Road Surface Roughness Research Articles

An Application of Android Sensors and Google Earth in Pavement Maintenance Management Systems for Developing Countries

Governments and road agencies face the challenging task of maintaining roads. One of the reasons this is challenging is that the maintenance process requires utilizing a substantial amount of road network condition data. There are many approaches for measuring road surface conditions which are very costly and time-consuming, as well as requiring skillful operators. Developing countries have limited budgets for planning and monitoring road maintenance. This research aims to establish a low-cost pavement maintenance management system for intermediate and small cities in developing countries. The system utilizes low-cost sensors embedded in smartphones that can be used to measure road surface conditions. Google Earth is then used to present maintenance data, select a maintenance strategy, and view the maintenance output information. Road Lab Pro, an android application, is used to collect the data and estimate the surface condition of roads by using accelerometers, gyroscopes, and a GPS. The road network is divided into segments and the road surface conditions are estimated for each segment using the smartphone application and a suspension vehicle. The required maintenance activities for these segments are then established. A priority index is determined for each segment to decide which segments should be maintained with the available budget. This effort allows us to investigate the feasibility of assessing road surface roughness using a smartphone to determine the presence of road distresses and the overall road condition, which is taken into account when making maintenance decisions. The application of this system reveals that these new technologies can provide cost-effective, easy-handling, and efficient ways for a road agency to perform good maintenance planning.

Flexibility matrix identification using the moving vehicle induced responses for beam type bridge

Bridge flexibility matrix is widely used in damage detection and condition assessment. As the mass-normalized mode shapes with high spatial resolution are required in formulating the flexibility matrix, the difficult tasks of measuring excitation and arranging a large number of sensors are normally involved. This paper proposes a flexibility matrix identification method by using the moving vehicle induced responses for beam type bridges. Firstly, the proportional relationship between the modal flexibility matrix and the matrix formulated by the bridge influence lines (ILs) of several points is derived theoretically. Then the quasi-static component response of the moving vehicle induced responses is extracted through the analytical mode decomposition (AMD), and the IL of the measurement point is obtained via polynomial fitting accordingly. Finally the flexibility matrix is formulated based on the measured ILs and the derived proportional relationship, and employed to estimate the static displacement of the bridge. Numerical and experimental examples are conducted to verify the accuracy and feasibility of the proposed method. The results indicate that it can identify the bridge flexibility matrix and estimate the static displacement with high accuracy and efficiency at the cost of few sensors. Besides, the proposed method is insensitive to vehicle parameters (velocity, spring stiffness, wheelbase, and weight), road surface roughness and measurement noise.

Damage Detection of Continuous Beam Bridge Based on Maximum Successful Approximation Approach of Wavelet Coefficients of Vehicle Response

In view of problems such as closed traffic, the large number of sensors required, and the labor-intensive and time-consuming nature of previous bridge detection, this paper analyzes the dynamic response of the vehicle body of the continuous girder bridge under the action of vehicle load. Based on theoretical analysis and formula derivation, a new method of bridge damage detection based on coupled vehicle–bridge vibration is conceived. This method can accurately identify the location of bridge damage and approximately estimate the degree of bridge damage. The method is as follows: Taking the continuous beam bridge as an example, first, use the tractor inspection vehicle model to drive over the continuous beam bridge before and after the damage, and collect the acceleration response of the vehicle body. Then, the acceleration response difference is transformed by wavelet transform. Furthermore, perform the innovative use of the maximum successive approximation approach to process wavelet transform coefficients, which can identify the location of the bridge damage. Additionally, study the impact of vehicle speed, vehicle weight, road surface roughness, and noise on this damage detection method. In addition, a method for judging bridge damage degree based on wavelet transform coefficients is proposed, and the judgment error basically meets the requirements.

Novel identification technique of moving loads using the random response power spectral density and deep transfer learning

Accurate identification of vehicle loads plays a great significance in design, monitoring and evaluation of bridges. However, it is hard to acquire them via conventional methods because of their weak practicality or low modeling accuracy. To this end, this study proposes an innovative method to perform load identification based on the random response power spectral density (PSD) and deep transfer learning strategy. This method belongs to a data-driven model, which avoids the expensive facility requirement or ill-posed inverse problem in conventional methods. Specifically, a vehicle-bridge interaction dynamic system considering the disturbance of road surface excitation is firstly established to generate structural response for any specified vehicle loads. Then, the corresponding response PSD is transformed to a three-dimensional matrix in the form of color image, by which the deep transfer learning strategy is incorporated in deep convolutional neural network for describing the mapping relationship between structural response and these specified vehicle loads. On this basis, load identification can be performed via inputting any interested response PSD into the established relationship. Finally, the effectiveness of the proposed method is validated numerically. The results demonstrate that it can accurately identify the vehicle weight, vehicle speed and road surface roughness with an overall accuracy exceeding 98% and presents an excellent anti-noise capacity.

