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Applicability of Geostatistical Method to Ground Investigation for Embankments of Narrow Detached Houses | SciTechnol
Geoinformatics & Geostatistics: An Overview.ISSN: 2327-4581
Special Issue 1
Research Article, Geoinfor Geostat An Overview Vol: 6 Issue: 1
Applicability of Geostatistical Method to Ground Investigation for Embankments of Narrow Detached Houses
Shinya Inazumi1*, Ken-Ichi Shishido2 and Kohei Urakami3
1Department of Civil Engineering, Shibaura Institute of Technology, Toyosu Koto, Tokyo, Japan
2Tomec Corporation, Shibadaimon, Minato, Tokyo, Japan
3Department of Social Informatics, Kyoto University, Gokasho, Uji, Kyoto,Japan
*Corresponding Author : Shinya Inazumi
Associate Professor, Department of Civil Engineering, Shibaura Institute of Technology, Toyosu Koto, Tokyo, Japan
E-mail: inazumi@shibaura-it.ac.jp
Received: October 27, 2017 Accepted: February 13, 2018 Published: February 21, 2018
Citation: Inazumi S, Shishido K, Urakami K (2018) Applicability of Geostatistical Method to Ground Investigation for Embankments of Narrow Detached Houses. Geoinfor Geostat: An Overview 6:1. doi: 10.4172/2327-4581.1000174
Various problems have often been observed recently on narrow detached house embankment grounds. In order to clarify the cause of various problems on narrow detached house ground, it is necessary to grasp the information in the ground in more detail. Currently, when making details of the ground, a method of interpolating between drilled stationary points by a linear method is main stream, and it is general that it is roughly grasped. In this study, we improved a Swedish Weight Sounding test (SWS test) to determine the physical properties of the subsoil underneath weak, narrow and heterogeneous detached house embankment grounds.
Moreover, we predict the cross-sectional distribution of the subsoil characteristics using the Kriging Geostatistical method. Originally Kriging was not used as a strength prediction method in narrow detached house embankment grounds. The results indicate that the Kriging method predicts the subsoil cross-sectional characteristics with high accuracy, and it can be applied to subsoil characterization of detached houses grounds.
Keywords: Detached houses ground; Geostatistics; Ground characteristic; Ground investigation; Improved Swedish weight sounding test; Kriging method
Ground subsidence and liquefaction have often been observed on detached house grounds. For example, during the great East Japan earthquake on March 11, 2011, large-scale ground liquefaction was observed widely in Urayasu City, Chiba Prefecture, Japan. These phenomena involved the permanent ground displacement on a large scale, damaging public infrastructures extensively. However, appropriate investigation methods and countermeasures are still under development and have not been established yet, partly because the suitable study areas are very confined [1].
In addition, in-situ subsurface investigations of the subsoil properties are performed only at the limited points due to temporal, economic, and physical constraints. Not limited to detached-house grounds, this is also the case with relatively large-scale constructions such as tunnels [2]. Therefore, with respect to unknown points or areas, it is common to predict the ground characteristics, including its physical properties, using some method based on a finite number of subsurface measurements. Nevertheless, accurate estimates of the grounds characteristics at specific areas and points are necessary for the establishment of a subsurface investigation and liquefaction countermeasures system [3].
In the ground survey, it is thought that it is essential to obtain a lot of investigation results in a limited place in order to detail the inside of the ground. Therefore, it is necessary to establish a testing machine capable of measuring results at finer intervals. Moreover, in the interpolation of the conventional ground survey results, a method of linearly interpolating between the drilled stationary points is main stream, and it is common that the information in the ground is roughly grasped. Therefore, even in interpolation of ground survey results, development and establishment of methods for predicting unknown points or unknown regions in the ground with high precision based on the ground survey results are considered to be indispensable tasks. It is best to establish measures to deal with ground problems after making the ground more detailed.
In this study, we conduct a detailed ground survey for the ground using an improved Swedish weight sounding test (Nippon screw weight system; hereinafter referred to as NSWS test (machine)) on a soft ground in a certain detached house embankment ground. Then, using the measured converted N value data as an example, using the Geostatistical method which was originally not used in the narrow detached house ground, the converted N value is not known unknown in the ground prediction of planar spatial distribution of points / unknown regions is carried out. The Geostatistical method is a method of giving a mathematically optimal weight by setting a predicted value at an unknown point in a space as a weighted average of known values. Although this method was used for estimating the strength of homogeneous ground, there are few cases used in heterogeneous and narrow ground like detached house embankment ground. Therefore, there is still unknown part in heterogeneous ground for the Geostatistical method. In this study, we will verify the effectiveness of the new ground survey tester and develop it as a new application case study of the Geostatistical method by applying the Geostatistical method to the heterogeneous ground.
Current State of Subsurface Investigations and Prediction of Cross-Sectional Distributions
Current state of subsurface investigations
Multiple methods exist for subsurface investigation. However, two of these are most commonly used: the standard penetration test (hereinafter, referred to as the SPT test) and the Swedish sounding test (hereinafter, referred to as SWS test). The SPT test is a test for determining ground hardness, tightness, and the geological structure; it is conducted during sampling, by penetrating the SPT sampler dynamically [4]. The SPT test is frequently performed during construction of lifelines such as roads, dams, and large-scale buildings. On the other hand, in the SWS test, the lot equipped with a screw point, whose edge is drill-shaped, is loaded with weight and penetrates the ground; we measure the rotation frequency required to penetrate up to 0.25 m of ground depth [4]. Based on the required weight load and the rotational frequency, we calculate the converted N-value. When detached houses are built, the SWS test is used to investigate the targeted ground. Both tests allow N-values and converted N-values to be measured (calculated) only in relatively wide intervals. For example, the SPT test measures N-values every 0.3 m, and the SWS test measures converted N-values at 0.25 m depth intervals. In other words, although it is possible to measure the ground physical property (the N-value distribution) using either method, with a certain level of accuracy, it is impossible to measure changes in the detailed N-value distribution in the ground. Moreover, the SWS test used for detachedhouse grounds is considered completed, when the examiner judges that drilling has reached the rigid layer [4]. Consequently, even if the rigid layer is only comprised of pebbles, the test may be finished. For these reasons, it is impossible to measure the detailed N-values and the converted N-values at present. Furthermore, due to temporal, economic, and physical reasons, in-situ soil investigation cannot be performed in finite locations. Therefore, it is difficult to grasp the physical property value of all the target ground only by the ground survey and it is a situation where the ground cannot be investigated in detail.
Current state of predictions of cross-sectional distribution
It is difficult to understand the physical properties of all the objects' ground areas only by subsurface investigation. Therefore, in most cases, the properties are predicted empirically, based on a finite number of measured values. In other words, experts evaluate the results of investigation, and the so-called "drawing" is carried out, or the values are predicted by applying to each sample the weight in inverse proportion to the distance or the square distance as the weighted average [5]. However, in the former case, the expert opinion affects the estimation, and it takes a huge amount of time. In the latter, certain problems remain, including ambiguity in the mathematical justification for each weighting. In these methods, as the reliability of the assessments cannot be quantified, the entire ground area cannot be understood. Thus, it is thought that it will be easier to grasp the detailed information in the ground by using a method that is relatively time-consuming and accurate.
Prediction of Cross-Sectional Distribution for Ground Characteristics by Geostatistics
Geostatistics is a discipline with a relatively short history. In the early 1960s, for the first time, Georges Matheron, a mathematician at Mines Paris Tech, defined "Geostatistics" as the prediction method of cross-sectional distribution and presented its concept. In this study, based on converted N-values obtained through the NSWS test, we predict the cross-sectional distribution using the Kriging Geostatistical method [6].
Overview of the kriging method
The Kriging method is the most fundamental technique in Geostatistics, commonly used in meteorology and forestry, which was proposed by D.G. Krige, an engineer in South Africa [6]. This method is also used in agriculture, climatology, fisheries sciences, and other fields. In civil engineering, this method is used in hydraulic engineering and geotechnical engineering [7]. The Kriging method allows the cross-sectional fluctuating distribution of a physical property to be predicted objectively [8]. There are several established variations of this method, such as the ordinary Kriging method, the simple Kriging method, and the indicator Kriging method [9].
In this study, we use the ordinary Kriging method, which includes weighting limitations, and allows for the relative representation of a space-random field.
