Research on the International Roughness Index Threshold of Road Rehabilitation in Metropolitan Areas: A Case Study in Taipei City
, Chih-Hsien Lin 1, Chao-Wei Tang 2,3,4,*, Liang-Pin Chu 5 and Chiu-Kuei Cheng 6
Edmon Perkins
Round 1
Reviewer 1 Report
This paper presents the results of road roughness evaluation and analysis before and after road rehabilitation in the Taipei City. Thresholds are recommended for road rehabilitation based on the results of statistical analysis. The work presented in this paper can be instructive for the road roughness evaluation and road maintenance in similar cities or areas. Major revisions are still required before being accepted by this journal.
- In figure 1, different explanations are given to some symbols, which can be quite confusing. For example, m1 is body mass or sprung mass?
- What does the speeds in the left part of Figure 2 mean. The authors need to explain all the information presented in figures, even for some borrowed from other publications.
- Is Figure 6 for the second phrase?
- Why not put the curve in Figure 7 into Figure 4 for better comparison?
- You may want to cite the following papers in the introduction section.
- Zhang, Zhiming, et al. "Application of a machine learning method to evaluate road roughness from connected vehicles." Journal of Transportation Engineering, Part B: Pavements 144.4 (2018): 04018043.
- Bridgelall, Raj, et al. "Precision enhancement of pavement roughness localization with connected vehicles." Measurement Science and Technology 27.2 (2016): 025012.
- Abeygunawardhana, C., R. M. K. Sandamal, and H. R. Pasindu. "Identification of the Impact on Road Roughness on Speed Patterns for Different Roadway Segments." 2020 Moratuwa Engineering Research Conference (MERCon). IEEE, 2020.
Author Response
Response to Reviewer 1 Comments
This paper presents the results of road roughness evaluation and analysis before and after road rehabilitation in the Taipei City. Thresholds are recommended for road rehabilitation based on the results of statistical analysis. The work presented in this paper can be instructive for the road roughness evaluation and road maintenance in similar cities or areas. Major revisions are still required before being accepted by this journal.
Point 1: In figure 1, different explanations are given to some symbols, which can be quite confusing. For example, m1 is body mass or sprung mass?
Response: In the revised manuscript, "body mass" has been changed to "sprung mass", and "Axle Mass" is changed to "unsprung mass".
|
Unsprung Mass (m2) |
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Sprung Mass (m1) |
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Damper |
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Suspension Spring |
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Tire Spring |
|
® IRI |
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Measured Profile ® |
|
Computer Algorithm |
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x1 |
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x2 |
|
x0 |
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x0: surface evaluation x1: displacement of sprung mass x2: displacement of unsprung mass m1: sprung mass m2: unsprung mass k1: suspension spring rate k2: tire spring rate c1: suspension damping rate |
|
c1 |
|
k1 |
|
k2 |
|
Road |
Figure 1. Illustration of computer algorithm used to compute the IRI [9,14].
Point 2: What does the speeds in the left part of Figure 2 mean. The authors need to explain all the information presented in figures, even for some borrowed from other publications.
Response: The speeds on the left side of Figure 2 indicate "approximately normal safe operating speeds".
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Superhighways & Airport Runways |
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New Paved Roads |
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Old Paved Roads |
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Maintained Unpaved Roads |
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Rough Unpaved Roads |
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General Pavement Type or Condition |
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Approximate Normal Safe Operating Speeds Operating Speeds |
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50 km/h |
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60 km/h |
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80 km/h |
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100 km/h |
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> 100 km/h |
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Damaged Paved Roads |
Figure 2. Illustration of the IRI scale for different pavements [9].
Point 3: Is Figure 6 for the second phrase?
Response: Figure 6 is the histogram and cumulative curve of the IRI value after the first stage of the road smoothing project.
Point 4: Why not put the curve in Figure 7 into Figure 4 for better comparison?
Response: Figure 4 is the comparison of IRI values of 72 identical road sections in the first phase before and after road leveling. Figure 7 shows the IRI values of the other 101 road sections in the second phase after road leveling. Therefore, the IRI values are expressed separately.
Point 5: You may want to cite the following papers in the introduction section.
- Bridgelall, R.; Huang, Y.; Zhang, Z.; Deng, F. Precision enhancement of pavement roughness localization with connected vehicles. Measurement Science and Technology 2016, 27, 025012.
- Zhang, Z.; Sun, C.; Bridgelall, R.; Sun, M. Application of a machine learning method to evaluate road roughness from connected vehicles. Journal of Transportation Engineering Part B: Pavements 2018, 144(4): 04018043.
- Abeygunawardhana, C.; Sandamal, R.M.K.; Pasindu, H.R. Identification of the Impact on Road Roughness on Speed Patterns for Different Roadway Segments. 2020 Moratuwa Engineering Research Conference (MERCon). IEEE, 2020.
Response: In the introduction section of the revised manuscript, the above papers have been cited.