Reference-free damage identification method for highway continuous girder bridges based on long-gauge fibre Bragg grating strain sensors

Damage information is important for bridge condition assessment and maintenance. Extensive research has been conducted to extract this information from the responses of bridges (e.g., displacement and strain). However, among these methods, the responses of intact bridges are required for damage identification, which is difficult to acquire for most existing bridges. For such circumstances, a reference-free damage identification method for highway continuous girder bridges is presented in this paper, which takes advantage of long-gauge fibre Bragg grating strain sensors. A 1:10 indoor model experiment is conducted to verify the practicability of the method, which is effective under various damage and vehicle parameters. The extents of damage identification errors can reach 50% because of experimental errors. Furthermore, the number of bridge spans and road surface roughness are proven to have no effect on the method with a bridge-vehicle coupling system, and the extents of damage identification errors are below 10%.

Damage Detection of Bridge Structures Under Vehicle Loads Using Moving-Window Correlation Coefficient of Signals with Limited Sensors

Based on the moving-window correlation coefficient of signals, a damage detection method is proposed for bridge structures. The signals collected by two sensors during the vehicle crossing the bridge are intercepted as windowed pair time series, and the damage index is defined based on the correlation of them. The damage vector is composed of damage indices from the beginning to end according to the moving direction of the window, and the damage location can be determined by the peak information of all the damage vectors which are obtained by different sensor pairs. First, the damage detection method and corresponding detection steps are introduced. Then, the proposed method is validated by numerical simulation, and the influence of moving window length, moving step, the number of sensors, deviation of vehicle parameters and road-surface roughness on damage localization are discussed, respectively. Finally, the proposed method is validated by experiments using a two-axis vehicle and a steel–concrete composite beam. The results show that the proposed method can effectively identify the damage location. High-pass filtering of the raw data to remove trend items and wavelet noise reduction can significantly improve the accuracy of damage detection, and can accurately locate the damage in the presence of road-surface roughness. The damage detection result is not sensitive to vehicle parameter deviation, and the damage location can still be accurately identified when the vehicle weight or speed deviates 50% from the vehicle parameters in the reference data. In addition, the proposed method can make full use of the damage information between sensors, and can accurately identify the damage location with limited sensors. The number of sensors can be flexibly determined considering both accuracy and economy.

Assessment of Ride Quality and Road Roughness by Measuring the Response from a Vehicle Mounted Android Smartphone

Road surface roughness is considered as one of the most important aspect in functional condition of roads indicating to riding comfort in both transverse and longitudinal direction. For all highway projects maintaining proper surface evenness has been a mandatory requirement from the government of India. In this study, pavement unevenness was estimated using an android-based smartphone and system rely on the movement of a suspension system in response to the unevenness of the road surface travelled by a vehicle. Roughness index (RI) is an indicator to measure the standard road roughness. Conventional way of measuring RI involves usage of bump integrator, profilometer and roughometer. Present study proposes an alternative method to measure roughness using smartphones. The study was carried out in 2 different locations and 3 different smartphones to obtain the magnitude of deflections. Data collected using standard fifth wheel bump integrator is used for validation. Data collected through smartphone i.e., accelerometer sensor details was given as input to Fast Fourier transforms (FFT) to find the magnitude of deflection on the road. The results obtained from smartphones was then correlated with the existing roughness index data collected by standard fifth wheel bump integrator to establish an equation to predict RI using an android mobile.

Dynamic responses of a damaged double Euler-Bernoulli beam traversed by a 'phantom' vehicle.