Ordinary Kriging method
When the investigated physical property assumes different values based on the probability distribution, it is considered a random variable [10]. If the expected value is constant throughout the target area, and the covariance depends only on the distance between the data and is irrelevant of the data location, stationary is achieved [11]. Geostatistics is based on the assumption that these random variables exhibit stationary [12]. The method of calculating the weighted average of variables under the stationary conditions is the ordinary Kriging method.
The estimated value at a certain point (x) is given generally as the weighted average of the measured values (w(x )(i 1, 2,...,n)) at n points (xi) around (x), from Eq. (1).
When the difference between the estimated value from Eq. (1) and the true value is the estimated error (R(x0)) and the expected value is obtained from Eq. (2)
Where, if the abovementioned stationarity is assumed for a stochastic process (w), the expected value of the estimated error (R(x0)) becomes zerp, and the impartiality term of the estimated value shown in Eq. (3) is derived.
Also, when the minimum value is calculated from the estimated error distribution shown in Eq. (4), using the Lagrange multipliers method, Eq. (5) can be obtained.
where is called the "variogram" and is the value that reflects the cross-sectional distribution of a physical property (Eq. (6)), which depends on the covariance function is the dispersion of the stochastic process (V).
In addition, the dispersion of (V) in the estimated error of Eq. (6) is converted to Eq. (7).
If Eqs. (3) and (5) are solved together, the weighting (wi) and the Lagrange undetermined multiplier (μ)are obtained, and, by substituting them into Eqs. (1) and (7), the estimated value and the dispersion of (V) in the estimated error at the random location (x0) can be obtained.
Semi-variogram
The cross-sectional distribution of physical properties can be expressed using several methods, such as the covariance function, the correlation function, and the variogram. Here, we use the semivariogram, a type of variogram that has the advantage of easy function modeling, even if the distribution of the physical property is infinitely large [2].
The semi-variogram is represented as half the mean square of the difference in the physical property value between two locations, as shown in Eq. (8).
where Vi and Vj refer to the physical property located at points i and j, respectively. In addition, the used semi-variogram is a discrete function; it is impossible to measure a theoretical semi-variogram (γij) that is a continuous function, from the obtained data. Thus, as shown in Eq. (9), the semi-variogram is obtained by discretizing the data in couples of any distance. This is referred to as the experimental variogram (γ * (h)).
Where vi and vj are the values for distances hij and N (h) In addition, when the experimental variogram for any distance (h) is obtained, the allowable distance ±Δh is provided, and γ * (h) is calculated from the average value of the discrete values within the range.
In order to obtain this semi-variogram using the Kriging method, it is necessary to approximate the discretized data based on a functional model [13]. In this study, a Gaussian type semi-variogram is used, which is simple and strict when the ground is modeled as a random field, particularly in the three dimensions [14]. The Gaussian type semi-variogram is shown in Eq. (10)
where a is called the "range," and its value indicates that the distance between the data cannot be correlated, if a exceeds this range. C0 is called the "nugget", and it reflects the data uncertainty, including the measurement error; if this value is too large, the prediction accuracy decreases considerably. (C0 + C1) is called the "sill", the convergence value at which the semi-variogram becomes constant, when the distance between data exceeds it. The range, the nugget, and the sill are obtained from the results of Eq. (9), using the least-square method [15].
Searching over an ellipsoidal region
In predicting the cross-sectional distribution, only the data included in searching over an ellipsoidal region is used. Searching over an ellipsoidal region refers to a spheroid with a certain diameter, which is determined from the range obtained from semi-variogram [13]. In this study, the depth data for two points is frequently treated. If the Kriging method does not make a prediction considering the data configuration, the accuracy may be poor [16]. Therefore, in this study, we search over an ellipsoidal region considering the range and data configuration, to perform the prediction of the cross-sectional distribution.
Overview of the NSWS test
The NSWS test is a reformed version of the conventional SWS test, in which the ground soundness is evaluated from the weight of the penetration rod and the rotation [1]. Figure 1 shows an overview of the NSWS test. The "inclination direction measurement" is one of the features of the NSWS test. Inclination direction measurement refers to measuring ground characteristics including physical properties by penetrating the rod diagonally into the ground in Figure 2. Figure 3 shows an example of the converted N value in the vertical direction and the converted N value in the inclination direction actually measured by the NSWS test.
Figure 1: Overview of the NSWS test.
Figure 2: Inclination direction measurement as a feature of the NSWS test.
Figure 3: Overview of the measuring points based on the NSWS test.
Site Summary of In-situ ground investigation by the NSWS test
The study site is a bank of detached houses constructed in Nishinomiya City, Hyogo Prefecture. The site is a stepwise residential land in the slope with two lines of general two-story detached houses. In part of the premises, ground subsidence was generated, partially deforming the buildings and the surrounding concrete walls. The measuring points are the vertical measuring points on the north side of the premises (1, 2, 5, and 6), and the vertical measuring points on the south side of the premises (retaining wall side) (3, 4, 7, and 8), as shown in Figure 4. Moreover, inclination direction measurement was carried out at points 10, 11, 12, and 13.
Figure 4: Overview of the measuring points on site based on the NSWS test.
Plan and Results for Prediction of Cross-Sectional Distribution by Geostatistical Methods
The ground characteristics from the predicted cross-sectional distribution in this study are converted N-values obtained from the NSWS test. Using these converted N-values as a case study, we examine the applicability of the Kriging interpolation method to detached-house grounds. In the NSWS test, the converted N-value is measured at 0.01 m depth intervals. Thus, based on the converted N-values obtained by the NSWS test, the Kriging method is applied to predict the cross-sectional distribution in the depth direction using the converted N-values. In order to obtain highly accurate predictions, it is necessary to acquire measurements at regular depth intervals. In addition, for comparison, we predict the cross-sectional distribution from 0.25 m measurements using the SWS test, in the same manner.
Prediction of cross-sectional distribution for the cross section between measuring points 1 and 5
When the Kriging method is used, modeling is performed by approximating the semi-variogram. Semi-variogram at 0.01 m measurement interval and semi-variogram at 0.25 m measurement interval are shown. The results of each semi-variogram are shown in Figure 5. The figure on the left is a semi-variogram at the 0.01m measurement interval, the figure on the right is the semi-variogram at the measurement interval of 0.25 m. respectively. In semi-variogram at 0.01 m measurement interval, C0 =1.0, C0+C1=3112.0, and a=3.06. In semi-variogram at 0.25 m measurement interval, C0=320.0, C0+C1=5750, and a=2.62. In general, the prediction accuracy increases when C0 decreases, whereas correlation is improved for larger values of a. Comparing both results, it appears that C0 is smaller, a is larger, and the measurement accuracy is higher for the 0.01 m interval measurements than for the 0.25 m interval measurements, and the converted N-value is correlated better.
Figure 5: Calculated semi-variogram for 0.01 m and 0.25-m measurement intervals at the cross section of measuring points 1 and 5.
Both results differ significantly, regarding the number of measured values to be considered. The number of raw measured values is the same; however, when the number of measured values extracted from the raw data is different, this difference falls within the prediction accuracy boundaries. Thus, accuracy improves when the number of measured values is large, allowing for the prediction of the crosssectional distribution. Therefore, the cross-sectional distribution should be predicted taking into account as many measured values for the vertical direction as possible. Therefore, it is necessary to predict the spatial distribution after measuring a lot of data in the vertical direction as much as possible for spatial distribution prediction in order to make the inside of the ground more detailed.
The predicted values by the Kriging method for the measurement intervals of 0.01 m and 0.25 m are shown in Figure 6, respectively. The figure on the left is the result of kriging at 0.01 m measurement interval, the figure on the right is the result of kriging at 0.25 m measurement interval. In both cases, the soft ground, where the converted N-values are below 5, from the surface layer down to almost 1.0 m depth, is frequently predicted. The converted N-values are predicted accurately (Figure 6). In addition, we do not observe any significant difference between the results shown in these two figures. Therefore, prediction of the cross-sectional distribution for 0.25 m measurement intervals is considered accurate. This is attributed largely to the difference in the nature of the semi-variogram, as well as to the number of measured values used. Based on the discussion presented in the previous section, it is thought that, due to the nature of the semi-variogram, the accuracy of the predicted cross-sectional distribution using 0.01 m measurement interval values is higher than that using 0.25 m measurement interval values.