“With the rapid advancement of science and technology, the techniques and methods used to measure road roughness are constantly innovating. A variety of roughness measurement instruments have been developed internationally. Bridgelall et al. [32] evaluated the increase in accuracy that can be achieved by adding standard speed bumps or existing anomalies to standard locations to enhance conventional geofencing systems to establish reference inertial markers. Their research results indicated that transportation agencies will benefit from using connected vehicle methods to achieve a level of precision and accuracy comparable to existing laser-based inertial profilers. Zhang et al. [33] used machine learning technology to estimate the roughness category and roughness index from inertial sensors on at least two connected vehicles. Their research results showed that the classification and estimation accuracy exceed 90%. Abeygunawardhana et al. [34] investigated the influence of road roughness on the speed patterns of different road sections under different traffic levels. The IRI measured through a smartphone application was used as an indicator of road roughness. The influence of road roughness was studied at free-flow speed (85th percentile speed) and 50th percentile speed, which was determined using the speed distribution that occurred during a specific time interval. In addition, the above behaviours were analyzed separately for junctions, mid-block sections, and horizontal curves to represent different road conditions. Moreover, remote sensing provides another method for IRI assessment. Meyer et al. [35] studied the applicability of satellite radar remote sensing data, especially the high-resolution synthetic aperture radar (SAR) data acquired in the X-band, to the network-wide mapping of pavement roughness of roads in the United States. Their research showed the capacity of X-band SAR for road surface roughness mapping.”
Author Response File: 
Author Response.pdf
Reviewer 2 Report
This paper discusses using a road roughness measurement technique to compare different road sections in Taipei City. The paper is interesting, and the results are compelling. However, technical aspects of the methods are missing, which should be clarified before publication.
1) For the quarter-car model, why is m2 called the unsprung mass? It has a spring both below it and above it. The Little Book reference states that the “compliance in the tire help to isolate the body from high frequency road excitations.” This is only somewhat true, since the tire itself has natural frequencies. If the road surface and speed produce a frequency, the tire will actually amplify the vibrations from the road surface!
2) From equation 1, it seems that the accelerations are numerically integrated to approximate the IRI. Is this correct? As the road surface is typically stochastic, this means that band-limited noise is being integrated. Since noise is integrated, how is this integration performed? Popular integration schemes, such as ode45, give incorrect results for stochastic systems.
3) Additionally, the road surface likely has discontinuities. Many standard integrators, such as ode45, do not handle discontinuities well. As another option, the linear quarter-car model has an analytical solution, such as the one presented in: Tucker, Alex, and Edmon Perkins. "Asphaltophones: Modeling, analysis, and experiment." The Journal of the Acoustical Society of America 148, no. 1 (2020): 236-242.
4) Is the GPS receiver used to monitor the variations in the speed of the vehicle? Why do the road surface measurements need to be taken at a constant speed of the vehicle, if the speed of the vehicle is known?
5) The words in the legend of Fig. 10 are overlapping.
6) If the desire is to measure the road roughness, why is a quarter-car modeled used? The road roughness is directly measured using a laser displacement sensor.
7) How were the system parameters obtained for the quarter-car model (m1, m2, k1, k2, etc.)? These parameters could be chosen so that the same road at the same speed looks smooth for one car model and rough for another car model.
Author Response
Response to Reviewer 2 Comments
This paper discusses using a road roughness measurement technique to compare different road sections in Taipei City. The paper is interesting, and the results are compelling. However, technical aspects of the methods are missing, which should be clarified before publication.
Point 1: For the quarter-car model, why is m2 called the unsprung mass? It has a spring both below it and above it. The Little Book reference states that the “compliance in the tire help to isolate the body from high frequency road excitations.” This is only somewhat true, since the tire itself has natural frequencies. If the road surface and speed produce a frequency, the tire will actually amplify the vibrations from the road surface!
Response: For the quarter-car model, the unsprung mass is the mass of the suspension, wheels or tracks, and other components directly connected to it rather than supported by the suspension. The unsprung mass includes the mass of components such as the wheel axles, wheel bearings, wheel hubs, tires, and a portion of the weight of driveshafts, springs, shock absorbers, and suspension links. The suspension supporting the sprung mass and the compliance in the tire help to isolate the body from high frequency road excitation. When the vehicle is running, it bumps into the road, causing the tires to jump up and down. However, the suspension spring and suspension damping above the tires greatly slow down the pulsation of the sprung mass above it. At very low frequency, the body moves up and down exactly as does the ground. At a frequency of about 1 Hz, the body will resonate on the suspension, which magnifies the road input of a typical car by 1.5-3.0 times. At higher frequencies, the suspension absorbs road input, isolating the body from the road. The wheel resonates at a frequency of about 10-15 Hz and bounces back and forth with greater motion than the road surface provides. This reduces the isolation to some extent in this frequency range, but this is an inevitable phenomenon.
