in studies by (Deng et al., 2024b , 2021c , 2019 , 2021a , 2024a ; Kumar, Mahapatro & Singh, 2023 , 2024a ; Zhang, Wang & Meng, 2024 ; Smith & Dixon, 2015 ; Smith, Dixon & Fowmes, 2017 ; Dixon & Spriggs, 2007 ; Dixon et al., 2018 ). However, the deformation dynamics subjected to AWS varied with each study, which created an unavailability of a standard AE calibration of AWS. In present study, AWS is subjected to deformation rates as per Varnes’ landslide velocity scales and their AE behaviour is calibrated. AE signal parameters of duration, counts, order of AE signals, amplitude, signal strength etc. have been qualitatively and quantitively analysed to be correlated with particular landslide velocity scale. The results and observations were validated and reverified on a different set of AWS. The proposed LEWS can predict a particular landslide velocity scale and its associated slope instability state based on the analysis of AE behaviour of AWS installed on the slope. Figure 12 represents the prime objective of the study i.e., conceptualization of a generalised LEWS generating warnings at different scales of landslide velocity. Conclusion This study addresses the AE characteristics of AWS at different landslide velocity scales. AE signal parameters of AWS have been analysed at deformation rates corresponding to five landslide velocity scales. Different stages of slope instabilities descripted on the landslide velocity scales are identified and evaluated based on AE signals recorded on the AE data acquisition and analysis system. Landslide velocity scale V can be identified by analysis of AE signal duration. Saturation of AE signal duration at 1000 ms indicates the landslide velocity scale V. Landslide velocity scale I, II, III, and IV can be identified by two criteria: one is the proportionality relationship between the deformation rate and coefficient (β) of polynomial fit of cumulative AE counts, and another is the order of the number of captured AE signals per unit time. Accelerating slope deformations or change of landslide velocity scales is quite evident by the abrupt change in AE signal parameters. However, it should be noted that deformations produced in the AWS are under controlled and confined laboratory set-up which might vary up to some extent on a real-time landslide prone slope. Laboratory studies on soil shear testing fixture are under progress to simulate a more realistic approach to the testing of AWS and calibration of its AE behavior. Preliminary results are positive and will be shared in our upcoming publications. Declarations Conflict of interests: The authors state no conflict of interests or competing interests. Authors : Corresponding Authors Sushil Kumar Singh Funding: Authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Acknowledgements: DK is grateful to Solid State Physics Laboratory, Defence Research and Development Organization, Delhi for providing the infrastructure and equipment for the experiments. 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(2023) A Potential Landslide Early Warning System Based on Threshold Velocity of 1 mm/min. In: 2023 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE) . 8 November 2023 IEEE. pp. 1–6. doi:10.1109/RASSE60029.2023.10363496. Kumar, D., Mahapatro, A.K. & Singh, S.K. (2024a) Active waveguide deformation dynamics using acoustic emission technology for landslide early warning system. Bulletin of Engineering Geology and the Environment . 83 (2), 68. doi:10.1007/s10064-024-03548-6. Kumar, D., Mahapatro, A.K. & Singh, S.K. (2024b) Analyzing the effects of critical active waveguide parameters on acoustic emission characteristics for soil slope deformation monitoring. Bulletin of Engineering Geology and the Environment . Mao, W., Hei, L. & Yang, Y. (2021) Advances on the acoustic emission testing for monitoring of granular soils. Measurement . 185, 110110. doi:10.1016/j.measurement.2021.110110. McColl, S.T. (2022) Landslide causes and triggers. In: Landslide Hazards, Risks, and Disasters . Elsevier. pp. 13–41. doi:10.1016/B978-0-12-818464-6.00011-1. Pecoraro, G., Calvello, M. & Piciullo, L. (2019) Monitoring strategies for local landslide early warning systems. Landslides . 16 (2), 213–231. doi:10.1007/s10346-018-1068-z. Segoni, S., Piciullo, L. & Gariano, S.L. (2018a) A review of the recent literature on rainfall thresholds for landslide occurrence. Landslides . 15 (8), 1483–1501. doi:10.1007/s10346-018-0966-4. Segoni, S., Piciullo, L. & Gariano, S.L. (2018b) Preface: Landslide early warning systems: monitoring systems, rainfall thresholds, warning models, performance evaluation and risk perception. Natural Hazards and Earth System Sciences . 18 (12), 3179–3186. doi:10.5194/nhess-18-3179-2018. Shiotani, T. & Masayasu Ohtsu (1999) Prediction of slope failure based on AE activity. ASTM Special Technical Publication . 1353, 156–174. Smith, A. & Dixon, N. (2015) Quantification of landslide velocity from active waveguide–generated acoustic emission. Canadian Geotechnical Journal . 52 (4), 413–425. doi:10.1139/cgj-2014-0226. Smith, A., Dixon, N. & Fowmes, G.J. (2017) Early detection of first-time slope failures using acoustic emission measurements: large-scale physical modelling. Géotechnique . 67 (2), 138–152. doi:10.1680/jgeot.15.P.200. Smith, A., Dixon, N., Meldrum, P. & Haslam, E. (2014a) Inclinometer casings retrofitted with acoustic real-time monitoring systems. Ground Engineering . 16, 07. Smith, A., Dixon, N., Meldrum, P., Haslam, E. & Chambers, J. (2014b) Acoustic emission monitoring of a soil slope: Comparisons with continuous deformation measurements. Géotechnique Letters . 4 (4), 255–261. doi:10.1680/geolett.14.00053. Thirugnanam, H., Uhlemann, S., Reghunadh, R., Ramesh, M.V. & Rangan, V.P. (2022) Review of Landslide Monitoring Techniques With IoT Integration Opportunities. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing . 15, 5317–5338. doi:10.1109/JSTARS.2022.3183684. Vashista, S., Kumar, D., Kaur, R., Agrawal, A. & Kumar Singh, S. (2024) IoT-Based Landslide Detection System. In: Interdisciplinary Research in Technology and Management . London, CRC Press. pp. 491–497. doi:10.1201/9781003430469-57. Zaki, A., Chai, H.K., Razak, H.A. & Shiotani, T. (2014) Monitoring and evaluating the stability of soil slopes: A review on various available methods and feasibility of acoustic emission technique. Comptes Rendus. Géoscience . 346 (9–10), 223–232. doi:10.1016/j.crte.2014.01.003. Zhang, K., Wang, L. & Meng, G. (2024) Monitoring warning criterion of acoustic emission active waveguide system based on loess deformation and failure. Scientific Reports . 