Figure 6: Predicted values via the Kriging method using 0.01-m and 0.25-m measurement intervals in the cross section of measuring points 1 and 5.
Measuring point 2 is located 7 m away from measuring point 1, almost along the line connecting measuring points 1 and 5 (Figure 4). Therefore, it is considered that the converted N-value in the vertical direction of measuring point 2 can be compared with the predicted values of the cross-sectional distribution for the cross section between measuring points 1 and 5. Based on the predicted values of the crosssectional distribution using 0.01 m and 0.25 m measurement interval values at measuring points 1 and 5, we compare these predicted values (converted N-values predicted with the Kriging method) with the measured values (converted N-values obtained through the NSWS test) in the vertical direction at measuring point 2 (Figure 7). The figure on the left compares the predicted value with the measured value at the 0.01 m measurement interval, and the figure on the right compares the predicted value and measured value at the 0.25 m measurement interval. From these figures, predicted values close to the measured values are obtained at less than 1.2 m depth in both cases. However, the predicted values are different from the measured values (converted N-values obtained by the NSWS test) at depths greater than 1.2 m in both Figure 7. For a quantitative evaluation, the relative errors between the predicted values and the measured values are approximated. The relative error is 0.523 for measurement intervals of 0.01 m, and 1.054 for measurement intervals of 0.25 m. From this, the error is smaller when the Kriging method is applied with measurement intervals of 0.01 m, and it is possible to make higher accuracy predictions, when C0 in the semi-variogram is smaller. Therefore, it is considered preferable to take into account the number of the measured values, when predicting the cross-sectional distribution of the ground characteristics.
Figure 7: Comparison between the values predicted through the Kriging method with measurement intervals of 0.01-m and 0.25-m at the cross section of measuring points 1 and 5, and NSWS test measured values at measuring point 2.
Comparison of the prediction methods
The distribution of ground characteristics has been predicted by the "drawing method," which is a conventional method, using N-values and converted N-values obtained through the SPT test and the SWS test.
Based on the measured values obtained with the NSWS test at measuring points 1 and 5, the prediction of the cross-sectional distribution with the Kriging method (Figure 6) and the drawing method (Figure 8) are compared. The surface portion is considered the soft layer with a converted N-value of 5 or less (Figures 6 and 8). The difference is that, at the points around measuring point 5, the ground with a converted N-value of 5 or more is abundantly predicted from the place with relatively shallow depth in Figure 8, compared with Figure 6. Large converted N-values are not predicted until the depth increases (Figure 6). From this, it is clear that interpolation differs significantly between the Kriging method and the drawing method.
Figure 8: Values predicted using the drawing method with 0.01-m measurement intervals for measuring points 1 and 5.
The values predicted using the drawing method is also compared with the measured values. Figure 9 shows the measured values (converted N-values obtained with the NSWS test) and the predicted values (converted N-values predicted with the drawing method). The results start to differ significantly around the depth of 1.3 m (Figure 9). Therefore, it is difficult to predict the cross-sectional distribution with relatively high accuracy using the drawing method. In addition, the relative error in the separation level between the measured values and the predicted values is 1.282. Comparing this with the cross-sectional distribution predicted with the Kriging method with measurement intervals of 0.01 m and 0.25 m, the largest relative error occurs using the drawing method. Consequently, in both cases, the cross-sectional distribution can be predicted with higher accuracy through the Kriging method than with the drawing method.
Figure 9: Comparison between the values predicted through the drawing method and the measured values obtained through NSWS testing at measuring point 2.
Predictions of cross-sectional distribution for different cross-sections of the measuring points
Measuring points 1, 2, 5, and 6 are located almost on a straight line (Figure 4). In the previous section, prediction of cross-sectional distribution was made only at measuring points 1 and 5, and the obtained values (converted N-values obtained through the NSWS test) were compared to the predicted values at measuring point 2. In this section, the cross-sectional distribution is predicted at measuring points 1, 5, and 6, and the converted N-values at measuring point 2 are predicted and compared with the measured values. Thus, we determine the difference between the predicted values and the measured values at measuring point 6, and the difference in the estimation accuracy when the predicted values increase in the horizontal direction.
Figure 10 shows the predicted values based on the Kriging method at the cross section of measuring points 1, 5, and 6. The figure on the left is the result of kriging at 0.01 m measurement interval, the figure on the right is the result of kriging at 0.25 m measurement interval. In Figure 10, the characteristics of the soft ground are predicted along the vertical direction (depth) by adding the corresponding measured values at measuring point 6. Thus, the predicted values of the cross-sectional distribution are changed by taking into account the measured values at different measuring points.
Figure 10: Values predicted using the Kriging method with 0.01-m and 0.25 –m measurement intervals in the cross section of measuring points 1, 5, and 6.
Figure 11 shows the comparison between the measured and the predicted values at measuring point 2. The figure on the left compares the predicted value with the measured value at the 0.01 m measurement interval, and the figure on the right compares the predicted value and measured value at the 0.25 m measurement interval. When the measurement interval of 0.01 m is applied, the two values differ below 1.5 m depth (Figure 11). However, when the measurement interval of 0.25 m is used, a large separation occurs around the depth of 1.4 m. Comparing the respective predicted cross-sectional distributions (Figures 6 and 10), large converted N-values do not appear in depth due to the addition of measuring point 6. Moreover, comparing Figures 7 and 11, the values around 1.4 m depth are similarly separated by the addition of measuring point 6, even though the overall accuracy is higher. In addition, the relative errors in both cases are small, 1.0 or less. Comparing this with the results at measuring point's 1 and 5 (Figure 6), both relative errors are smaller. Therefore, the predicted converted N-values in the vertical direction at measuring point 2 become more accurate by taking into account the measured values at measuring point 6, and it is thought that the predicted values around measuring point 2 are affected by the measured values at measuring point 6. Moreover, by increasing the number of data to be considered, prediction accuracy is increased, and it is clear that the prediction accuracy of the Kriging method increases by increasing the number of measurement points in the horizontal direction and measuring a lot of data. Therefore, it is necessary to measure as much data as possible in the vertical and horizontal direction.
Figure 11: Comparison of the values predicted through the Kriging method with 0.01-m and 0.25-m measurement intervals at the cross section of measuring points 1, 5, and 6 with the NSWS-test measured values at measuring point 2.
Measuring points 3, 4, and 7 are located almost on a straight line (Figure 4). Thus, the cross-sectional distribution is predicted on the cross section of measuring points 3 and 7 to check the difference between the measured values and those predicted values at measuring point 4. The results using the Kriging method at the same cross section are shown in Figure 12. The ground is soft down to the relatively deep part (Figure 12). Moreover, a ground layer with high converted N-values is predicted around the depth of 1.8 m. This is attributed to the converted N-values of the debris close to measuring point 3.
Figure 12: Values predicted using the Kriging method with 0.01-m measurement intervals in the cross section of measuring points 3 and 7.
Figure 13 shows the comparison of the measured values and those predicted. The separation from the measured values is smaller down to the depth (Figure 13). In addition, the overall relative error is calculated, which turned out to be 0.284. Thus, the value of the relative error is 1.0 or less, close to 0, showing that the error is relatively small. Therefore, the Kriging method is considered applicable for predicting the cross-sectional distribution of ground characteristics including physical properties at narrow detached house embankment grounds.
Figure 13: Comparison of the values predicted using the Kriging method with 0.01-m measurement intervals at the cross section of measuring points 3 and 7 with measured values obtained through NSWS testing at measuring point 4.
Predictions of cross-sectional distribution considering measured values in the inclination direction
The converted N-values may also be measured in the inclination direction through NSWS testing. Based on this, by comparing the measured values with predicted values in the inclination direction, it is possible to check whether the Kriging method can predict the crosssectional distribution successfully. Figure 14 shows the measured converted N-values from the NSWS test at measuring point 10 in the inclination direction toward measuring points 3 and 4. Therefore, since the predicted values (converted N-values predicted with the Kriging method) at measuring point 10 are determined by predicting the cross-sectional distribution from the values at measuring points 3 and 7 (Figure 12), it is possible to compare the measured values with the predicted values.
Figure 14: Comparison between the value predicted using the Kriging method with 0.01-m measurement intervals at the cross section of measuring points 3 and 7 and the NSWS-test measured values at measuring point 10.
