1. Introduction
In recent years, the market size of urban railway maintenance (damage assessment, diagnosis, repair, and reinforcement) expanded. In South Korea, related laws (enforcement ordinances) were implemented for the systematic inspection, diagnosis, and performance evaluation of track facilities, and diagnoses and performance evaluations were performed on a regular basis. However, few studies were conducted on methods that evaluate elastic materials, which are key components of concrete tracks. For the elastic-pad performance evaluation based on current standards, on-site measurement is performed for trains in operation or the static spring stiffness is measured in a laboratory by collecting samples from the field. On-site measurement is a technique used to estimate the spring stiffness of elastic materials by measuring the dynamic wheel load and vertical displacement of the rail during train passage and calculating the track support stiffness. In the laboratory tests used for on-site samples, a static spring stiffness test is conducted in a laboratory on elastic materials collected from tracks in operation through nighttime construction. However, this method cannot fully reflect actual field conditions, and nighttime track construction is required for sampling [
1,
2].
Most domestic urban railways comprise concrete tracks, and the track support stiffness that directly affects the performance of concrete tracks is determined by the spring stiffness of the elastic pads. Therefore, deteriorated elastic materials may increase shock and vibration, compromise riding comfort, cause complaints, and deteriorate other track components, including rails. Elastic materials may lose their functions over time owing to deterioration; thus, maintenance, such as replacement, must be performed in a timely manner through status assessment [
1,
2].
Figure 1 shows the conventional evaluation techniques that are proposed in this study [
1,
2].
Thus far, track performance was mostly evaluated using the techniques shown in
Figure 1a,b. For on-site measurement on tracks in operation, wheel load and lateral pressure sensors as well as displacement meters are installed at the site during the nighttime power-outage period, as shown in
Figure 1a. In addition, track performance must be evaluated under the actual train load during the daytime train-operation period [
1,
2]. In the laboratory test conducted on samples, a vertical stiffness test was conducted on a laboratory scale by collecting elastic pads from the site, as shown in
Figure 1b. For the test, collecting elastic pads at the site by dismantling rail fastenings and lifting sleepers is necessary. In addition, the spring stiffness of the elastic pads must be evaluated through a vertical stiffness test on the collected elastic pads.
Figure 1c shows the recently developed test equipment used to evaluate the performance of concrete tracks. The TSS equipment can apply a train load and evaluate track performance by calculating the spring stiffness of elastic pads immediately after measurement [
1]. However, the performance-evaluation techniques shown in
Figure 1a–c cannot directly evaluate resilience pads, which are the main evaluation target. That is, they indirectly evaluate the track support stiffness through the vertical displacement of the rail under the train load and predict the spring stiffness of elastic pads by calculating the rail support spring stiffness [
1,
2].
Li et al. used Monte Carlo simulations (MCS), which are based on statistical analyses, to analyze the correlation between the deformation behavior and the shape and size of aggregates. The analyzed results showed that aggregate behavior was affected significantly by the elongation index (EI) distribution, and was less affected by the excitation frequency and flatness index (FI) distribution [
3]. Fu et al. conducted an analysis by setting the support conditions at the bottom of the sleeper according to the surrounding environment (gravel loss due to flooding, etc.) as a variable to evaluate safety in the case in which a partial concrete sleep replacement method was applied for a gravel track laid with wooden sleepers. In addition, they proposed a technique to predict the sleep support conditions using an acceleration sensor installed on the rail and then, determined the train speed limit [
4]. Navaratnarajah et al. analyzed the sleep-gravel track interface for the shear behavior of a gravel track when there are various types of sleepers. The analyzed results showed that both commercial and recycled USP significantly improved the shear resistance at the contact surface between sleeper and gravel track, and that particle decomposition decreased compared with the contact surface between the concrete and wooden sleeper [
5]. Previous studies mostly used a strain gauge to measure the wheel load, and LVDT were used to measure the vertical displacements of the rail and sleeper. Spring stiffness at the rail support point was estimated based on the measured results.
However, in this study, a pressure sensor was directly installed on the elastic pad to evaluate the pressure under the train load, as shown in
Figure 1d. Pressure was used to determine the reaction force at the rail support point, and the changes in spring stiffness at the rail support point could be found. The change in elastic pad spring stiffness is a factor that directly affects the track support performance and is highly correlated with pressure. Therefore, this study presents a system that can evaluate this change.
The booted sleeper track system (STEDEF) was selected as the research target among various types of concrete tracks. The STEDEF is a track system that is embedded in the roadbed. It is a structure in which concrete sleepers are separated from the concrete bed, and highly elastic resilience pads are installed inside the rubber boots. This structure minimizes the transmission of the vibration and shock caused by the train load to the track structure [
6].
This study proposes a technique to evaluate the change in the spring stiffness of resilience pads by measuring the pressure generated at the bottom of a concrete sleeper using a pressure measurement sensor, which is defined as a pressure sensor.
Figure 2 shows the pressure sensor installation location.
In this study, a pressure sensor was installed between the resilience pad and rubber boots of the STEDEF to measure the generated pressure, as shown in
Figure 2. The existing track-performance evaluation method was performed based on the change in track support stiffness, which is an indicator of support performance under the application of the train load and a factor that can directly affect tracks and vehicles. It is also a major factor that affects the static and dynamic behavior of tracks and is important for evaluation [
2,
6].
The analysis of previous studies revealed that the STEDEF is a structure that involves changes in track support stiffness and pressure at the bottom of the sleeper depending on the uplift and settlement. A decrease in track support stiffness can be evaluated under the settlement condition; thus, the pressure at the bottom of the sleeper may decrease. Under the uplift condition, the pressure may increase owing to an increase in track support stiffness, which can be evaluated. Therefore, uplift and settlement sections can be evaluated using the change in pressure at the bottom of the sleeper as well as the increase/decrease in track support stiffness.