Point 2: From equation 1, it seems that the accelerations are numerically integrated to approximate the IRI. Is this correct? As the road surface is typically stochastic, this means that band-limited noise is being integrated. Since noise is integrated, how is this integration performed? Popular integration schemes, such as ode45, give incorrect results for stochastic systems.
Response: The analysis of a road profile falls into the category of signal processing. Also, the calculation of the profile from transducer signals is a form of signal processing. Electronic signals are filtered to remove unwanted “noise” and to extract information of interest. The concept of an electronic filter has been extended to mathematics in general, particularly when a series of numbers is processed by a computer. Signals are processed mainly for two reasons: 1) to improve the quality of measurement by eliminating unwanted “noise” from the data, and 2) to extract information of interest from the signal. In order to make practical use of a profile measurement, it is almost mandatory to filter the sequence of numbers that makes up the profile. Filtering is particularly important when viewing data from high-speed inertial profilers. This is because the most visible features of the unfiltered measurement—the underlying grade and overall curvature—are the least accurate parts of the data. Usually, every inertial profiler has at least one filter built into it. Filtering is used to convert the data originating from the accelerometer and the height sensor into the same units. Additional filtering is added to prevent electronic “noise” from causing a large drift in the calculated profile. Several standard filters exist, and some of them are routinely applied to road profiles. So, equation (1) is correct. A general representation of a two-degree-of freedom quarter-car model is shown in Figure 1. In this model, the sprung and unsprung masses that correspond to one corner of the vehicle are denoted by ?1 and ?2, respectively. The suspension system is represented by a linear spring of stiffness k1 and a linear damper with a damping rate c1, while the tire is modelled by a linear spring of stiffness k1. x0 is the input. The vertical acceleration can be measured using accelerometers mounted on the body and front axle (or rear axle) of the vehicle. A transducer converts the strain of accelerometers into the electrical signal. By drawing free body diagrams and applying Newton’s Second law, we obtain the following differential equations [9,14]:
Using the response of the quarter-car model at a travel speed of 80 km/h, calculated for each point along the distance of travel, the IRI can be defined as follows [9,14]:
Here, IRI = International Roughness Index (m/km); L = length of the section (km); x = longitudinal distance (m); v = speed of the quarter car model (m/s); x/v = time the model takes to run a certain distance x; dt = time increment; = the time derivative of vertical displacement of the sprung mass; = the time derivative of vertical displacement of the unsprung mass.
|
Unsprung Mass (m2) |
|
Sprung Mass (m1) |
|
Damper |
|
Suspension Spring |
|
Tire Spring |
|
® IRI |
|
Measured Profile ® |
|
Computer Algorithm |
|
x1 |
|
x2 |
|
x0 |
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x0: surface evaluation x1: displacement of sprung mass x2: displacement of unsprung mass m1: sprung mass m2: unsprung mass k1: suspension spring rate k2: tire spring rate c1: suspension damping rate |
|
c1 |
|
k1 |
|
k2 |
|
Road |
Figure 1. Illustration of computer algorithm used to compute the IRI [9,14].
Point 3: Additionally, the road surface likely has discontinuities. Many standard integrators, such as ode45, do not handle discontinuities well. As another option, the linear quarter-car model has an analytical solution, such as the one presented in: Tucker, Alex, and Edmon Perkins. "Asphaltophones: Modeling, analysis, and experiment." The Journal of the Acoustical Society of America 148, no. 1 (2020): 236-242.
Response: The authors thank the reviewer for providing feasible analytical solutions. IRI is data obtained by measurement, not data obtained by processing theoretical ordinary differential formulas. Instead, it records the average value of the cumulative vertical displacement of a road bounce every 250 mm, and divides it by the total distance passed. Therefore, there is no requirement for continuity of pavement data in the theoretical formula. A profilometer is an instrument that measures a surface profile to determine its roughness. At present, acceleration inertia compensation laser profilometers measurement method is a relatively common method. Because of its efficiency and accuracy, it has become the mainstream method of flatness detection. This method uses a high-precision laser rangefinder to measure the distance between the vehicle body and the road surface, and then use the accelerometer's quadratic integral value to compensate the laser ranging value to obtain the relative elevation data of the road longitudinal section to calculate the road flatness index. In view of the error of the accelerometer itself and the complicated road detection environment, we can use the relationship between the instantaneous speed data and the sequence of displacement data obtained during the acceleration data integration calculation process, combined with the characteristics of wavelet transform multi-resolution and layer-by-layer decomposition, so as to accurately obtain the road surface roughness index.
Point 4: Is the GPS receiver used to monitor the variations in the speed of the vehicle? Why do the road surface measurements need to be taken at a constant speed of the vehicle, if the speed of the vehicle is known?