Figure 14 shows the comparison of the measured values and those predicted at measuring point 10 based on Figure 12. The separation from the measured values is small down to about 1.7 m depth (Figure 14). However, the separation from the measured values is larger beyond the depth of about 1.8 m. This is because the place brought to close at the measuring point 3 has an influence. The overall relative error is 0.596, less than 1.0, and thus relatively small. Therefore, the Kriging method can be used to predict the cross-sectional distribution successfully.
In order to predict the converted N-values in the inclination direction, the cross-sectional distribution is predicted based on the measured values only at measuring points 3 and 7 (Figure 12). Here, we investigate the effect of interpolation on the predicted values, by adding the converted N-values measured in the inclination direction at measuring point 4.
Figure 15 presents the predicted cross-sectional distribution based on the measured values at measuring points 3, 4, and 7, which is used to predict the converted N-values in the inclination direction. Compared with the predicted cross-sectional distribution using the measured values at measuring points 3 and 7 (Figure 12), we confirm that the size of the weak surface layer changes. However, the other distributions are not significantly changed, and weak layers appear across the deeper ground layers. Figure 16 shows the comparison of the measured values (converted N-values obtained with the NSWS test) with the predicted values (converted N-values predicted with the Kriging method) at measuring point 10 based on Figure 15. The difference is small down to about 1.7 m in depth (Figure 16). However, it increases below 1.8 m, probably due to the presence of debris near measuring point 3. In addition, the overall relative error is 0.465, i.e., less than 1.0, which is considered small. Therefore, it is considered that the kriging method is effective also when predicting the property value in the inclination direction.
Figure 15: Values predicted using the Kriging method with 0.01-m measurement intervals at the cross section of measuring points 3, 4, and 7.
Figure 16: Comparison between the values predicted using the Kriging method with 0.01-m measurement intervals at the cross section of measuring points 3, 4, and 7 and NSWS-test measured values at measuring point 10.
In all the above results, the cross-sectional distribution is predicted based on the converted N-values only in the vertical direction. Therefore, next, we examine the differences in the predicted crosssectional distribution based on the measured values in the inclination direction.
The measuring point 10 shows the converted N-values measured in the inclination direction toward the measuring points 3 and 4. Thus, we examine the interpolation effect on the predicted crosssectional distribution at measuring point 3, 7, and 10. Figure 17 shows the predicted cross-sectional distribution at measuring points 3, 7, and 10. Comparing Figures 12 and 17 the predicted values around 1.5 m depth near measuring point 7 are only slightly different. Moreover, the values at measuring point 4 are also predicted from this cross section, and they are compared here with the measured values.
Figure 17: Values predicted using the Kriging method with 0.01-m measurement intervals at the cross section of measuring points 3, 7, and 10.
Figure 18 shows the comparison between the measured values (converted N-values obtained with the NSWS test) and the predicted values (converted N-values predicted with the Kriging method) at measuring point 4 based on Figure 4. The difference from the measured values is small at all depths (Figure 18). In addition, the overall relative error is 0.347, i.e., less than 1.0, which is considered small. However, compared with the results of Figure 13, the predicted values using the measured values at only measuring points 3 and 7 are lower, although the difference is very small. Consequently, the measured values in the inclination direction may be ignored when predicting the cross-sectional distribution. Therefore, we consider that the measured values in the inclination direction should only be used as ancillary results.
Figure 18: Comparison of the values predicted using the Kriging method with 0.01-m measurement intervals at the cross section of measuring points 3, 7, and 10 with the NSWS-test measured values.
Effectiveness of New Ground Survey Tester and New Application Example of Kriging Method
In this research, spatial distribution prediction by the Kriging method was performed using both the survey results at 0.01 m intervals at which the data measurement interval can be measured with NSWS and the survey results at 0.25 m intervals that can be measured with SWS. From the results, it was confirmed that the error in the survey results at intervals of 0.01 m, which is the measurement interval of NSWS, is smaller in any case in comparison between the measured value and the predicted value. Comparing the results of both spatial distribution predictions, it was confirmed that the 0.01 m interval, which is the measurement interval of NSWS, can interpolate the distribution of ground physical property values in detail. From these results, it is thought that by precisely measuring ground physical property values, we can predict the interior of the ground in detail and at the same time with high precision. Therefore, the effectiveness of NSWS which is a new ground survey tester is considered to be very high.
Next, in various cases, the Kriging method was carried out to summarize the state of the distribution of the ground physical property values and the relative error with the actual measurement value, and at the same time, it compared with the prediction result of the spatial distribution by the conventional wire drawing. From these results, it was confirmed that the Kriging method can predict the ground physical property value with high accuracy even in heterogeneous ground. In addition, it can be confirmed that the Kriging method predicts the physical property value in detail also in the state of distribution, and from this it is considered that information in the ground can be grasped in detail, not in a rough but detailed manner, by using the Kriging method. From this, it is considered that the Kriging method can be applied sufficiently in heterogeneous ground. Inhomogeneous ground will have larger variability in comparison with homogeneous ground. Therefore, it is considered that using the Kriging method is more suitable for predicting the ground physical property value of heterogeneous ground because it can predict fine physical values.
In the Great East Japan Earthquake, large-scale ground problems occurred (for example, liquefaction) in detached house embankment ground. One of the reasons for this was considered to be due to the fact that the state of the ground just under the house was only grasped roughly and detailed information was not known. In order to solve such problems from now on, it is essential to grasp the state of the ground in more detail, and to solve the ground problems beforehand on that basis, as mentioned in the first chapter.
The NSWS used in this study can be thought from the result of this research that it is a testing machine that is necessary to solve the ground problem because it can measure the ground physical property value finely. Moreover, it is considered that the application in heterogeneous ground by the Kriging method is sufficiently applicable from the result of this research. Therefore, in order to solve the ground problems in the future, it is essential to establish so that both the testing machine used this time and the Kriging method will be applied to the heterogeneous ground. And it is thought that we can solve and elucidate the ground problem by widely notifying even in the homogeneous ground.
In this study, spatial distribution prediction is performed using Geostatistical method based on data of ground physical property values measured using soft and thin, heterogeneous embankment ground (detached house ground) using NSWS test (machine) prediction of surface strength of target ground was carried out. Based on the results of surface strength prediction, we investigated the effectiveness of a new survey tester and applied case analysis of the Kriging method in heterogeneous ground. As these results, the following conclusion was obtained.
(1) In this study, Kriging method was carried out for both the 0.01 m interval which is the measurable interval of the ground physical property value of NSWS and the 0.25 m interval which is the measurable interval of the ground physical property value of SWS. As a result, it was confirmed that the error interval from the surveyable interval of NSWS, 0.01 m interval is smaller than the measured value as a result of spatial distribution prediction, and the distribution of the ground physical property value can be grasped in detail. Therefore, NSWS is considered to be a very effective testing machine in order to grasp the state of the ground well.
(2) Kriging is performed in various cases on a heterogeneous ground. As a result, it was confirmed that the error from the measured value was small in any case. Therefore, it is considered that Kriging method can be sufficiently applied even in heterogeneous ground.
(3) From the results of spatial distribution prediction based on the difference in the number of measurement points, it can be confirmed that the deviation from the measured value decreases as the number of measurement points increases in the horizontal direction in the survey. From this, it is considered that as the number of data in the horizontal direction increases, the prediction accuracy increases, so it is necessary to measure as much data as possible in the horizontal direction. Therefore, NSWS is considered to be highly effective because it can measure ground physical property values in the horizontal direction.
In this study, the ground characteristics, including the physical properties of the target ground, are revealed through cross-sectional interpolation. At the same time, the applicability of the Kriging method to narrow grounds such as detached-house grounds is investigated by comparing the predicted with the measured values. In the future, the target ground is going to be evaluated from the viewpoint of risk engineering, discussing the difference with the measured values.
Nishimura G, Inazumi S, Okita K (2016) The investigation and reinforcement for residential grounds located on inclined bedrocks. Journal of the Society of Materials Science 65: 74-79.
Yoshida M, Miyajima M and Kitaura M (1999) Estimation of spatial liquefaction potential and its application to evaluation of liquefied ground flow at plane ground. Journal of the Japan Society of Civil Engineers 49: 155-166.