SummaryIn this paper, the dynamic response of a damaged double‐beam system traversed by a moving load is studied, including passive control using multiple tuned mass dampers. The double‐beam system is composed of two homogeneous isotropic Euler–Bernoulli beams connected by a viscoelastic layer. The damaged upper beam is simulated using a double‐sided open crack replaced by an equivalent rotational spring between two beam segments, and the lower primary beam is subjected to a moving load. The load is represented by a moving Dirac delta function and by a quarter car model, respectively. Road surface roughness (RSR) is classified as per ISO 8606:1995(E). The effect of vehicle speed of the moving oscillator and variable RSR profiles on the dynamics of this damaged double Euler–Bernoulli beam system for different crack‐depth ratios (CDRs) at various crack locations is studied. It is observed that coupling of two beams leads to a vehicular effect on the damaged beam, even when no vehicle on it is present. The effects of single and multiple tuned mass dampers to control the vibrational responses of the primary beam due to damage on the secondary beam is studied next. The performance of tuned mass dampers to reduce the transverse vibrations of the damaged double‐beam system and of the quarter car is investigated. The paper links the coupling between the two levels of double beam with the inertial coupling of the vehicle to the double‐beam system.

Development of position calculation method using elevation data by barometric pressure sensor

The global navigation satellite system (GNSS) is used to help people get from A to B by determining the position of us and the vehicles we use for travel. However, there are issues surrounding the GNSS, including its accuracy. This is due to a variety of factors that can cause radio waves coming in from satellites to experience poor reception. In addition, cybercrime is a concern for the GNSS as with cybercriminals becoming more sophisticated, they could one day interfere with the satellite system, which would cause havoc. Dr Takayoshi Yokota, Graduate School of Engineering, Tottori University, Japan, is working to overcome these challenges by developing a method for estimating the position of a travelling vehicle based on altitude profile, driving behaviour, road surface roughness and changes in inclination. Yokata and his team are working to enhance the effectiveness of Micro Electro-Mechanics System (MEMS) sensors. This work involves developing a position calculation method using elevation data by barometric pressure sensor as altitude can be indirectly measured using pressure sensors and this is a convenient method as these sensors are embedded in most smartphones. In order to aid localisation, acceleration data and angular velocity data are required along with a pressure reading that can be used to calculate altitude. Yokota anticipates that drivers will benefit from the technology in due course.

Enhanced drive‐by bridge modal identification via dual Kalman filter and singular spectrum analysis

The drive-by bridge health monitoring is to assess the bridge condition using the acceleration responses measured on the body or axle of instrumented vehicles. The vehicle responses are greatly affected by the road surface roughness that makes the bridge dynamic information blurred. Instead of direct using vehicle responses for the bridge monitoring, the dynamic response of contact point (CP) between the vehicle and bridge is further explored to enhance the drive-by bridge modal identification. A novel three-step framework is proposed to extract the components related to the bridge vibration from vehicle responses. The first step is to identify the input forces of two successive vehicles by solving the combined state-input estimation problem using dual Kalman filter. The CP responses of two contact points are calculated using the input forces and vehicle parameters. In the second step, the subtraction technique is applied to the identified CP responses of the two instrumented vehicles and the effect of the road surface roughness can be significantly reduced. Finally, an automatic singular spectrum analysis technique (auto SSA) is incorporated to decompose the response residual. Then the mono-component modes related to the bridge response are extracted from the response residual for drive-by bridge modal identification and/or the nonstationary characteristic identification of vehicle–bridge interaction (VBI) system. Results of numerical and experimental study demonstrate that the method can significantly suppress the vehicle response component and reduce the effect of road surface roughness to enhance the bridge modal identification.

Nutrient quality characteristics of fresh tomatoes subjected to various transportation and disinfection treatments under commercial conditions in South African supply chains

ABSTRACT This study investigated effects of disinfection treatments and storage conditions on the nutrient quality characteristics of tomatoes harvested and transported under typical commercial conditions in South Africa. The experimental treatments included harvest of tomatoes in two seasons, three maturity stages, three transportation conditions (varying distances and road surface profiles), four disinfection treatments (control, hot water, chlorinated or anolyte water in combination with biocontrol) and two storage conditions (cold (11°C) and ambient). For fruit harvested in winter, treatment with hot water (also for summer harvest) or with anolyte water + biocontrol and storage in cold conditions resulted in the highest concentrations of ascorbic acid (AA) compared with the other treatments. Fruit transported along the shorter and relatively smoother roads maintained higher AA (summer harvest) and lycopene concentrations compared with fruit transported the longest distance with moderately rough surface profile. The harvesting season and the disinfection treatments had significant effects on the concentrations of sugars. The nutrient characteristics of the tomatoes in the supply chain were shown to be affected not only by environmental and postharvest practices, but also by the road quality, demonstrated for the first time in this study. Cold storage of tomatoes, disinfection with hot water or anolyte water + biocontrol, and transportation planning to minimise the use of rough road surfaces and to use the shortest distances to markets, were recommended as best practices for the industry. Options for improvement of fruit packaging to provide adequate cushioning against physical damage during transportation should be investigated.