14 (1), 11399. doi:10.1038/s41598-024-62030-1. Cite Share Download PDF Status: Published Journal Publication published 02 May, 2025 Read the published version in Natural Hazards → Version 1 posted Editorial decision: Major revisions 06 Apr, 2025 Reviewers agreed at journal 16 Aug, 2024 Reviewers invited by journal 11 Aug, 2024 Editor assigned by journal 10 Aug, 2024 First submitted to journal 10 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4891330","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338744943,"identity":"2bc0eba0-7322-422e-b7a6-ee0f800798b0","order_by":0,"name":"Deepak Kumar","email":"","orcid":"","institution":"University of Delhi Department of Physics and Astrophysics","correspondingAuthor":false,"prefix":"","firstName":"Deepak","middleName":"","lastName":"Kumar","suffix":""},{"id":338744944,"identity":"54197d66-eb37-4054-b856-6d01950de66e","order_by":1,"name":"Ajit K. Mahapatro","email":"","orcid":"","institution":"University of Delhi Department of Physics and Astrophysics","correspondingAuthor":false,"prefix":"","firstName":"Ajit","middleName":"K.","lastName":"Mahapatro","suffix":""},{"id":338744945,"identity":"47a6aa29-922f-4994-ae5a-4090eb7b0f6a","order_by":2,"name":"Sushil Kumar Singh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie2RsQrCMBCGrwTqUuiaSV8hUNDX6eEqLi4OtWRKl0JnQXwMcYwI7VLfoS7OdXTzUungktbNIR+X44Z88OcC4HD8IwGduCui2ZruyR+UuB6rQK+gGpFrlmvW3s+7NYTVo8Hjbio0U41NETfpc6yrDfDVQuCpioT2MmFVQvApT4mSB+SeStxLT3FrsAJY2ykUjONhhAI3CRxVghLiOUeZYAEDiqgvihSNyrwlLnUUsqFg+fL6fKkUC7OxNkmn/iR72IN9Pu5KSzBFAzD7/Z7UNL8fHA6Hw/HFGw0GSlFbe5i0AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1418-3218","institution":"SSPL: DRDO Solid State Physics Laboratory","correspondingAuthor":true,"prefix":"","firstName":"Sushil","middleName":"Kumar","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2024-08-10 10:38:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4891330/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4891330/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11069-025-07316-x","type":"published","date":"2025-05-02T15:57:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64068149,"identity":"20652254-81fd-4235-8ca3-ab667b3e7d9a","added_by":"auto","created_at":"2024-09-06 05:54:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91112,"visible":true,"origin":"","legend":"\u003cp\u003eCritical velocity classes for LEWS working on slope deformation assessments.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4891330/v1/adb01fdc22dd6bf5b64eef4c.jpg"},{"id":64068152,"identity":"3f27acbd-3d96-4c1b-9f2d-a0ab7f70bffe","added_by":"auto","created_at":"2024-09-06 05:54:07","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":96539,"visible":true,"origin":"","legend":"\u003cp\u003eAE technology-based monitoring of a landslide prone soil slope.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4891330/v1/f7cc45545817dafb6d7bee79.jpg"},{"id":64068507,"identity":"1f595eda-edbb-499b-a019-84907ef15374","added_by":"auto","created_at":"2024-09-06 06:02:06","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":79414,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Indigenous AE sensor, \u003cstrong\u003e(b)\u003c/strong\u003e Sensor Highway-III system, \u003cstrong\u003e(c)\u003c/strong\u003eGravel backfill material.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4891330/v1/da5d3fc2c4c9ac14c8dfae22.jpg"},{"id":64068492,"identity":"7ca59302-e77e-4cb9-9cfc-6632cf4398ed","added_by":"auto","created_at":"2024-09-06 06:01:42","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":93094,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eExperimental set-up for testing of AWS on UTM, (b) Photograph of experimental set-up.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4891330/v1/4dd0721d33cc7db181d0d263.jpg"},{"id":64068490,"identity":"f06792a0-6347-43e9-9885-c213c324038a","added_by":"auto","created_at":"2024-09-06 06:01:41","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":90640,"visible":true,"origin":"","legend":"\u003cp\u003eAE signal duration (µs) vs. time (hh:mm:ss) for AWS testing at deformation rates corresponding to landslide velocity scale; \u003cstrong\u003e(a) \u003c/strong\u003eI\u003cstrong\u003e, (b) \u003c/strong\u003eII\u003cstrong\u003e, (c) \u003c/strong\u003eIII\u003cstrong\u003e, (d) \u003c/strong\u003eIV\u003cstrong\u003e, (e) \u003c/strong\u003eV\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4891330/v1/f63023d72d64206380d63228.jpg"},{"id":64068139,"identity":"c5b02b3c-e271-4a00-b7d9-263e965d2851","added_by":"auto","created_at":"2024-09-06 05:53:41","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":70767,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative AE counts vs. time (mins) for 200 mm AWS testing at deformation rates corresponding to landslide velocity scale; \u003cstrong\u003e(a) \u003c/strong\u003eI\u003cstrong\u003e, (b) \u003c/strong\u003eII\u003cstrong\u003e, (c) \u003c/strong\u003eIII\u003cstrong\u003e, (d) \u003c/strong\u003eIV\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4891330/v1/a121c321aeb67804eab4ef43.jpg"},{"id":64068147,"identity":"c5f9d19e-a717-4afd-9223-9f154ea543e5","added_by":"auto","created_at":"2024-09-06 05:54:06","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":56882,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative AE counts vs. time (mins) for 160 mm AWS testing at deformation rates corresponding to landslide velocity scale; \u003cstrong\u003e(a) \u003c/strong\u003eI\u003cstrong\u003e, (b) \u003c/strong\u003eII\u003cstrong\u003e, (c) \u003c/strong\u003eIII\u003cstrong\u003e, (d) \u003c/strong\u003eIV\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4891330/v1/e527f7f9ecae1fc0b85f2b8d.jpg"},{"id":64068491,"identity":"7dbef9f0-c0df-450f-91b7-4aaebc7103ab","added_by":"auto","created_at":"2024-09-06 06:01:42","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":81746,"visible":true,"origin":"","legend":"\u003cp\u003eASL (dB) vs. Amplitude (dB) for AWS testing at deformation rates corresponding to landslide velocity scale; \u003cstrong\u003e(a) \u003c/strong\u003eI\u003cstrong\u003e, (b) \u003c/strong\u003eII\u003cstrong\u003e, (c) \u003c/strong\u003eIII\u003cstrong\u003e, (d) \u003c/strong\u003eIV\u003cstrong\u003e, (e) \u003c/strong\u003eV.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4891330/v1/121a3a3c7ab1280c1a6f0bcf.jpg"},{"id":64068502,"identity":"1cdd2c91-c8a0-4321-93a2-5b4e6ba91fa6","added_by":"auto","created_at":"2024-09-06 06:01:56","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":78426,"visible":true,"origin":"","legend":"\u003cp\u003eAE signal strength (pV-s) vs. amplitude (dB) for AWS testing at deformation rates corresponding to landslide velocity scale; \u003cstrong\u003e(a) \u003c/strong\u003eI\u003cstrong\u003e, (b) \u003c/strong\u003eII\u003cstrong\u003e, (c) \u003c/strong\u003eIII\u003cstrong\u003e, (d) \u003c/strong\u003eIV\u003cstrong\u003e, (e) \u003c/strong\u003eV.