Noguchi T, Tanaka M, Watanabe Y, Sakagami S, Sadamura T (2011) Verification of 3-D visualization of ground at Tokyo International Airport. Japanese Geotechnical Journal 6: 69-79.
Fujii M, Satoru K, Futaki M, Tamura M, Ijuin H et al. (1996) Relation between NSW of Swedish weight sounding and N-value of standard penetration test. AIJ Journal of Technology and Design 3: 64-68.
Ohnishi Y, Tanaka M, Ohsawa H (1992) Basic study on estimation of physical parameters in heterogeneous ground. Doboku Gakkai Ronbunshu. 457: 51-58.
Georges M (1963) Principles of geostatistics. Economic Geology 58: 1246-1266.
Matsui K, Maeda Y, Isii K, Suzuki M (1991) Probabilistic estimation of spatially distributed N-values and its application to pile design. Doboku Gakkai Ronbunshu 436: 57-64.
Koike K, Liu C, Tamura A (2005) Application of spatio-temporal geostatistics to detection of secular changes of environments in a closed sea. Geoinformatics 16: 3-15.
Miyaya Y, Kogure K, Honjo Y (1997) Estimation of spatial variation of soil profiles in a submarine clayey deposit. Japan Society of Civil Engineers 13: 441-446.
Kobayashi A, Hinata H, Fujii K (1998) Back Analaysis of Hydraulic Conductivity Distribution with Geostatistical Simulation Results as Prior Information. Journal of applied mechanics 1: 129-136
Syoji T, Koike K (2007) Kriging-estimation of spatial data taking account of error. Journal of the Geothermal Research Society of Japan 29: 183-194.
Cao R, Zee MY, Gomez E (2014) Geostatistical applications in petroleum reservoir modelling. Journal of the Southern African Institute of Mining and Metallurgy 114: 625-631.
Stein ML. Interpolation of spatial data: some theory for kriging. Springer Science & Business Media; 2012 Dec 6.
Honjo Y, Otake Y, Kato H (2012) A simplified scheme to evaluate spatial variability and statistical estimation error of local average of geotechnical parameters in reliability analysis. Journal of Japan Society of Civil Engineers 68: 41-55
Journel AG, Huijbregts CJ (1978) Mining Geostatistics, Blackburn Press, New Jersey, USA.
Araki SS, Ueda T, Yamamoto S, Tohno S (2011) A study of the prediction method of the spatial distribution of ozone by Kriging in Otsu city and its surrounding area. Journal of Japan Society for Atmospheric Environment 46: 241-250.
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Within this Website Within LTA Websites Within All Government Websites
Statistics & Publications
Rail Expansion
Road & Commuter Facilities
Technologies & Innovation
Active Mobility
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Industry & Innovations
Industry Transformation Map
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Rules & Public Education
Rules & Code of Conduct
Cycle, Scoot and Ride Considerately
Our paths and roads are shared spaces. We should consider the safety of our fellow travellers, no matter whether we drive, walk, cycle or ride active mobility devices.
LTA's vision for a car-lite nation is not only about providing the amenities to support walking, cycling and riding, but also building a culture of courtesy and respect for fellow commuters.
Active Mobility Act (AMA)
The Active Mobility Act (AMA) came into force on May 2018, providing a set of rules and code of conduct to enable safer sharing of public paths among various users. If you fail to adhere to the rules, you may face prosecutorial action by the Authority. Notices to Attend Court (PDF, 52kb) may also be issued should you commit an offence under the Active Mobility Act.
Active mobility devices governed under the AMA include:
Power-Assisted Bicycles (PAB)
Motorised and Non-motorised Personal Mobility Devices (PMDs): Kick-scooters, electric scooters, hoverboards, unicycles, etc.
Personal Mobility Aids (PMAs): Wheelchairs, motorised wheelchairs or mobility scooters which are designed to carry an individual who is unable to walk or has walking difficulties.
The AMA also provides LTA with legislative and enforcement powers to regulate the sale of PMDs, PABs and PMAs.
Where You Can Ride Your Device
Know where you can ride and the speed limits to observe for the safety of yourself and others. You may face penalties if caught speeding or riding your device on the wrong path or road.
Cycling Paths and Park Connectors can be easily identified by markings on the ground (see examples below). These paths have a speed limit of 25km/h, and can be used by cyclists and users of PMDs, PABs and PMAs.
In comparison, footpaths do not have any 'Cyclist' or 'PCN' logos. The speed limit for footpaths is 10km/h. The riding of PABs and motorised PMDs are not allowed on footpaths (PDF, 537kb). Bicycles, PMAs and non-motorised PMDs, such as manual kick-scooters, can be used on footpaths.
Only cyclists and PAB riders are allowed to ride on roads, except for expressways and road tunnels. Under the Road Traffic Act, it is mandatory for cyclists and PAB riders to wear a helmet when riding on roads.
1All e-scooters and PABs must be registered with LTA. To find out more about the registration process, visit www.onemotoring.com.sg
Device Criteria
If you are using a bicycle, power-assisted bicycle (PAB) or personal mobility device (PMD), ensure your device meets the following device criteria before riding them on public paths:
UL2272 fire safety standard for motorised personal mobility devices
The UL2272 fire safety standard improves the safety against fire and electrical hazards significantly. Motorised personal mobility devices (PMDs) that are certified to the UL2272 standard have to pass a stringent set of tests conducted by accredited testing centres under extreme physical conditions. Users who modify a UL2272-certified PMD may render its certificate invalid, posing a fire risk.
From 1 July 2020, only UL2272 motorised PMDs (PDF, 1.7mb) are allowed on cycling paths. Non-UL2272 e-scooters that have been registered with LTA have been automatically de-registered on that date. Non-UL2272 and modified PMDs are a fire risk, and should be properly and safely disposed of. LTA will continue to maintain records of non-UL2272 PMDs and may contact the owners for further action in future should the need arises. For more details on where to dispose your PMDs, please click here.
A mandatory inspection regime for registered e-scooters has been introduced from 3 April 2020, and all e-scooters which were earlier registered and self-declared UL2272-certified will be scheduled for inspections (please note these inspections will resume from 4 June 2020 onwards). All new e-scooters will also have to pass inspections for UL2272 certification and width, weight, and device speed before they can be registered with LTA.
You may face penalties if caught riding a device that does not meet these criteria on public paths.
Why is the adoption of a device safety standard necessary?
The number of fires caused by motorised PMDs have increased in the past few years, with more than 90 fire incidents involving PMDs since 2016. As PMDs only started gaining popularity in recent years, many of the motorised PMDs used do not conform to any reliable safety standards. The adoption of a safety device standard is therefore necessary to ensure the safe use of such devices.
LTA carefully studied the safety standards available and determined that the UL2272 standard is most suitable for the common types of motorised PMDs used in Singapore, such as e-scooters, self-balancing hoverboards and electric unicycles.
Incidents of PMD-related fires over the last few years (photos credit from Singapore Civil Defence Force's Facebook)
Why was UL2272 chosen over other standards?
It is often difficult to determine the exact cause of the fires due to the extent of fire damage to the PMDs involved. However, based on literature reviews and consultations with the Singapore Civil Defence Force (SCDF), PMD fires may be caused by an electrical anomaly to the electrical circuitry or batteries, which could result from various factors, but not limited to, physical damage to the device, overcharging of rechargeable batteries, use of an unsuitable charger/battery, or manufacturing defects.
UL2272 is assessed to be a suitable safety standard as it evaluates PMDs from a system-level perspective, which better anticipates the full spectrum of usage conditions of the entire PMD, instead of just assessing individual components of a device.
Getting a device certified for UL2272 requires passing a series of electrical, mechanical and environmental tests. Its testing parameters are based on several components like the electrical and mechanical factor for safety, as well as the impact of environmental factors such as exposure to water.
Evaluation tests for UL2272 can be broadly classified into 3 categories as follows:
1. Electrical Tests
Electrical tests typically cover a range of tests:
Over-charge
Over-discharge
Dielectric voltage
Isolation resistance
Imbalanced charging
A temperature test typically determines whether a device's battery cells and critical components are able to withstand specific operating current, voltage and temperature limits during charging and discharging conditions, e.g. when the device is in use/operation.