Response: The GPS sensor can be placed in any corner of the front panel of the vehicle to obtain the measurement position. In addition, the detected road section information can be graphically displayed for subsequent review. The GPS coordinate information received by the system will generate a KML file of the detected path after the detection operation is completed, which can be applied to the GOOGLE MAP by the operator. On any road, the level of roughness to which a vehicle is exposed depends on the travel speed. The perceived roughness generally increases with speed. This arises from the fact that the forces and accelerations imposed on a wheel by a bump increase with the speed at which it must “follow” the bump. Therefore, roughness to the road user is not a constant, but may be judged differently on low- and high-speed roads. However, to the highway community roughness is a geometric property of the road. The geometry is constant; therefore, a road should have a single roughness value. To accommodate the differences in these viewpoints, the IRI is based on a quarter-car response at 50 mph (80 km/h). A fixed speed for evaluating IRI ignores the fact that the prevailing travel speed varies with different types of roads. Thus, the choice of a fixed speed is a compromise between the needs of the highway engineer and the realities of the physics governing vehicle behaviour. However, it should be recognized that the compromise is not unique to the IRI. Any geometrically based measurement of roughness—specifically measures that depend on a particular band of wavelengths—do exactly the same thing. The choice of all geometrically based measures has been driven by the goal of evaluating a quantity that is closely correlated with vehicle response. Thus, the band of wavelengths selected is implicitly linked to an assumed vehicle travel speed, although that bias is usually unrecognized. The only difference is that the IRI explicitly reflects a chosen speed.
Point 5: The words in the legend of Fig. 10 are overlapping.
Response: The size of words in the legend of Fig. 10 has been adjusted.
Figure 10. Cumulative percentage curve of IRI values before and after the Road Smoothing Project.
Point 6: If the desire is to measure the road roughness, why is a quarter-car modelled used? The road roughness is directly measured using a laser displacement sensor.
Response:
IRI has become a recognized road roughness measurement standard. The main advantage of IRI is that it can remain stable over time and can be transmitted globally. However, the measurement of roughness is very difficult and complicated, because in addition to the actual road surface, it also depends on the characteristics of the vehicle. Moreover, the level of road roughness is easily affected by the structure of the vehicle and driving speed. Fortunately, whether the roughness is viewed as deviations in elevation (displacement inputs), slope (velocity inputs), or change of slope (acceleration inputs) the quarter car responds in a defined manner. In other words, the response can be mathematically described with a relatively simple set of dynamic equations known as a quarter-car simulation. The main function of the laser displacement sensor is to receive the vertical relative distance between the vehicle and the road. But this result needs to be corrected. The vertical displacement change of the vehicle can be obtained through quadratic integration of acceleration, and then the distance between the vehicle and the pavement measured by the laser displacement sensor is added or subtracted to obtain the longitudinal profile elevation data of the pavement.
Point 7: How were the system parameters obtained for the quarter-car model (m1, m2, k1, k2, etc.)? These parameters could be chosen so that the same road at the same speed looks smooth for one car model and rough for another car model.
Response: The system parameters of the quarter car model are provided by the car manufacturer. When the profiler moves along the pavement, the onboard accelerometer provides the computer data necessary to calculate the change in the vertical position of the vehicle body. The laser measures the distance between the vehicle body and the road surface. All of this information is regularly stored in the computer. After a run is completed or in real time, the information is summarized using a roughness statistic. The inputs from the accelerometer and laser sensor are fed to the onboard computer, which calculates and stores the pavement profile. In order to obtain high-quality roughness test data, the accuracy of the equipment must be ensured. Basically, the equipment should meet the requirements of ASTM E950 Class I for use on highway pavement. Although the system parameters of the test vehicle used are different, as long as the test vehicle meets the requirements of ASTM E950, the error of the test results is within an acceptable range. In other words, with a series of meticulous verification procedures, the accuracy and reproducibility of the detection system can be ensured. In fact, the system parameters of the quarter car model do not need to be measured. It only needs to use the laser sensor to measure the vertical height and the acceleration of the up and down vibration of the sensor, and then correct the real height of the road. The system parameters of the quarter car model will affect the acceleration of the vehicle up and down vibration, resulting in different vehicles getting different vertical distances of the laser sensor and different accelerations. But the modified road surface will have the same height at the end.
Author Response File: Author Response.pdf
Reviewer 3 Report
The paper describes the main results of a project regarding the threshold values of acceptance for the International Roughness Index in the Taipei City, Taiwan. This an interesting research topic with impact on road infrastructures management.
The paper should point out the novelty of the paper objectives. The paper focus on urban areas. The introduction of the paper should be improved through a literature review in this sense. It seems that the paper is a report of the project of Taipei City.
The structure of the paper is not the most adequate. The paper should have a section of methodology and it should be clear that Taipei City is a single case study of demonstration to achieve the main objectives of the paper. Sections fo results and discussion should also be considered.
Author Response
Response to Reviewer 3 Comments
Point 1: The paper describes the main results of a project regarding the threshold values of acceptance for the International Roughness Index in the Taipei City, Taiwan. This an interesting research topic with impact on road infrastructures management.
Response: The authors thank the reviewer for affirming this paper.
Point 2: The paper should point out the novelty of the paper objectives. The paper focus on urban areas. The introduction of the paper should be improved through a literature review in this sense. It seems that the paper is a report of the project of Taipei City.