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4891330/v1/8271efb0a58b25ead3353483.jpg"},{"id":64068494,"identity":"6106860b-fa21-4bb9-ba26-8e6384d3cdd4","added_by":"auto","created_at":"2024-09-06 06:01:46","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":90669,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative AE counts vs. time (mins) indicating the changes in landslide velocity scales with focus at: \u003cstrong\u003e(a)\u003c/strong\u003e 1441 mins, \u003cstrong\u003e(b)\u003c/strong\u003e 1621 mins, \u003cstrong\u003e(c) \u003c/strong\u003e1641 mins, \u003cstrong\u003e(d)\u003c/strong\u003e 1643 mins.\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4891330/v1/cd33495c3ee3dd5ceccff58c.jpg"},{"id":64068148,"identity":"f266fd18-de7d-407f-aff7-26d7722462c6","added_by":"auto","created_at":"2024-09-06 05:54:06","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":44249,"visible":true,"origin":"","legend":"\u003cp\u003eAE signal duration (µs) vs. time (hh:mm) for day 02 of testing AWS.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4891330/v1/618505e4cd8805cd0eac924f.jpg"},{"id":64068151,"identity":"5d45d836-6744-49f6-9eef-b8fab5e88737","added_by":"auto","created_at":"2024-09-06 05:54:07","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":68197,"visible":true,"origin":"","legend":"\u003cp\u003eConceptualization of a generalized LEWS based on Varnes’ landslide velocity scales.\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4891330/v1/b7a3003a598a563699761ae2.jpg"},{"id":81988007,"identity":"c5195a93-451a-43f9-a598-3f1369daa370","added_by":"auto","created_at":"2025-05-05 16:07:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1995082,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4891330/v1/4a18ffa3-a3ff-4067-a96d-d557723a4da7.pdf"}],"financialInterests":"","formattedTitle":"Assessing landslide velocity scales with acoustic emission active waveguides for early warning system","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLandslides are the natural disasters that refers to release of a huge mass of a slope downwards under the driving gravitational force(Bobrowsky \u0026amp; Highland, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Landslide disasters cause significant loss of life, destruction of infrastructure, disruption of transportation, economic losses, and environmental damages(Dai, Lee \u0026amp; Ngai, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Unified global landslide database indicates the concerning number of landslide disasters throughout the globe(Gómez, García \u0026amp; Aristizábal, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Bulk failure of a slope is influenced by a huge number of factors including rainfall, slope angle, nature of the rock underneath the slope, geographical conditions, human interventions etc.(McColl, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the view of minimization of landslide risks and losses, developing a robust and reliable landslide early warning system (LEWS) is the need of the hour. The primary function of a LEWS is to monitor a landslide prone slope for its deformation dynamics and generate alerts/ warnings when the risk of slope failure becomes critical(Segoni, Piciullo \u0026amp; Gariano, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e). Since rainfall is the prime triggering factor for landslides, majority of LEWS are based on direct or indirect measurement of water content in the soil or rainfall threshold models(Segoni, Piciullo \u0026amp; Gariano, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e). The major shortcoming of LEWS based solely on rainfall thresholds is that it does not provide real-time state of the slope in terms of slope deformation parameters(Guzzetti et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Another disadvantage is that the rainfall threshold models are not universal but varies with site conditions. Additionally, factors like rainfall and water content are not instantaneously reflected in the slope instability. Hence, real-time monitoring of slope is necessary for assessing the instability of slopes and offers immense potential for designing such LEWS(Pecoraro, Calvello \u0026amp; Piciullo, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Geotechnical monitoring of slopes has become mainstream method to assess the physical parameters of slope deformation dynamics(Thirugnanam et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Displacement, velocity, and acceleration of slope deformation can be monitored using various geotechnical instruments like inclinometers, extensometers, time-domain reflectometry, accelerometers, etc. Based on the data of slope deformation dynamics, prediction of time of slope failure is calculated. However, there is one major problem that one encounters while designing a LEWS that there is no particular constant value of slope deformation rate at which the slope collapses. It differs from slope to slope depending on numerous site and geological factors as stated above. Hence, developing a standard LEWS corresponding to deformation rates of the slope has been a tedious task. The Varnes classification system, developed by David J. Varnes in 1978, is the most widely used method for categorizing landslides (Hungr, Leroueil \u0026amp; Picarelli, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). It deals with landslide classifications based on type of materials and slope movements. It also encompasses different classes of landslide velocities along with their probable destructive significance. The landslide velocity scales consisted of 07 velocity classes ranging from extremely slow to extremely rapid rates of slope deformations along with their probable destructive significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Varnes’ classification of landslide velocity scales has been widely accepted throughout the scientific community for landslides. A LEWS oriented around Varnes’ classification of landslide velocity scales can be considered as a framework for a standard and more generalised LEWS. In this study, a potential soil slope deformation monitoring system based on Varnes’ landslide velocity scales has been discussed. Velocity class of slow, moderate, and rapid is of interest for a LEWS working on slope deformation assessments. A slope monitoring system that can detect and identify deformation rates corresponding to each velocity class can be instrumentalized to develop a standard LEWS working on Varnes’ landslide velocity scale.