Image on the left: A device undergoing a 'temperature test' (photo credit: SGS Testing & Control Services)
2. Mechanical Tests
Mechanical tests typically cover a range of tests:
Mold stress release
Handle loading
A vibration test evaluates the device's ability to withstand vibration that may occur during its anticipated use. Device would be subjected to vibration in each axis for a stipulated period of time depending on the number of samples submitted.
Image on the left: A device undergoing a 'vibration test' (photo credit: TUV Rheinland)
During an 'IPX4 test', water is sprayed on devices from all directions using a rotating sprinkler system for 10 minutes to simulate rain and splashing of water. Following the test, device will be placed under an observation period to ensure that there is no explosion, fire, rupture, electrolyte leakage and shock based on the stipulated testing parameters.
Image on the left: A device undergoing an 'IPX4 test' (photo credit: SGS Testing & Control Services)
PMD models that have been awarded the UL2722 certification must also undergo regular factory inspections to verify that device production continues to comply with requirements under the UL2272 standard. Manufacturers who persistently do not demonstrate adherence to the standard and take subsequent actions to ensure that their production line adheres to the standard may have their certification cancelled.
NOTE: While the UL2272 fire and electrical safety standard greatly reduces the risks of fire, PMD users should always practice proper handling and safety tips to prevent PMD fires and avoid exposing devices to extreme conditions or stress.
View these frequently-asked questions (FAQs) (PDF, 219 kb) for more information on UL2272 certified PMDs.
Walking, Cycling, Riding PMDs on Public Paths
Whether you are walking, cycling or riding a PMD, everyone has a role to play to build a safer and more gracious path sharing culture.
Rules for cycling and riding PMDs
Familiarise yourself with the rules under the Active Mobility Act before you ride.
5. Stop and look out for incoming traffic before riding across the road
6. Dismount and walk your device when you see a "No Riding" sign.
7. Stop, offer help, exchange particulars, and make a police report if you are involved in an accident.
8. Those under 16 years of age cannot ride PABs on cycling paths even if supervised by an adult.
9. Those under 16 years of age cannot be carried as a passenger on a PAB on cycling paths.
From 1 August 2020, individuals under the age of 16 are not allowed to ride e-scooters, unless supervised by an adult who is at least 21 years old. If you intend to supervise an individual below the age of 16 to ride an e-scooter, please familiarise yourself with the guidelines of a supervisor (PDF,177kb).
Guidelines for walking, cycling and riding your PMDs
Being gracious keeps you and others safe. If you ride a bicycle or PMD, always give way to more vulnerable users on the paths. If you are walking, remain alert and keep to footpaths.
Cyclists and PMD riders
Always give way to pedestrians.
Watch your speed and go slow around others.
Slow down when approaching bus stops and/or intersections of public paths.
Walk your device in crowded areas.
Gently alert others before overtaking.
Keep left on paths unless overtaking.
Ride on cycling paths and bicycle crossings when available.
Keep a safe distance from other path users, especially when overtaking to avoid a collision.
Avoid shining your lights onto the face of other path users.
Check that your lights, brakes and tyres are in good working condition before setting off.
Check the height of your handlebars and seat on the bicycle to ensure that you have full control of the device when coming to a sudden stop in an emergency.
Keep both hands on the handlebars. Signal your intention to change course or make a turn ahead of time.
Park your devices at designated parking places such as bicycle racks and yellow boxes. Be sure to securely lock your devices (PDF, 388kb) to prevent it from being stolen.
Keep left unless overtaking.
Keep to footpaths and pedestrian crossings.
Stay alert when on public paths. Using a mobile phone or listening to music could prevent you from detecting danger or obstacles.
Read the full code of conduct for public path users (PDF, 93kb) under the Active Mobility Act.
Cycling on Roads
Rules for cycling
Road safety is a shared responsibility. Keep a firm grip on these on-road cycling rules if you use a bicycle or power-assisted bicycle (PAB) on roads.
Obey all traffic signals and travel in the same direction as the flow of traffic.
Wear a helmet when cycling on roads.
Cycle in a single file on single-lane roads and during bus lane operational hours2.
Switch on front white and rear red light3 in the dark.
Do not use a mobile communication device while riding.
Those under 16 years of age may not ride PABs on roads.
Those under 16 years of age may not be carried as a passenger on PABs on roads.
Do not cycle on expressways and in road tunnels.
2Otherwise, cycling two abreast is allowed.
3Rear red reflectors can be used on bicycles and power-assisted bicycles (PABs).
Guidelines for cycling
Cyclists and PAB riders are encouraged to adopt the following practices to safeguard themselves and share the roads safely with motorists.
Always ride as close as practicable to the left-hand edge of roads, and allow traffic to overtake you safely. Keep a straight course, do not weave through traffic and avoid sudden swerves.
Always use bicycle lanes when available.
Keep a safe distance behind moving vehicles. Do not hold on to the back or side of motor vehicles and maintain awareness of traffic when riding.
Do not squeeze between the kerb and a bus that has stopped at a bus stop, or a turning vehicle and a kerb.
Slow down and look out for other road users when approaching bends, junctions, bus stops and pedestrian crossings or when passing a parked car.
If the hill is too steep, get off and walk with the bicycle. Keep your cycling speed under control when riding on downhill roads.
Wear bright-coloured clothing to increase your visibility to other vehicles and pedestrians.
Plan ahead and pick the safest route, and keep out of heavy traffic as much as possible.
Do not carry anything in your arms that may interfere with the proper control of your bicycle.
Check the height of your handlebars and seat on the bicycle to ensure that you have full control of the device.
Keep both hands on the handlebars. When signalling your intention to change course or make a turn, do so ahead of time and return your hand to the handlebars before you turn.
Read the full code of conduct for cycling on roads under the Highway Code.
Mandatory Registration for E-scooters and Power-Assisted Bicycles
Own an e-scooter or a power-assisted bicycle (PAB)? Do note that there is mandatory registration for these devices.
From April 2020, do check that any new e-scooters you purchase is already registered with LTA by the retailer. Retailers are required to send all e-scooters to be inspected and certified at an LTA designated E-scooter Inspection Centre before they can be advertised, displayed and/or sold for use on cycling paths (including letting for hire). It is an offence to ride an unregistered e-scooter on cycling paths.
Power-assisted bicycles (PABs) must be sealed with the LTA approval seal, registered and affixed with a rear registration number plate. View this list (PDF, 1.14mb) to find out the type-approved PAB models. For details on how to register your PAB, visit www.onemotoring.com.sg/PAB.
Mandatory Inspection for E-scooters
The e-scooter inspection regime was introduced in April 2020 to ensure that e-scooters comply with the device criteria for use on public paths.
There are two categories of inspection under the e-scooter inspection regime:
1) Verification Inspection of e-scooters registered before April 2020 and declared to be compliant with the device criteria of weight, width, speed and UL2272 certification;
2) Registration Inspection for new e-scooters to be sold or let for hire by retailers/companies. From April 2020, only new e-scooters meant for sale or let for hire by retailers/companies are allowed to be registered with LTA after passing Registration Inspection.
For Existing Registered E-Scooters
LTA will schedule mandatory inspections for e-scooters registered before 1 April 2020 and were declared as compliant to the device criteria of no more than 20kg, 70cm and 25km/h device speed, as well as UL2272-certified. This first-time inspection will be at no cost to these owners. Owners of these devices will receive a notice from LTA informing them to bring their devices in for inspection at any of the nine E-scooter Inspection Centres (EICs) island-wide. Click here (PDF, 114kb) to see a list of the locations of the inspection centres.
Those who fail to send their devices for inspection by the deadline stated in the notice could have their devices deregistered and face a fine of up to $1,000 and/or jailed up to three months, if convicted.
Please ensure that your e-scooter registration mark is affixed at the front or at the sides of your e-scooter stem, is upright, intact, not obscured and not void/damaged before presenting for inspection. E-scooters with missing/invalid registration mark will be barred from inspection. Owners who require a replacement of the registration mark for their e-scooter may wish to submit a request via www.lta.gov.sg/feedback with more details1 and schedule an appointment with LTA for the registration mark. Do note that a fee of $20.00 for the replacement of the registration mark will be chargeable, and the registration number of the e-scooter will be changed.
Owners who require more time to present their e-scooter(s) for inspection may wish to write in to LTA_escooter_insp@lta.gov.sg with more details.