Response: Based on the reviewer’s suggestion, the revised manuscript has revised the contents of the “Introduction” section.
“Cities are areas with a high density of living environments and active economic activities. Due to human communication and economic activities, urban roads are usually planned as interconnected networks. Basically, urban roads are composed of roadways, sidewalks, green belts, drainage systems, transportation facilities, intersections and auxiliary facilities. Generally speaking, urban road design needs to meet the traffic characteristics and traffic needs of its service objects (people, goods, cars, etc.). The pros and cons of road service performance are often reflected in the quantity, quality, and form. In other words, the scale of road construction should be sufficient, the road structure can ensure safe driving, the road network layout and road alignment should be reasonable, and the auxiliary facilities and management standards should be matched. Therefore, providing complete roads and good service performance is not only one of the important functions of modern cities, but also the foundation of sustainable urban development.”
“Urban roads can be divided into express roads, main roads, secondary roads, and branch roads. Express roads and main roads are the backbones of urban traffic, ensuring the connectivity of various areas in the city, while secondary roads and branch roads function as auxiliary main roads to ensure the accessibility of the regional road network. Urban roads are the facilities that most directly affect the experience of pedestrians, the quality of driving, and the safety of driving. They must have the characteristics of stability, comfort, and safety, and provide the service quality that satisfies road users under moderate maintenance. Therefore, how to maintain road quality with limited maintenance funds has become an important issue for relevant authorities. If a systematic method can be used to manage the pavement, the limited engineering funds will be able to maximize the benefits and maintain the service level of the overall road network.”
“Taipei City is located in the Taipei Basin in northern Taiwan. It is the central city of the Taipei Metropolitan Area and northern Taiwan, and it is also an international city. For the sustainable development of the city, the Taipei City government has conducted the Road Smoothing Project since 1998 to improve the quality of roads under its jurisdiction. In addition to the smoothness test of the original asphalt concrete pavement before it is demolished, after the new asphalt concrete pavement of the road is completed, the smoothness test is conducted in conjunction with the supervision department and the construction manufacturer. However, compared with the U.S. states that currently use IRI values to assess road conditions and control the construction quality of individual highway projects, the threshold set by Taiwan's current laws and regulations have room for adjustment. Furthermore, it has been mentioned in many previous documents that a higher initial roughness not only shortens the service life of the pavement but also increases the number of rehabilitations [11]. In view of this, this study aims to detect the road surface smoothness in Taipei City and examine the threshold values of acceptance for pavement surface characteristics to clarify whether there is room for adjustment of the current regulations.”
Point 3: The structure of the paper is not the most adequate. The paper should have a section of methodology and it should be clear that Taipei City is a single case study of demonstration to achieve the main objectives of the paper. Sections for results and discussion should also be considered.
Response: Based on the reviewer’s suggestion, the revised manuscript has detailed the theoretical background of IRI calculation in the “Introduction” section. In the "Analysis and Discussion of Actual Measurement Results" section, the existing IRI regulations in Taiwan have been compared with the test results.
“The IRI value was proposed by a study promoted by the World Bank in the 1980s [8]. It is calculated by using the dynamic response of vehicles to calculate the road profile, and then simulated by the so-called quarter car system. The quarter car simulation model consists of two parts: a sprung mass and an unsprung mass, as shown in Figure 1 [9,14]. The former represents the vehicle body, while the latter represents the set of wheel/tire and half axle/suspension. The sprung mass is connected to the unsprung mass through the suspension, and the suspension is simulated by a damper and a spring. In this model, the sprung and unsprung masses that correspond to one corner of the vehicle are represented by ?1 and ?2, respectively. The suspension system is denoted by a linear spring with a stiffness of k1 and a linear damper with a damping rate of c1, while the tire is modelled by a linear spring with a stiffness of k1. x0 is the input. In the simulation process, the quarter car system drives on the longitudinal profile of the test road. The profile is measured in the field at a constant speed of 80 km/h. The roughness on this surface causes dynamic excitation of the quarter car system, resulting in different vertical speeds ( and ) or accelerations ( and ) in the sprung and unsprung masses. The result is a relative movement between the chassis and the wheel-axle of the imaginary vehicle. The vertical acceleration can be measured using accelerometers mounted on the body and front axle (or rear axle) of the vehicle. The transducer converts the strain of accelerometers into an electrical signal. By drawing free body diagrams and applying Newton’s Second Law, we get the following differential equations [9,14]:
|
(1) |
|
|
(2) |
The vertical displacement change of the vehicle can be obtained through quadratic integration, and then the distance between the vehicle and the pavement measured by the displacement sensor is added or subtracted to obtain the longitudinal profile elevation data of the pavement. Therefore, the IRI value for a given road section length can be calculated according to Equation (3) [9,14].
|
(3) |
Here, IRI = International Roughness Index (m/km); L = length of the section (km); x = longitudinal distance (m); v = speed of the quarter car model (m/s); x/v = time the model takes to run a certain distance x; dt = time increment; = the time derivative of vertical displacement of the sprung mass; = the time derivative of vertical displacement of the unsprung mass.”