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmongst all the geotechnical methods to assess slope deformation dynamics, acoustic emission (AE) technology has emerged as a state-of-the-art for monitoring landslide prone slopes (Zaki et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mao, Hei \u0026amp; Yang, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is a non-destructive testing method used for structural health monitoring of various structures and materials(Gholizadeh, Leman \u0026amp; Baharudin, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The principle of AE technology is to detect and capture ultrasonic mechanical waves generated by virtue of material deformations and process them for material evaluations(Anon, 2008). AE technology-based geotechnical monitoring of soil slopes has become quite mainstream in the past two decades(Mao, Hei \u0026amp; Yang, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). AE behaviour of a soil slope has been instrumentalized to assess the instability extent of the slope in variety of research articles(Zaki et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eActive waveguide system (AWS) is the key part of the entire instrumentation of AE technology-based soil slope monitoring(Dixon, Hill \u0026amp; Kavanagh, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). AWS deals with the problem of high attenuation coefficients of soil media for AE signals(Shiotani \u0026amp; Masayasu Ohtsu, 1999). Soil slope shear deformations deform the AWS and cause interactions among backfill material particles i.e., generally rough angular gravel particles. These interactions generate AE signals which are captured by AE sensors placed on the central metal waveguide. AE sensors transfer the signals to AE data acquisition and analysis system (DAAS) for signal conditioning and AE signal parameters extraction. This way, slope shear deformations are captured in terms of AE signals using AWS. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e represents the methodology of monitoring of a soil slope using AE technology. Slope deformation dynamics (displacement, velocity, and acceleration) has been both qualitatively and quantitatively correlated with AE signal parameters of AE signals arising from AWS(Kumar, Mahapatro \u0026amp; Singh, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Zhang, Wang \u0026amp; Meng, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Deng et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021c\u003c/span\u003e; Dixon et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Smith, Dixon \u0026amp; Fowmes, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). At the time of invention of the concept of AWS, rate of ground deformation i.e., velocity was found to be related to the generated AE signals on an unstable soil slope(Dixon, Hill \u0026amp; Kavanagh, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Generally, greater the extent of AE signals arising from the slope, higher is the instability extent of the slope and vice-versa.\u003c/p\u003e \u003cp\u003eLaboratory testing of AWS on compression or shear testing machines and shear testing fixtures has been reported for correlation between AE characteristics and deformation parameters(Smith \u0026amp; Dixon, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Deng et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dixon \u0026amp; Spriggs, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Quantification of AE signal parameters with respect to slope deformation dynamics is also reported in these articles. Mathematical empirical relationships have been published displacement, velocity and various AE signal parameters like counts, energy, and amplitude etc. Deployment of active waveguides on real-time landslide prone slopes has been performed and the relationship between trends of rainfall, slope displacement parameters, and AE signal parameters are very promising (Berg et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dixon et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e; Smith et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014b\u003c/span\u003e). In our recent studies, a potential LEWS at a threshold deformation rate of 1 mm/min(Kumar, Mahapatro \u0026amp; Singh, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e) and IoT based landslide detection systems(Vashista et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) has been discussed. In this study, the focus is more concentrated on a standard LEWS at Varnes’ landslide velocity scales. The primary step to evaluate slope deformation parameters from AE technology is to calibrate the AE behaviour of the AWS with respect to its deformation dynamics. Then this behaviour is expected to replicate up to some extent on real-time landslide prone slopes.\u003c/p\u003e"},{"header":"Experimental methodology","content":"\u003cp\u003eLaboratory testing of AWS to analyse the AE behaviour in response to various deformation dynamics has been quite efficient and reliable in monitoring a real-time landslide prone soil slope. It prominently involves confined laboratory compression testing (Kumar, Mahapatro \u0026amp; Singh, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Dixon \u0026amp; Spriggs, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Smith \u0026amp; Dixon, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang, Wang \u0026amp; Meng, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Deng et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e) and shear testing of AWS(Dixon et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Deng et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Smith, Dixon \u0026amp; Fowmes, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Deng et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021c\u003c/span\u003e). However, it is noteworthy that the stress states of AWS in laboratory confined compression testing and real-time field testing are not identical. The main objective of compression testing of AWS is to study the interactions among backfill material i.e., gravel particles in this study at different deformation dynamics. The gravel interactions are in stimulus to the alternating deformation rates and not the static loads. The first step of monitoring soil slope deformations using AWS is to calibrate the AE behavior of the AWS with respect to different velocities. This is done by the compression testing, 03-point bend testing, or shear testing of AWS in the laboratory. The results obtained in the laboratory deformation testing are very useful as they provide critical insights about the trends of AE signal parameters with actual slope deformation parameters like displacement, velocity, and acceleration.\u003c/p\u003e\u003cp\u003eIn this study, a universal testing machine has been used to calibrate the AE behaviour of AWS with respect to different landslide velocity scales. For the calibration, AWS is subjected to various deformation processes under a confined and controlled laboratory compressive test. Simultaneous data from deformation dynamics of AWS is collected on the UTM and corresponding AE data is recorded on AE data acquisition and analysis system (DAAS). Based on the analysis of AE signal parameters, each landslide velocity scale is characterized. The aim of the study is to conceptualize a potential LEWS based on multi-AE parameter assessment of landslide velocity scale and slope instability state.