Approaches for Road Surface Roughness Estimation Using Airborne Polarimetric SAR

The road surface roughness is an important parameter that determines the quality of a road network. It has a direct influence on the grip and skid resistance of the vehicles. For this reason, this parameter has to be periodically monitored to keep track of its changes. Nowadays, road surface roughness is measured by driving measurement vehicles equipped with laser scanners all over the country. But, this approach is very costly, labor-intensive, and time-consuming. This article is done to evaluate the potential of high-resolution airborne polarimetric synthetic aperture radar (SAR) to remotely estimate the road surface roughness on a wide scale. Different SAR backscatter-based semi-empirical models and SAR polarimetry-based models for surface roughness estimation are implemented in this article. Also, a new semi-empirical model is proposed in this article, which is trained specifically for the road surface roughness estimation. Additive noise subtraction, upper sigma nought threshold masking, and lower signal-to-noise ratio threshold masking techniques were implemented in this article to improve the reliability of road surface roughness estimation. The feasibility of this approach is tested using fully polarimetric X-band datasets acquired with DLR's airborne radar sensor F-SAR. The surface roughness results estimated using these airborne SAR datasets show good agreement with the ground truth surface roughness values and the results are discussed in this article.

Application of Multi-Input Uncorrelated Periodic Signals for Identification of Active Suspension System

Active suspension system has been widely used to improve ride comfort in passenger cars by adjusting the force between the tyre and the vehicle body such that the effect of the rough road surfaces will be minimally felt by the passengers. Control algorithms for active suspension are frequently designed using the well-known quarter-car model, which is a linear approximation of the system. In this paper, multi-input uncorrelated periodic signals are applied on a laboratory-scale active suspension system. The signals incorporate harmonic suppression allowing more accurate estimation of the frequency response functions and quantification of nonlinearities and noise. The results reveal significant amount of nonlinearities, which should be taken into account during controller design.

Semantic Segmentation on Smartphone Motion Sensor Data for Road Surface Monitoring

Improving road safety is one of the critical issues for road maintenance and management. Motion sensors embedded in smartphones to sense vibrations can be used to detect rough road surfaces when carried in moving vehicles. Finding segments in the signal which reflect the condition of the road surface, however, is a challenging task. This study proposes a modified U-Net architecture with integrated bidirectional Long Short-Term Memory layers to perform semantic segmentation on smartphone motion sensor data for road surface classification. Experiments show that using z-axis accelerometer and z-axis gyroscope features, the proposed method outperforms multiple existing semantic segmentation algorithms.

Subspace identification of bridge dynamics via traversing vehicle measurements

This study establishes a subspace identification scheme to estimate bridge frequencies by processing the dynamic response of a traversing vehicle for two traverses over the target bridge. Specifically, this study discusses the feasibility of the Multivariable Output Error State Space (MOESP) method for vehicles with high speed and/or bridges with short characteristic lengths. To deal with the time-varying nature of the vehicle–bridge system, the study employs the singular value decomposition (SVD) based pseudo-inverse algorithm. The proposed scheme is numerically verified on synthetic datasets, which are attained via simulation of the vehicle–bridge interaction (VBI) problem, in order to obtain the vehicle response, which serves for application of the MOESP algorithm. The numerical experiments concern both a sprung mass vehicle and a more realistic multi-degree-of-freedom (MDOF) vehicle identifying a 3-span bridge. The results demonstrate that the proposed approach succeeds in identifying structural frequencies of the bridge even under assumption of high vehicle speeds and high levels of road surface roughness.

Influence of the road surface roughness (according to the International Roughness Index) on road safety

The high rate of road accidents and the various injuries associated with them are one of the most ambitious problems from an economic and demographic point of view for most countries of the world. For the Russian Federation, this problem is particularly acute, it concerns all road users, both vehicle owners and pedestrians. The paper examines the activities of the state aimed at improving the level of road safety in Russia and reducing accidents on the roads. The impact of one of the most important operational indicators of the road – the roughness of the road surface, on the frequency of traffic accidents is being assessed. The paper establishes the nature of the dependence of the accident risk indicator on the value of the road surface roughness expressed by the International Roughness Index (IRI). The results of the study are verified by comparing the results of calculating the accident risk indicator according to the obtained equation and according to the known dependence.