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Unsprung Mass (m2) |
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Sprung Mass (m1) |
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Damper |
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Suspension Spring |
|
Tire Spring |
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® IRI |
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Measured Profile ® |
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Computer Algorithm |
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x1 |
|
x2 |
|
x0 |
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x0: surface evaluation x1: displacement of sprung mass x2: displacement of unsprung mass m1: sprung mass m2: unsprung mass k1: suspension spring rate k2: tire spring rate c1: suspension damping rate |
|
c1 |
|
k1 |
|
k2 |
|
Road |
Figure 1. Illustration of computer algorithm used to compute the IRI [9,14].
Author Response File: Author Response.pdf
Reviewer 4 Report
I think the Introduction section should be divided into two parts: actual introduction, which generally introduces the considered issues and (from line 62) a section on the state of art. Please extend the first part – add a few more literature items, especially some newest. Please justify your topic to be published in Sustainability as well.
Figure 1. It seems to me that a damper c2 should be added – a tire acts also as a damper.
Table 2. Could you add the value of standard deviation as well?
Figure 4. You must not link the points. A continuous line suggest there are some values in between, for instance one could expect the value for x = 7.3 (what is obviously a nonsense).
I’m wondering whether diagrams 5 and 6 should be merged – it may better visualize the change. Please consider this.
Figure 10. Please rearrange the legend.
Table 6. There are two strange entries: #1 and #3 – IRI values increased after leveling. Please comment this.
Author Response
Response to Reviewer 4 Comments
Point 1: I think the Introduction section should be divided into two parts: actual introduction, which generally introduces the considered issues and (from line 62) a section on the state of art. Please extend the first part – add a few more literature items, especially some newest. Please justify your topic to be published in Sustainability as well.
Response: Based on the reviewer’s suggestions, the "Introduction" section has been rewritten.
“Cities are areas with a high density of living environments and active economic activities. Due to human communication and economic activities, urban roads are usually planned as interconnected networks. Basically, urban roads are composed of roadways, sidewalks, green belts, drainage systems, transportation facilities, intersections and auxiliary facilities. Generally speaking, urban road design needs to meet the traffic characteristics and traffic needs of its service objects (people, goods, cars, etc.). The pros and cons of road service performance are often reflected in the quantity, quality, and form. In other words, the scale of road construction should be sufficient, the road structure can ensure safe driving, the road network layout and road alignment should be reasonable, and the auxiliary facilities and management standards should be matched. Therefore, providing complete roads and good service performance is not only one of the important functions of modern cities, but also the foundation of sustainable urban development.”
“With the rapid advancement of science and technology, the techniques and methods used to measure road roughness are constantly innovating. A variety of roughness measurement instruments have been developed internationally. Bridgelall et al. [32] evaluated the increase in accuracy that can be achieved by adding standard speed bumps or existing anomalies to standard locations to enhance conventional geofencing systems to establish reference inertial markers. Their research results indicated that transportation agencies will benefit from using connected vehicle methods to achieve a level of precision and accuracy comparable to existing laser-based inertial profilers. Zhang et al. [33] used machine learning technology to estimate the roughness category and roughness index from inertial sensors on at least two connected vehicles. Their research results showed that the classification and estimation accuracy exceed 90%. Abeygunawardhana et al. [34] investigated the influence of road roughness on the speed patterns of different road sections under different traffic levels. The IRI measured through a smartphone application was used as an indicator of road roughness. The influence of road roughness was studied at free-flow speed (85th percentile speed) and 50th percentile speed, which was determined using the speed distribution that occurred during a specific time interval. In addition, the above behaviours were analyzed separately for junctions, mid-block sections, and horizontal curves to represent different road conditions. Moreover, remote sensing provides another method for IRI assessment. Meyer et al. [35] studied the applicability of satellite radar remote sensing data, especially the high-resolution synthetic aperture radar (SAR) data acquired in the X-band, to the network-wide mapping of pavement roughness of roads in the United States. Their research showed the capacity of X-band SAR for road surface roughness mapping.”
“Urban roads can be divided into express roads, main roads, secondary roads, and branch roads. Express roads and main roads are the backbones of urban traffic, ensuring the connectivity of various areas in the city, while secondary roads and branch roads function as auxiliary main roads to ensure the accessibility of the regional road network. Urban roads are the facilities that most directly affect the experience of pedestrians, the quality of driving, and the safety of driving. They must have the characteristics of stability, comfort, and safety, and provide the service quality that satisfies road users under moderate maintenance. Therefore, how to maintain road quality with limited maintenance funds has become an important issue for relevant authorities. If a systematic method can be used to manage the pavement, the limited engineering funds will be able to maximize the benefits and maintain the service level of the overall road network.”