E-scooters that do not pass the mandatory inspection due to non-compliance with the weight, width, speed or UL2272 certification requirement will be deregistered by LTA. Owners who wish to request for a re-inspection of their e-scooter(s) may write in to LTA_escooter_insp@lta.gov.sg after completing the necessary adjustments to ensure compliance with LTA's device requirements.
For New E-scooters to be Registered On or After 1 April 2020
From April 2020, all new e-scooters will be required to pass through inspection at one of the nine EICs before they can be registered for use on cycling paths. Retailers/businesses will only be able to display, advertise, sell or let for hire e-scooters that are inspected and certified.
It is an offence to ride an unregistered e-scooter on cycling path. First-time offenders can be subject to a fine of $2,000 and/or imprisonment of up to three months.
It is also an offence to use non-compliant e-scooters on public paths and first-time offenders may face a fine $10,000 and/or imprisonment of up to six months.
For more information on inspection and registration of new device(s), please refer to the comprehensive guide here.
1 If your registration mark is damaged, please provide a photograph of the damaged registration mark to LTA for assessment, the registered owner's full name and the last 4 characters of the Singapore NRIC/Employment Immigration Pass Card (e.g. 321A).
If your registration mark is missing, please make a police report on the loss of your registration mark first, followed by providing LTA with the e-scooter registration number, the registered owner's full name and the last 4 characters of the Singapore NRIC/Employment Immigration Pass Card (e.g. 321A).
Using Shared Devices Responsibly
Help keep our streets neat and free of obstruction by following these steps when you end your trip on a shared bicycle.
Park your shared bicycle at designated parking zones such as yellow boxes and bicycle racks. To find designated parking zones nearest to your location, use your bicycle sharing app.
Look out for the QR code located near the parking area and scan it to end your trip.
You may be charged an additional $5 fee by the bicycle sharing company for failing to park properly and scan the QR-code. After 3 failures, you will face a one-month ban from using all shared bicycle services. The ban period will increase with every subsequent ban. For more details on the user ban process, read this guide (PDF, 261kb).
Active Mobility Insurance
Third-party liability insurance products for non-commercial riders
LTA strongly encourages non-commercial riders to purchase active mobility insurance to protect yourself from personal accident expenses and third-party injury claims. The following are some products1 (PDF,459kb) available on the market:
AA Personal Mobility Plus (AAS Insurance Agency Pte Ltd, underwritten by Liberty Insurance Pte Ltd)
AXA Personal Mobility Protect (AXA Insurance, sold exclusively by AXA@POST)
ePROTECT personal mobility (EtiQa)
Personal Mobility Guard (NTUC Income)
1This list is not meant to be exhaustive nor imply endorsement or recommendation of any of these products or companies. You should make your own assesment on the product that best meet your needs.
Mandatory third-party liability insurance requirements for businesses
For information on active mobility insurance for businesses and commercial riders, please refer to the guide here.
Active Mobility Advisory Panel
The Active Mobility Advisory Panel commissioned in July 2015, is led by Minister of State Associate Professor Dr Muhammad Faishal Ibrahim, and comprises representatives from key stakeholder groups including seniors, youths, cyclists, users of PMAs, motorists and grassroots leaders.
The Panel conducts regular review of active mobility regulations to enhance path safety. You can view AMAP's past reports below:
2016: The Panel recommended a set of rules and code of conduct for cycling and use of PMDs which were accepted in full by the Government and incorporated into the Active Mobility Act.
2018: The Government accepted the Panel's recommendations on 4 September 2018 and have implemented them on 1 February 2019.
2019: The Government accepted the Panel's recommendations with an expanded scope on 4 December 2019.
2020: AMAP submitted its recommendations to the Government on 30 December 2020.
Impounded and Disposal Notice for Devices
The devices listed in this notification (PDF, 252kb) have been impounded by the Land Transport Authority.
For enquiries/requests:
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These are in Fine to excellent condition. Cambellfield went out of business in 1904 so there is some minor ships and scratches. These were found in a shed in Teton Valley Idaho just below The Grand. The Jacksonhole Guide Newspaper they were wrapped in was dated Thursday June 14, 1979. See pictures, further pictures will be posted upon request.
This bottle is in excellent condition with a few blemishes that appear to have occurred during manufacturing. The bottle weighs 1 pound 10 ounces and is 9 inches tall. This bottle has no manufacturer mark.


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Home » Celebrities » Shawne Merriman Won't Rule Out Taking MMA Fight, Any WWE Wrestlers Wanna Scrap?!
Shawne Merriman Won't Rule Out Taking MMA Fight, Any WWE Wrestlers Wanna Scrap?!
Shawne Merriman's bringing back Lights Out Xtreme Fighting — after more than a year of being dormant — and the former NFL star and promotion founder tells us won't rule out taking a fight!
"I'm not opposed to [fighting]," the former Pro Bowler told Babcock and Mojo on the "TMZ Sports" TV show (airs nightly on FS1), saying "I would fight the right former athlete, another guy in the WWE, something that people would pay to see in Lights Out."
The 38-year-old former NFL star continued … "They got my Instagram. They got my number. If anybody really thinking about it and it's a good fight and people wanna see it, come on and step in Lights Out. I got no problem!"
Of course, Shawne wouldn't be the first former football player to transition to fighting. Former NFL star Greg Hardy, who had no combat sports background, fought in the UFC.
Matt Mitrione, former Giants, 49ers and Vikings defensive lineman, was a UFC and Bellator star. Matt beat heavyweight legend Fedor Emelianenko, amongst a bunch of other top fighters. Not to mention Brock Lesnar … and the list goes on and on.
6'4″ Merriman would have had the best football career (though Hardy was dominant on the gridiron, too) of all players if he fought. Shawne played around 265 lbs. and was an athletic freak. He ran a 4.68 40-yard dash and put up 25 reps of 225 lbs. before he was drafted 12th overall in 2005.
Shawne Merriman Launches MMA Promotion, Considering Fighting
Merriman, a huge MMA fan, founded Lights Out (his nickname during his playing days) in 2019. They've had 7 events — all taking place in southern California — with the last going down in December 2021.
But, the promotion is back … "Lights Out Xtreme Fighting 8: The Return" goes down on Saturday in Riverside, CA. The event preliminaries will stream at 6 p.m. PST and the main event will stream live at 7 p.m. PST on Fubo Sports as well as the network's Instagram, Twitter and YouTube plus on Shawne Merriman's social channels
Merriman says he's signed some super-talented fighters and his only hope is they don't leave for Bellator or UFC.
"I know Scott Coker and [Dana White], I've been around this space for a long time. They all know I have a big appetite for growing and being big in the space but ultimately, it's about giving guys, these fighters more eyeballs. Giving them a platform to fight."
"UFC, Bellator, great organizations. They're actually what made me get into the fight game and watch from the beginning. With Lights Out, I think we have an opportunity to come in here and give these fighters an opportunity to go and take care of their families, be legends in the sport, be champions in the sport," Merriman says.
In the meantime, if there are any WWE Superstars out there who wanna get in the cage with Shawne, hit him in the DMs!