\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eMaterials\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eBased on the optimized active waveguide components in our previous studies(Kumar, Mahapatro \u0026amp; Singh, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e), indigenous broadband AE sensor placed on the top of an aluminium central waveguide is used. The central waveguide has 2000 mm length, 50 mm outer diameters, and 4–5 mm wall thickness. Indigenous AE sensor (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003e(a)\u003c/b\u003e) has a wideband frequency response in 20–80 kHz frequency domain. Sensor Highway-III system made by Physical Acoustics Corporation; USA (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003e(b)\u003c/b\u003e) is used as AE data acquisition and analysis system (DAAS). Based on the findings of (Kumar, Mahapatro \u0026amp; Singh, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e), backfill material and outer flexible membrane is selected. Rough angular gravel particles with average size 10 mm (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003e(c)\u003c/b\u003e) are used as backfill material. Double wall corrugated high density polyethylene pipe with 1500 mm length and 160/ 200 mm outer diameter is the outer flexible membranes used to encapsulate the central waveguide and gravels. Universal testing machine (UTM) with 100 kN capacity is used for compression testing on AWS.\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eExperimental set-up and procedure\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eFive scales of landslide velocity I-V (slow, intermediate, moderate, critical intermediate, and rapid respectively) are taken as input deformation rates for AWS at UTM. Deformation rates as per Varnes’ landslide velocity scale are assigned to each scale (0.0035, 0.03, 0.3, 3.0, and 30.0 mm/min respectively). Keeping the limitations of UTM into consideration, slowest rate of deformation is kept at 0.0035 mm/min instead of 0.003 mm/min. Total deformations produced in the AWS are approximately 20–30 mm and test duration varied accordingly. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the landslide velocity scales with their description and deformation rates for testing of AWS on UTM.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLandslide velocity scales and corresponding deformation rates for testing of AWS on UTM.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLandslide velocity scale\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeformation rate (mm/min)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTest duration (mins)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSlope instability state\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSlow (I)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0035\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5550\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStable slope\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntermediate (II)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e720\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIntermediate state\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eModerate (III)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnstable slope\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCritical intermediate (IV)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCritically unstable\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRapid (V)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDanger\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eAWS is placed horizontally on the UTM as represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Load is applied at centre point of outer flexible membrane. Constant deformation rates are applied using UTM and corresponding generated AE activities in the backfill material of AWS is captured and collected for post-analysis using AE DAAS unit. The prime focus of the study is to analyse the interaction of gravel particles in terms of AE signal parameters with respect to different deformation rates or landslide velocity scales. Following that, establishing a method to distinguish and identify each landslide velocity scale based on AE parametric analysis alone. The AE data was acquired at a threshold amplitude value of 40 dB. For data processing and analysis, the AE data was filtered for AE signals higher than or equal to 50 dB to eliminate low strength and unwanted AE signals.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe first AE signal parameter analysed was signal duration. AE signal duration is the total time elapsed between the first threshold crossing and last threshold crossing of AE signal. AE signal duration of AE signals captured was plotted against time for each scale (I-V) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003e(a)\u003c/b\u003e has 04 individual graphs: one for each day of test at 0.0035 mm/min. A clear distinction was observed for landslide velocity scale V (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003e(e)\u003c/b\u003e) that duration of AE signals was saturated to 1,000,000 \u0026micro;sec (1000 ms). This is the maximum duration for capture of AE signals, kept in AE DAAS for signal acquisition. Additionally, there was substantial increment in the signal durations of AE signals with increasing deformation rate. Also, along the time axis, density of AE signals increased with increasing rate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAE counts represent the number of times AE signal waveform crosses the predefined threshold amplitude value. AE counts per minute was calculated throughout the test duration and then cumulative AE counts were plotted against time (mins). Polynomial curves with excellent R\u003csup\u003e2\u003c/sup\u003e values of 0.98\u0026ndash;0.99, 95% confidence band, and 95% prediction band were fitted on the graphs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). For each polynomial fit, the intercept was kept zero, since at time t\u0026thinsp;=\u0026thinsp;0, there are no AE counts. The equation for best fit polynomial curve with zero intercept was:\u003c/p\u003e \u003cp\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{y}=\\varvec{\\beta\\:}{\\varvec{x}}^{2}+\\varvec{\\alpha\\:}\\varvec{x}\\:$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the polynomial curve equation, coefficient of x\u003csup\u003e2\u003c/sup\u003e (denoted by β) was found to be correlated with the actual deformation rate on AWS. The velocity or deformation rate of AWS was directly proportional to sqrt (β).