“Taipei City is located in the Taipei Basin in northern Taiwan. It is the central city of the Taipei Metropolitan Area and northern Taiwan, and it is also an international city. For the sustainable development of the city, the Taipei City government has conducted the Road Smoothing Project since 1998 to improve the quality of roads under its jurisdiction. In addition to the smoothness test of the original asphalt concrete pavement before it is demolished, after the new asphalt concrete pavement of the road is completed, the smoothness test is conducted in conjunction with the supervision department and the construction manufacturer. However, compared with the U.S. states that currently use IRI values to assess road conditions and control the construction quality of individual highway projects, the threshold set by Taiwan's current laws and regulations have room for adjustment. Furthermore, it has been mentioned in many previous documents that a higher initial roughness not only shortens the service life of the pavement but also increases the number of rehabilitations [11]. In view of this, this study aims to detect the road surface smoothness in Taipei City and examine the threshold values of acceptance for pavement surface characteristics to clarify whether there is room for adjustment of the current regulations.”
Point 2: Figure 1. It seems to me that a damper c2 should be added – a tire acts also as a damper.
Response: Figure 1 is a quote from Sayers and Karamihas' research. The study did not consider the damping effect of the tire, so the damper is not drawn in Figure 1. As the reviewer said, the damping effect of tires can be considered. But the equations of motion that control the quarter car will be given by different ordinary differential equations.
Point 3: Table 2. Could you add the value of standard deviation as well?
Response: The revised manuscript has included the standard deviation in Table 2
Table 2. Inspection record of the first phase of road smoothness.
|
Item |
Stake (m) |
Forward lane |
Reverse lane |
||
|
Car speed (km/h) |
IRI (m/km) |
Car speed (km/h) |
IRI (m/km) |
||
|
1 |
50 |
22.5 |
9.07 |
20.9 |
9.51 |
|
2 |
100 |
28.8 |
7.36 |
18.8 |
8.49 |
|
3 |
150 |
30.0 |
5.74 |
15.9 |
9.14 |
|
4 |
200 |
31.8 |
6.09 |
23.9 |
9.40 |
|
5 |
250 |
28.4 |
6.88 |
23.9 |
7.58 |
|
6 |
300 |
21.2 |
5.57 |
22.1 |
7.39 |
|
7 |
350 |
21.2 |
6.01 |
20.5 |
8.75 |
|
8 |
400 |
28.0 |
4.13 |
20.3 |
9.10 |
|
9 |
450 |
30.6 |
6.59 |
18.0 |
8.26 |
|
10 |
500 |
31.6 |
8.35 |
19.7 |
8.12 |
|
11 |
550 |
20.2 |
6.53 |
18.7 |
9.15 |
|
12 |
600 |
27.2 |
7.75 |
15.6 |
6.73 |
|
13 |
650 |
29.1 |
7.41 |
21.6 |
6.34 |
|
14 |
700 |
29.0 |
5.69 |
20.6 |
6.72 |
|
15 |
750 |
27.9 |
7.70 |
22.2 |
7.44 |
|
16 |
800 |
22.2 |
7.54 |
28.1 |
7.73 |
|
17 |
850 |
16.7 |
7.84 |
19.2 |
8.24 |
|
18 |
900 |
15.1 |
6.78 |
20.3 |
7.77 |
|
19 |
950 |
24.0 |
5.65 |
21.3 |
7.50 |
|
20 |
1000 |
25.9 |
6.20 |
22.9 |
7.74 |
|
21 |
1050 |
27.1 |
11.40 |
32.1 |
8.63 |
|
22 |
1100 |
24.5 |
5.43 |
31.8 |
8.37 |
|
23 |
1150 |
25.8 |
5.22 |
30.7 |
7.10 |
|
24 |
1200 |
28.0 |
3.82 |
29.3 |
7.55 |
|
25 |
1250 |
31.3 |
3.48 |
31.8 |
4.65 |
|
26 |
1300 |
24.4 |
3.06 |
32.3 |
4.59 |
|
27 |
1350 |
13.7 |
5.80 |
32.2 |
7.67 |
|
28 |
1400 |
25.1 |
6.20 |
33.4 |
5.12 |
|
29 |
1450 |
23.9 |
7.99 |
33.3 |
4.37 |
|
30 |
1500 |
35.5 |
8.85 |
31.2 |
10.53 |
|
31 |
1550 |
37.4 |
5.95 |
24.9 |
9.76 |
|
Average value |
26.1 |
6.52 |
24.4 |
7.72 |
|
|
Standard deviation |
- |
1.73 |
- |
1.52 |
|
|
Maximum value |
37.4 |
11.40 |
33.4 |
10.53 |
|
|
Minimum value |
13.7 |
3.06 |
15.6 |
4.37 |
|
Point 4: Figure 4. You must not link the points. A continuous line suggests there are some values in between, for instance one could expect the value for x = 7.3 (what is obviously a nonsense).
Response: Figure 4 shows the usual expressions used in related research. According to the reviewer’s suggestion, Figure 4 has been revised in the revised manuscript.