Track support stiffness can be evaluated through the theoretical equations of Zimmermann [
2]. The equations used for calculating the deflection of rails, which are continuously supported infinite beams, and rail support spring stiffness are as follows [
2]:
where
EI is the bending stiffness of the rail,
Q is the vertical force,
w(
x) is the rail displacement at position
x,
M(
x) is the rail moment at position
x, and
F(
x) is the pressure load on the sleeper at position
x. The characteristics length
L is as follows [
2]:
where
L is the characteristic length of track,
kc is the track support stiffness,
ks is the rail support spring stiffness, and
is the sleeper distance. The equation used to calculate the
ks value of the STEDEF is given by Equation (5) [
2].
Figure 3 shows the spring model [
6].
where
k1 is the spring stiffness of the rail pad,
k2 is the stiffness of the resilience pad, and
k3 is the spring stiffness of the rubber boots [
6]. Rail support spring stiffness is directly affected by the spring stiffness of the track components. Thus, an increase in the spring stiffness of the elastic pad may decrease the rail characteristic length or increase the reaction force at the rail support point or the pressure transmitted to the bottom of the sleeper. However, measuring the pressure at the bottom of the sleeper is difficult for track types, such as the STEDEF. Thus far, track support stiffness and rail support spring stiffness were evaluated for the STEDEF using the wheel load and the vertical displacement of rail converted through strain gauges and low vibration displacement transducers (LVDTs). The spring stiffness of elastic pads is calculated by installing strain sensors, such as LVDT, at the site, and by calculating track support stiffness using the measured wheel load and displacement. The calculated track support stiffness was used to compute the spring stiffness of the rail support point. The spring stiffnesses of the elastic pads were also calculated.
In this study, a pressure sensor was installed at the bottom of the resilience pad, which directly affects the rail support spring stiffness, to measure the pressure according to the load. In addition, the validity of the pressure sensor was verified through laboratory tests and numerical analysis. Based on the research results, a technique is proposed to evaluate the spring stiffness of resilience pads via a pressure measurement at the bottom of the sleeper.
2. Materials and Methods
For the pressure sensor used in this study, the magnitude of its electrical resistance changes depending on the applied force or pressure. The physical definition of pressure is the load divided by the cross-sectional area, and it changes depending on the area under the same load. Pressure sensors are generally thin, with a thickness of less than 0.5 mm, light, and strong against impact. They are typically used to measure the pressure distribution on robot hands, human gloves, solid surfaces, and medical appliances [
7,
8,
9,
10,
11,
12]. In this study, a pressure sensor was applied to the railway (track) field.
Figure 4 shows the measurement principle of the pressure sensor that was used.
In the pressure sensor, conductive particles move closer to each other inside the polymer when pressure (load) is applied from the outside. When a load is applied on the pressure sensor, the resistance value between the particles is produced as an analog signal. This signal needs to be converted into a digital signal to derive the applied load value from the measurement system. For the pressure-sensor saturation criterion applied in this study, the maximum pressure measurement was set when the signal value through an analog-to-digital converter (ADC) reached a maximum of 255 (8 bits). Because the pressure sensor measures the load based on the resistance value between the particles, a resistance-value deviation may exist owing to fabrication errors. After the pressure sensor was fabricated, it was calibrated by measuring the ADC signal according to the applied load set by the user.
Figure 5 shows the signal measurement test according to the set load.
As shown in
Figure 5, the ADC signal value was measured for each load set by the user.
Figure 6 shows an example of measurement results.
For the pressure sensor, digital signal values were measured according to the load (pressure) set by the user, as shown in
Figure 6. Using the pressure-sensor calibration, the magnitude of the load was measured when a load was applied, as shown in
Figure 6.
Figure 7 and
Table 1 show the specifications of the pressure sensor used in this study and an example of visualization software for the measurement results.
This study examined the validity of the pressure sensor. The maximum pressure detected, accuracy, measurement speed, and measurement area were verified as validity criteria. The maximum pressure detected was determined by the saturation of the ADC value owing to the pressure increase. The ADC produced a measurement of 187 under a pressure of 4.09 MPa, 196 under 5.45 MPa, and 204 under 6.81 MPa. Thus, the observations proved that the pressure sensor could measure more than 6.81 MPa.
The pressure-sensor accuracy was determined using the error of the values measured when the set pressure was applied two or more times. When 1.36 MPa was applied and re-applied, the measurement was 1.38 MPa, and the error rate was observed to be less than 1.5%. For the measurement speed, measurements were performed under the application of pressure, and the sampling rate was then determined from the number of datapoints recorded per second. The pressure sensor used in this study had a total of 48 × 48 (2304) nodes, and it was observed that 32 datapoints were recorded per second per node. Therefore, the sampling rate was determined to be 73,728 (=2304 × 32) Hz.
To determine the measurement area of the pressure sensor, the length and width of the sensing area of the sample were measured to be 428 × 428 mm. Therefore, the error from the sensing area considered for the design was regarded as insignificant.
As shown in
Figure 6c, the system applied in this study can display the measurement results of the pressure sensor in two-dimensional (2D) and three-dimensional (3D) graphs. The performance of the system was verified at a laboratory scale before its application to an actual STEDEF site for the development of a technique for evaluating the aging (deterioration) of resilience pads and changes in track boundary conditions by measuring the pressure at the bottom of the sleeper via a pressure sensor.