\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{D}\\varvec{e}\\varvec{f}\\varvec{o}\\varvec{r}\\varvec{m}\\varvec{a}\\varvec{t}\\varvec{i}\\varvec{o}\\varvec{n}\\:\\varvec{r}\\varvec{a}\\varvec{t}\\varvec{e}\\propto\\:\\surd\\:\\varvec{\\beta\\:}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{D}\\varvec{e}\\varvec{f}\\varvec{o}\\varvec{r}\\varvec{m}\\varvec{a}\\varvec{t}\\varvec{i}\\varvec{o}\\varvec{n}\\:\\varvec{r}\\varvec{a}\\varvec{t}\\varvec{e}=\\varvec{c}\\:\\surd\\:\\varvec{\\beta\\:}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe constant of proportionality (c) can be found by preliminary calibration of AWS prior to deployment on landslide prone field site. In case of 200 mm AWS discussed here, the constant of proportionality was found to be \u003cb\u003e0.05\u003c/b\u003e empirically. Deformation rate was calculated using this proportionality relationship and compared with actual rate of deformation. Values of actual and calculated deformation rates were quite close as indicated by Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eActual and AE calculated deformation rate of 200 mm AWS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActual deformation rate (mm/min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient of x\u003csup\u003e2\u003c/sup\u003e (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\surd\\:\\varvec{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ec\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAE calculated deformation rate (mm/min) (=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{c}\\:\\surd\\:\\varvec{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.0035 (I)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.00354\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.03 (II)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0318\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.3 (III)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.398\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.0 (IV)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6126.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3.914\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo check the validity of this proportionality relationship between deformation rate and β, another AWS was studied. A new 160 mm AWS with same backfill material and central waveguide assembly was subjected to same deformation rates under UTM. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e contains the graphs for cumulative AE counts vs. time at different landslide velocity scales along with their best fit polynomial curves. Best fit polynomial curves had excellent R\u003csup\u003e2\u003c/sup\u003e values of 0.98\u0026ndash;0.99.\u003c/p\u003e \u003cp\u003eIn the case of 160 mm AWS, the constant of proportionality with was empirically found to be approximately 0.04. Values of actual and AE calculated deformation rates in case of 160 mm AWS are stated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eActual and AE calculated deformation rate of 160 mm AWS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActual deformation rate (mm/min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient of x\u003csup\u003e2\u003c/sup\u003e (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\surd\\:\\varvec{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ec\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAE calculated deformation rate (mm/min) (=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{c}\\:\\surd\\:\\varvec{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.0035 (I)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.00262\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.03 (II)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0323\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.3 (III)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.294\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.0 (IV)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10233.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e101.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAcoustic Signal Level (ASL) is a statistical measure calculated as the average of the amplitude of AE signals. ASL considers the AE amplitude on a logarithmic scale, it is typically reported in decibels (dB). ASL values for each AE signal at different landslide velocity scales (I-V) are plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The base line for ASL for landslide velocity scales I, II, and III was below 30, whereas for landslide velocity scales IV and V. it was over 35 and 40 respectively.\u003c/p\u003e\u003cp\u003eWhile plotting ASL values, it was observed that total number of AE signals are increasing from landslide velocity scale I to IV. It was interesting that landslide velocity scale V has least number of AE signals while having the maximum duration of captured AE activities as discussed earlier. It was because approximately each AE signal at landslide velocity scale V was having maximum duration of 1000 ms. Now the focus is on landslide velocity scales I-IV. To normalize the comparison, AE signals per minute was calculated by dividing total number of AE signals by total test duration in minutes. The value of AE signals per minute for landslide velocity scales I, II, III, and IV was calculated to be 0.18, 2.08, 30.94, and 645.29 respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOrder of number of AE signals per minute for different landslide velocity scale. This observation was verified with 160 mm AWS. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e comprises of the results regarding Number of AE signals captured in case of 160 mm AWS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLandslide velocity scale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAE signals per minute\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScientific notation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrder of number of AE signals per minute\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.8E-1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.08E0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.09E\u0026thinsp;+\u0026thinsp;1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e645.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6.45E\u0026thinsp;+\u0026thinsp;2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of AE signals per minute for different landslide velocity scale in case of 160 mm AWS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLandslide velocity scale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal AE signals\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTest duration (mins)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAE signals per minute\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOrder of number of AE signals per minute\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e383.