Figure 4. Comparison of IRI values before and after the first phase of the Road Smoothing Project.
Figure 7. IRI value after the second phase of the Road Smoothing Project.
Point 5: I’m wondering whether diagrams 5 and 6 should be merged – it may better visualize the change. Please consider this.
Response: Thanks to the reviewer’s suggestion, the cumulative curve of IRI values in Figure 5 and Figure 6 have been merged into Figure 10.
Point 6: Figure 10. Please rearrange the legend.
Response: The size of words in the legend of Figure 10 has been adjusted.
Figure 10. Cumulative percentage curve of IRI values before and after the Road Smoothing Project.
Point 7: Table 6. There are two strange entries: #1 and #3 – IRI values increased after levelling. Please comment this.
Response: Regarding #1-#3 road sections after levelling, the IRI value increased, indicating that there is still room for improvement in road maintenance. This is exactly the reason why the inspection and acceptance standards are set.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
The manuscript is properly revised and can be accepted.
Author Response
The authors thank the reviewer for his comments and approval of publication. The first draft of this article has undergone English language editing by MDPI.
Author Response File: Author Response.pdf
Reviewer 2 Report
The reviewer would like to thank the authors for their careful answers to the reviewer's previous points. However, the provided author response raises more serious concerns about this manuscript.
The m2 literally has a spring connected to it, so it should not be denoted as an unsprung mass. The wheel, as a continuous structure, has an infinite number of natural frequencies. It does not have one resonance near 10-15 Hz.
In the response to Point 2, the authors stated that they filters were used to remove "unwanted 'noise' and extract information of interest." In the described method, it is impossible for the authors to know the source of the noise. If the source of the noise is the road surface itself, they are changing the IRI by filtering the results. Additionally, the authors never stated how they performed integrations, even though it was asked directly. If the noise is removed, they are not using the road's surface to measure roughness; if the noise is not removed, ode45 cannot be used.
In the response to Point 3, the authors state that there is no requirement for continuity. However, most ODE solvers need continuity to converge to a solution. As mentioned before, discontinuities are a problem when trying to use ode45.
In the response to Point 7, the authors stated that the system parameters of the quarter-car model are provided by the manufacturer. This is not true. Tires are incredibly complex. Changing the pressure in the tire will significantly affect the effective spring stiffness of the tire. Moreover, changing the system parameters in the quarter car model can modify the IRI to be any desired value. If there are low frequency components in the road profile, a high tire stiffness can remove the effects of the road profile. If there are high frequency components in the road profile, a low tire stiffness can be used to remove the effects of the road surface. Thus, the IRI value is not a meaningful evaluation of road roughness.
Author Response
The authors thank the reviewer for his comments. The replies to the comments are attached. In addition, The first draft of this article has undergone English language editing by MDPI.
Author Response File: Author Response.pdf
Reviewer 4 Report
Since the Authors have addressed all my remarks I have no further comments
Author Response
The authors thank the reviewer for his comments and approval of publication. The first draft of this article has undergone English language editing by MDPI.
Author Response File: Author Response.pdf
Round 3
Reviewer 2 Report
Though some of the superficial problems with this paper were corrected, two large theoretical problems remain with this paper:
1) Road surfaces are stochastic, which is the reason for calculating a roughness metric. The authors do not use a stochastic integration routine, and so these results are simply wrong. In previous reviews, citations concerning this were given, but the authors ignored them.
2) From fundamental concepts of vibrations, this quarter car model is useless in determining a roughness metric for the road. The parameters in the quarter car model can be tuned to make the roughness profile arbitrarily high or arbitrarily low. It is misleading, since the roughness could be directly calculated by measuring the road surface itself.
In my opinion, it is unethical to report these findings, since the authors have been told that this information is incorrect on a theoretical level.
Author Response
Response to Reviewer 2 Comments
Though some of the superficial problems with this paper were corrected, two large theoretical problems remain with this paper:
Point 1: Road surfaces are stochastic, which is the reason for calculating a roughness metric. The authors do not use a stochastic integration routine, and so these results are simply wrong. In previous reviews, citations concerning this were given, but the authors ignored them.
Response: The authors respect the opinions of the reviewer. On the other hand, the authors agree with the research results of many researchers on IRI. Thank you very much for your valuable and constructive comments on our work, we really appreciate!
Point 2: From fundamental concepts of vibrations, this quarter car model is useless in determining a roughness metric for the road. The parameters in the quarter car model can be tuned to make the roughness profile arbitrarily high or arbitrarily low. It is misleading, since the roughness could be directly calculated by measuring the road surface itself. In my opinion, it is unethical to report these findings, since the authors have been told that this information is incorrect on a theoretical level.
Response: The authors respect the opinions of the reviewer. But it is undeniable that the IRI measurement system is already a commercially available device. Therefore, IRI has been widely used as an indicator of road roughness around the world. Thank you very much again for your valuable and constructive comments on our work, we really appreciate!
Author Response File: Author Response.pdf