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jefferson davis county, georgia
Robert Louis Hughes. IRWINVILLE, Ga. (WALB) - Irwin County Sheriff's Office was made aware of threats on social media to tear down and vandalize Jefferson Davis Memorial State Park in Irwinville. Year Erected: 1957 Marker Text: On May 4, 1865, Jefferson Davis arrived in Washington, Georgia (85 miles N), where he performed what proved to be his last duties as President of the Confederate States of America.Shortly thereafter, with a small staff and escort, he departed enroute to the trans-Mississippi Department, from which vantage point he hoped to negotiate a just peace. Christopher Samuel Green. The county seat of Jeff Davis is Hazlehurst. Phone: 912-375-6700 Fax: 912-375-6703. Showing Arrest Records 1 - 30 of 2,249. 12,684 333 sq mi (862 km 2) Jefferson County: 163: Louisville: 1796: Burke and Warren Counties: Thomas Jefferson (1743 – 1826), third President of the United States. Jeff Davis County Schools. Location: in front of Wilkes County Courthouse, Washington, Ga. JEFFERSON DAVIS On May 4, 1865, Jefferson Davis arrived in Washington where he performed what proved to be his last duties as President of the Confederate States of America. CLICK HERE TO EMAIL US WITH ANY QUESTIONS Jackson County, GA » Jefferson, GA; 2,249 Jefferson, GA Arrest Records Have Been Located. U.S. Route 129 passes through the southwest side of the city, leading northwest 21 miles (34 km) to Gainesville and southeast 19 miles (31 km) to Athens. Jefferson County formed in 1796, was one of the unfortunate counties that lay in the wake of Sherman's army. Cases Per 100k Cases Day Case Change Jeff Davis County Schools. Hazlehurst, GA 31539. The U.S. state of Georgia is divided into 159 counties, more than any other state except for Texas, which has 254 counties.Under the Georgia State Constitution, all of its counties are granted home rule to deal with problems that are purely local in nature. The county is named after Mississippi Senator and Confederate President Jefferson Davis.The county was carved out of Covington and Lawrence counties … James Joseph Roberts. Jeff Davis County, GA Vital Records. QuickFacts provides statistics for all states and counties, and for cities and towns with a population of 5,000 or more. Appling and Coffee counties: Jefferson Davis (1808 – 89), the first and only President of the Confederate States of America. Late on May 8, 1865, Jefferson Davis, with his family and a small escort, camped in Abbeville, unaware that hostile pursuit was close behind. Daniel Tucker's Grave - This classic American folk song may have evolved from slaves singing about a minister near Elberton, Georgia. His pursuers, the 1st Wisconsin Cavalry [Federal], Lt. Col. Henry Harnden, arrived next morning, shortly after his departure. A historic marker indicates the spot where he was arrested, and the surrounding area is now the Based on recent LandWatch data, Jeff Davis County, Georgia ranks 144th among the 159 counties in the state for the total acreage of land listings currently for sale here. JEFFERSON DAVIS. Clint W. Langley Circuit Clerk P.O. Jeff Davis County, Georgia Record Dates [edit | edit source] Information for this chart was taken from various sources, often containing conflicting dates. 44 Charles Rogers Blvd. Robert Louis Hughes. Find 44 homes for sale in Jeff Davis County with a median listing price of $85,000. Jeff Davis County Courthouse Jeff Davis Street PO Box 602 Hazlehurst, GA 31539 Phone: 912-375-6611 Jeff Davis County Website Clerk Superior Court has divorce, court and land records from 1905. Jefferson is located in central Jackson County at (34.126736, -83.590297 It is bordered to the northwest by Pendergrass and to the southeast by Arcade. Georgia law requires that all tax notices are sent to the taxpayer at their last know address. 44 Charles Rogers Blvd. Mobile/Manufactured Home Permits Owners of mobile homes that are located in Jeff Davis County, GA on January 1 must pay the ad valorem taxes by April 1 of each year. Jeff Davis County, Georgia The only president of the Confederate States of America during the Civil War, Jefferson Davis had a long political and military history throughout the tumultuous years that preceded the great conflict. Interpretive Marker Sites. Old Dan Tucker. Jeff Davis is a county in the State of Georgia. The Jefferson Davis County Purchasing office has sole responsibility for purchasing all equipment, heavy equipment, machinery, supplies, commodities, materials, and services used by any office or department of the County except for those offices or departments whose expenditures are not required by law to be approved by the Board of Supervisors. The most famous Civil War treasure traveled by wagon to Georgia with Confederate president Jefferson Davis in the last days of the Civil War. Box 339 Prentiss, MS 39474 Phone: 601-792-5169 Fax: 601-792-5980 Jefferson county, Georgia FREE genealogy. Here you will find information about our office and descriptions of our duties, plus our office address, telephone numbers, and email address. 2,249 Jefferson, GA Arrest Records. Shortly thereafter, with a small staff and escort, he departed enroute to the trans-Mississippi Department, from which vantage point he hoped to negotiate a just peace. The Jefferson Davis County Road Department is responsible for the maintenance, up-keep and improvements to all county roads and bridges. Welcome to the Jefferson Davis County Circuit Clerk web page! Jefferson Davis, Irwin County Posted on August 13, 2014 Marker Time Period: 19th Century Marker Subject: Civil War, Military History Region: Coastal Plain Marker Program: Georgia Historical Commission / Department of Natural Resources County: Irwin kdb@co.jefferson-davis.ms.us Duties and Responsibilities The Tax Assessor is required to annually locate, class and assess all taxable property within the County, and is responsible for setting fair, uniform and accurate values for each parcel of real and personal property. Year Erected: 1957 Marker Text: On May 4, 1865, Jefferson Davis arrived in Washington, Georgia (102 miles N), where he performed what proved to be his last duties as President of the Confederate States of America.Shortly thereafter, with a small staff and escort, he departed enroute to the trans-Mississippi Department, from which vantage point he hoped to negotiate a just peace. All records of deeds were burned at that time, and may valuable books in the office of the Ordinary, but, by some streak of luck, It was originally called Cass County for General Lewis Cass, secretary of war under U.S. president Andrew Jackson and the man most responsible for the removal of the Cherokee Indians from northwest Georgia. CLICK HERE TO EMAIL US WITH ANY QUESTIONS Hazlehurst, GA 31539. Where can find Jeff Davis County Birth, marriage, Divorce and Death Records? Published in 1843 by Dan Emmett (the author of "Dixie"), this tune was popular in both Northern and Southern armies. After the due date there is a 10% filing penalty and 1% per month fee is added. Jefferson Davis Memorial Historic Site (also known as the Jefferson Davis Capture Site) is a 12.668-acre (5.127 ha) state historic site located in Irwin County, Georgia that marks the spot where Confederate States President Jefferson Davis was captured by United States Cavalry on Wednesday, May 10, 1865. LandWatch has hundreds of farms, ranches and other land for sale in Jeff Davis County. Here you will find information about our office and descriptions of our duties, plus our office address and telephone numbers. Are you interested in Jeff Davis County, Georgia land listings and ranches for sale? This treasure train left Danville, Virginia, on April 6, 1865, with $327,022 in gold and silver coins, as well as bullion, donated jewelry, and … Cass sided with abolitionists during the Civil War (1861-65), however, and in 1861 the county was renamed in honor of … According to the United States Census Bureau, the county has a total area of 335 square miles (870 sq. Page 2 | Browse Jeff Davis County, GA real estate. Map of Georgia Counties by COVID-19 cases. QuickFacts Jefferson County, Georgia; Jeff Davis County, Georgia; Georgia. Bartow County was formed from Cherokee County on December 3, 1832. Please feel free to contact us if we can be of service to you. jefferson davis On May 4, 1865, Jefferson Davis arrived in Washington, Georgia (68 miles N), where he performed what proved to be his last duties as President of the Confederate States of America. Charlotte Lynn Jones. Ron Strickland Sheriff P.O. Danny Lee Cannon. Welcome to the Jefferson Davis County Sheriff web page. Its county seat is Prentiss. Phone: 912-375-6700 Fax: 912-375-6703. Please feel free to contact us if we can be of service to you. Mt Olive (Jefferson Davis County) 29 Mar 2013 Mack Graves Cemetery: 31.565655* -89.700946 16 Jul 2019 Magee Cemetery 31.594167 -89.8575 Prentiss East Magee Cemetery 31.634444 -89.873889 Mount Carmel Magee Cemetery 31.650833 -89.9325 New Hebron Magee Cemetery 31.689167 -89.761111 Mount Carmel Magee Cemetery 31.714444 -89.78 Mount Carmel Jefferson Davis County (Source: Jefferson Davis County) By Tim Doherty | July 26, 2020 at 9:04 PM CDT - Updated August 22 at 12:06 AM . Jeff Davis County Sheriff's Office and Jail Information In Georgia. Jefferson Davis County is a county located in the U.S. state of Mississippi.As of the 2010 census, the population was 12,487. Jefferson Davis Heritage Trail SM. Jeff Davis County vital records can be found at the Georgia Department of Health which has copies for copies of birth and death records since Jan 1919, copies of marriage since June 9, 1952 to 1996 and a Divorce records index since June 9, 1952. km). The last days of the Civil War treasure traveled by wagon to Georgia Confederate... Mississippi.As of the 2010 Census, the 1st Wisconsin Cavalry [ Federal ], Lt. Henry! By wagon to Georgia with Confederate President Jefferson Davis.The County was carved out of Covington Lawrence. Has a total area of 335 square miles ( 870 sq landwatch has hundreds of farms, and!: Jefferson Davis County Birth, marriage, Divorce and Death Records per month is. 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