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmplitude is the maximum peak voltage level reached by AE signal waveform for a burst AE signal. It is generally recorded in units of decibel (dB) which is the logarithmic scale of voltage value with respect to reference voltage of 1 \u0026micro;V. The parameter of AE signal strength represents the value of time integral of the absolute signal voltage. Its unit is pV-s in Sensor Highway-III system. AE signal strength of AE signals was plotted against amplitude for each landslide velocity scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). A quadratic relationship was found between the two AE signal parameters represented by the best fit curves of the graphs. For the best fit curve equation, the values of intercept, coefficient of x and x\u003csup\u003e2\u003c/sup\u003e increased with increasing deformation rate.\u003c/p\u003e \u003cp\u003eFollowing the individual testing of AWS at different constant deformation rates, single test consisting of all the velocity scales in sequential order was conducted on the AWS and results were reverified. This test also helped to check the sensitivity of this approach for real-time accelerating slope movements. The 200 mm AWS was subjected to successive deformation rates of 0.0035, 0.03, 0.3, 3.0 and 30.0 mm/min in continuation one after the other. The description of test deformation rates and their respective time durations are provided in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. However, the AE DAAS was paused to unload the AWS from UTM and test 30.0 mm/min from thereon. AE DAAS resumed with UTM deforming AWS at 30.0 mm/ min. So, in terms of AE data, the rates were consecutively one after the other.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLandslide velocity scales and corresponding deformation rates for testing of AWS in context of accelerating slope deformations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLandslide velocity scale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeformation rate (mm/min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTest duration (mins)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDeformation produced (mm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlow (I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate (II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate (III)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCritical intermediate (IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRapid (V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFirst observation was that abrupt changes in AE signal parameters were expected at 1441, 1621, 1641, and 1646 mins of test respectively indicating the change of landside velocity scale. Plot of cumulative AE counts vs. time (mins) indicated these prominent changes as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAs stated at the start of results section, AE signal duration was used to detect the landslide velocity scale V with saturation of AE signals at 1000 ms (10\u003csup\u003e6\u003c/sup\u003e \u0026micro;s) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eOrder of AE signals per minute for each landslide velocity scale was also validated in the analysis of accelerating deformations of AWS. Each velocity scale was distinguishable and identifiable based on the order of AE signals per minute (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Proportionality relationship between deformation rate and sqrt (β) was also verified for each scale separately.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLandslide velocity scales and corresponding deformation rates for testing of AWS in context of accelerating slope deformations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLandslide velocity scale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeformation rate (mm/min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTest duration (mins)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal no. of AE signals captured\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOrder of AE signals per minute\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlow (I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate (II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate (III)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCritical intermediate (IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study explores the potential of utilizing acoustic emission (AE) data from soil slopes to design a generalized landslide early warning system based on Varnes' landslide velocity scales. Laboratory testing of active waveguide system (AWS) has proven efficient and reliable for analysing the AE behaviour in response to various deformation dynamics in landslide-prone soil slopes. Key methods include confined compression and shear testing of AWS, as detailed in studies by (Deng et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021c\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Kumar, Mahapatro \u0026amp; Singh, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Zhang, Wang \u0026amp; Meng, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Smith \u0026amp; Dixon, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Smith, Dixon \u0026amp; Fowmes, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dixon \u0026amp; Spriggs, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Dixon et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, the deformation dynamics subjected to AWS varied with each study, which created an unavailability of a standard AE calibration of AWS. In present study, AWS is subjected to deformation rates as per Varnes\u0026rsquo; landslide velocity scales and their AE behaviour is calibrated. AE signal parameters of duration, counts, order of AE signals, amplitude, signal strength etc. have been qualitatively and quantitively analysed to be correlated with particular landslide velocity scale. The results and observations were validated and reverified on a different set of AWS. The proposed LEWS can predict a particular landslide velocity scale and its associated slope instability state based on the analysis of AE behaviour of AWS installed on the slope. Figure\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e represents the prime objective of the study i.e., conceptualization of a generalised LEWS generating warnings at different scales of landslide velocity.