Theoretical Study on Recognition of Icy Road Surface Condition by Low-Terahertz Frequencies

Recognition of road surface conditions should always be at the forefront of intelligent transportation systems for the enhancement of transportation safety and efficiency. When road surfaces are covered by ice or snow, accident rate would increase due to the reduction of road surface roughness and also friction between tire and road. High-resolution recognition of natural and manmade surfaces has been proved to be achievable by employing radars operating at low-terahertz frequencies. In this work, we present theoretical investigations on surface condition recognition of an icy road by employing low-terahertz frequencies. A theoretical model combining integral equation method (IEM), radiative transfer equation (RTE) and Rayleigh scattering theory is developed. Good agreement between the calculation results and measured data confirms the applicability of low-terahertz frequencies for the evaluation of icy road surface in winter. The influence of carrier frequency, ambient temperature, impurities inside the ice layer and frozen soil surface conditions on the efficiency of this method is presented and discussed.

Simultaneous Identification of Bridge Structural Damage and Moving Loads Using the Explicit Form of Newmark-β Method: Numerical and Experimental Studies

Bridge infrastructures are always subjected to degradation because of aging, their environment, and excess loading. Now it has become a worldwide concern that a large proportion of bridge infrastructures require significant maintenance. This compels the engineering community to develop a robust method for condition assessment of the bridge structures. Here, the simultaneous identification of moving loads and structural damage based on the explicit form of the Newmark-β method is proposed. Although there is an extensive attempt to identify moving loads with known structural parameters, or vice versa, their simultaneous identification considering the road roughness has not been studied enough. Furthermore, most of the existing time domain methods are developed for structures under non-moving loads and are commonly formulated by state-space method, thus suffering from the errors of discretization and sampling ratio. This research is believed to be among the few studies on condition assessment of bridge structures under moving vehicles considering factors such as sensor placement, sampling frequency, damage type, measurement noise, vehicle speed, and road surface roughness with numerical and experimental verifications. Results indicate that the method is able to detect damage with at least three sensors, and is not sensitive to sensors location, vehicle speed and road roughness level. Current limitations of the study as well as prospective research developments are discussed in the conclusion.

Fundamental mode shape estimation and element stiffness evaluation of girder bridges by using passing tractor-trailers

A comprehensive indirect method is proposed in this study to estimate the dynamic characteristics of the fundamental mode and evaluate the element stiffness of a girder bridge using a passing tractor with two identical trailers. The novelty of this study is the use of the dynamic interaction between the two identical trailers and the bridge rather than that between the truck and the bridge. One of the advantages of this is that the dynamic characteristics of trailers are easier to model and analyse. Under the proposed method, the residual acceleration of the two trailers is first used to eliminate the adverse effects of road surface roughness. The fundamental natural frequency of the bridge is obtained from the Fourier transform of the resulting signal. Next, the component corresponding to this fundamental frequency was extracted using bandpass filtering. The corresponding damping ratio and mode shape can be reconstructed through a short-time Fourier transform (STFT) under the assumption that the fundamental mode shape amplitude peak is located at the midspan. Finally, the element stiffness of the bridge was evaluated using the reconstructed fundamental mode shape. In this study, the proposed method is investigated via numerical examples and performs well in different scenarios, for example, in the presence of high-level measurement noise and low-level road surface roughness. Furthermore, the method was validated through field testing on the Li-Zi-Wan bridge in Chongqing. The extracted fundamental mode shape agrees well with that obtained using the stochastic subspace identification (SSI) method. The evaluated element stiffness matches very well with both the original design and the static deflection test results.

Study on spilled liquid from a continuous leakage in sloped tunnels

The study focuses on the behaviors of spilled liquid from a continuously leaked tank in sloped tunnels. Spillage width and area, which impact the potential heat release rates in case of fire, are investigated under different tunnel slopes and leakage flow rates by numerical simulations using interFoam based on the VOF method in the OpenFOAM toolbox following the validation. The simulation results show that the spillage width initially decreases rapidly and then slowly as the tunnel slope increases. Other parameters, including road surface roughness, physical properties of liquid and leakage source height, are also considered. Empirical models for predicting the spillage width and area are established considering both tunnel slope and leakage flow rate. The results may provide guidance for tunnel safety design and drainage system design affiliated with a tank leakage inside a tunnel.