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study addresses the AE characteristics of AWS at different landslide velocity scales. AE signal parameters of AWS have been analysed at deformation rates corresponding to five landslide velocity scales. Different stages of slope instabilities descripted on the landslide velocity scales are identified and evaluated based on AE signals recorded on the AE data acquisition and analysis system. Landslide velocity scale V can be identified by analysis of AE signal duration. Saturation of AE signal duration at 1000 ms indicates the landslide velocity scale V. Landslide velocity scale I, II, III, and IV can be identified by two criteria: one is the proportionality relationship between the deformation rate and coefficient (β) of polynomial fit of cumulative AE counts, and another is the order of the number of captured AE signals per unit time. Accelerating slope deformations or change of landslide velocity scales is quite evident by the abrupt change in AE signal parameters. However, it should be noted that deformations produced in the AWS are under controlled and confined laboratory set-up which might vary up to some extent on a real-time landslide prone slope. Laboratory studies on soil shear testing fixture are under progress to simulate a more realistic approach to the testing of AWS and calibration of its AE behavior. Preliminary results are positive and will be shared in our upcoming publications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interests:\u003c/h2\u003e \u003cp\u003eThe authors state no conflict of interests or competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003e \u003cb\u003eAuthors\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eCorresponding Authors\u003c/strong\u003e \u003cp\u003eSushil Kumar Singh\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eAuthors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eDK is grateful to Solid State Physics Laboratory, Defence Research and Development Organization, Delhi for providing the infrastructure and equipment for the experiments. DK would also like to express gratitude to Department of Physics and Astrophysics, University of Delhi, Delhi for allowing Ph.D. degree. DK is also thankful to Council of Scientific and Industrial Research (CSIR), India for providing Senior Research Fellowship for Ph.D.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBerg, N., Smith, A., Russell, S., Dixon, N., Proudfoot, D. \u0026amp; Take, W.A. (2018) Correlation of acoustic emissions with patterns of movement in an extremely slow-moving landslide at Peace River, Alberta, Canada. \u003cem\u003eCanadian Geotechnical Journal\u003c/em\u003e. 55 (10), 1475\u0026ndash;1488. doi:10.1139/cgj-2016-0668.\u003c/li\u003e\n\u003cli\u003eBobrowsky, P. \u0026amp; Highland, L. (2013) The Landslide Handbook-a Guide to Understanding Landslides: A Landmark Publication for Landslide Education and Preparedness. In: \u003cem\u003eLandslides: Global Risk Preparedness\u003c/em\u003e. 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(2022) Review of Landslide Monitoring Techniques With IoT Integration Opportunities. \u003cem\u003eIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\u003c/em\u003e. 15, 5317\u0026ndash;5338. doi:10.1109/JSTARS.2022.3183684.\u003c/li\u003e\n\u003cli\u003eVashista, S., Kumar, D., Kaur, R., Agrawal, A. \u0026amp; Kumar Singh, S. (2024) IoT-Based Landslide Detection System. In: \u003cem\u003eInterdisciplinary Research in Technology and Management\u003c/em\u003e. London, CRC Press. pp. 491\u0026ndash;497. doi:10.1201/9781003430469-57.\u003c/li\u003e\n\u003cli\u003eZaki, A., Chai, H.K., Razak, H.A. \u0026amp; Shiotani, T. (2014) Monitoring and evaluating the stability of soil slopes: A review on various available methods and feasibility of acoustic emission technique. \u003cem\u003eComptes Rendus. G\u0026eacute;oscience\u003c/em\u003e. 346 (9\u0026ndash;10), 223\u0026ndash;232. doi:10.1016/j.crte.2014.01.003.\u003c/li\u003e\n\u003cli\u003eZhang, K., Wang, L. \u0026amp; Meng, G. (2024) Monitoring warning criterion of acoustic emission active waveguide system based on loess deformation and failure. \u003cem\u003eScientific Reports\u003c/em\u003e. 14 (1), 11399. doi:10.1038/s41598-024-62030-1.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Geotechnical engineering, landslides, deformation, acoustic emission (AE), active waveguide system (AWS), landslide velocity scales, soil slope instability","lastPublishedDoi":"10.21203/rs.3.rs-4891330/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4891330/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study proposes a strategy to predict the different landslide velocity scales of susceptible slopes by analysing the acoustic emission (AE) behaviour of the active waveguide system (AWS). Laboratory compression tests were conducted on models of AWS utilizing a universal testing machine to induce strain-induced interactions within the backfill material, resulting in the generation of AE signals. AE characteristics of AWS has been analysed at deformation rates ranging from slow (0.003 mm/min) to rapid (30.0 mm/min) rates of Varnes\u0026rsquo; landslide velocity scales. Two intermediate scales (0.03 and 3.0 mm/min) have been introduced between slow, moderate, and rapid rates of landslide velocity scales. AE characteristics, including signal duration, counts, acoustic signal level, amplitude, signal strength, and their derivatives were meticulously analysed for each velocity scale. A strong proportionality relationship was observed between cumulative AE counts and deformation rate of AWS. Quadratic correlation was established between AE signal strength and amplitude. AE activity of the AWS for different velocity scales were also analysed. Significant results observed and correlations were validated using another different set of AWS. Additionally, one test consisting all the velocity scales in sequential order was conducted on the AWS and results were reverified. This study can significantly contribute to developing real-time landslide early warning systems that issue alerts based on varying landslide velocities and slope instability stages, as reflected in the AE data of AWS.\u003c/p\u003e","manuscriptTitle":"Assessing landslide velocity scales with acoustic emission active waveguides for early warning system","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-06 05:53:37","doi":"10.21203/rs.3.rs-4891330/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2025-04-06T04:17:57+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-08-16T09:35:14+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-11T14:34:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-10T10:25:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Natural Hazards","date":"2024-08-10T05:55:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b1ac7eac-9c0c-462f-9ea4-a2028dbd2b86","owner":[],"postedDate":"September 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-05T16:04:09+00:00","versionOfRecord":{"articleIdentity":"rs-4891330","link":"https://doi.org/10.1007/s11069-025-07316-x","journal":{"identity":"natural-hazards","isVorOnly":false,"title":"Natural Hazards"},"publishedOn":"2025-05-02 15:57:43","publishedOnDateReadable":"May 2nd, 2025"},"versionCreatedAt":"2024-09-06 05:53:37","video":"","vorDoi":"10.1007/s11069-025-07316-x","vorDoiUrl":"https://doi.org/10.1007/s11069-025-07316-x","workflowStages":[]},"version":"v1","identity":"rs-4891330","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4891330","identity":"rs-4891330","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}