Road Pavement Research Articles

The Durability of Stone Matrix Asphalt (SMA) Mixtures Designed Using Reclaimed Asphalt Pavement (RAP) Aggregates Against Floodwater Immersion

The durability of asphalt mixtures against floodwater immersion can serve as a reference to anticipate potential road damage. Moreover, Reclaimed Asphalt Pavement (RAP) materials have been discovered as a substitute for aggregate materials in road pavement due to their environmental friendliness and cost-effectiveness. Therefore, this research aimed to assess the durability of asphalt mixtures produced using RAP aggregate materials against floodwater immersion for 1, 2, 4, and 8 days. The process involved using Stone Matrix Asphalt (SMA) mixtures with a proportion of 33% RAP aggregate as test specimens. The Marshall test conducted on the asphalt mixture produced an optimum asphalt content (OAC) value of 6.1%. Moreover, the durability of the mixture reduced up to the 8th day of immersion with a residual strength value of 86.29%. It was also discovered that the reduction in the durability value of the mixture produced using 33% RAP aggregate was almost similar to the application of 100% new aggregate (non-RAP). This means RAP aggregate materials are feasible as an environmentally friendly substitute in the mixture of road pavement.

Comparison of Smartphone-Based and Drone-Based Approaches for Assessing Road Roughness

Continuous road-pavement condition monitoring is essential to maintain the effectiveness and performance of road infrastructure. It also helps to optimize resource allocation and prioritization for pavement rehabilitation programs. The traditional methods that have been used for assessing road pavement condition are high cost and time consuming. Contrary to conventional methods, low-cost and new emerging technologies such as smartphones and drones can be used. The study aims to investigate the merits of using two different types of device for assessing road roughness: the smartphone as a response-type and the drone as a non-contact-type device. To this end, firstly, linear vertical acceleration data is collected by using a smartphone that is mounted on a car dashboard. Then, the collected data is used for calculating vertical displacement data using a fast Fourier transform. The displacement data is used for calculating international roughness index (IRI) as a measure of road roughness. Secondly, a drone flies over the studied road to construct a 3D model, extract the longitudinal profile, and eventually calculate the accurate IRI values of the road. Finally, the results of the approaches above will be compared with the results of a road surface profiler (RSP) as a reference method. Although the results reveal a significant correlation (above 0.8) of both methods with the RSP method, from an operational point of view, the smartphone was more cost and time effective, and also an easy-to-use approach for the purpose.

Evaluation of HMA Modified with Titan Polymer

Improving the quality of the road pavement and ensuring the safety of the drivers on the road are issues of paramount importance. Cracking and rutting are two of the most common damages that occur to asphalt pavement due to environmental effects and traffic. Utilizing a modified binder is a solution for improving the pavement's resistance to these damages and enhancing pavement durability. This study investigates the performance of Hot Mix Asphalt (HMA) modified with Titan7205 polymers and then compares it with that of a control mix of modified HMA with Styrene-Butadiene-Styrene (SBS) or crumb rubber (CR). Two percentages of Titan7205 were utilized to find out which dose provided better performance. HMA was prepared by adding polymers with different percentages (3% and 5% Titan7205, 4% SBS, and 8% CR). After preparing the samples, they were tested (unconditioned and AASHTO R30) for Dynamic Shear Rheometer (DSR) testing, Cantabro Mass Loss (CML), Tensile Strength Ratio (TSR), and Indirect Tensile Tension (IDT). The results showed that the mix with 3% of Titan7205 has a similar or better performance than the mixes with the other polymer additives utilized in this study.

The evaluation of crumb rubber as a filler in asphalt mixture to against moisture sensitivity

In Malaysia, road pavements are frequently damaged by the accumulation of water on the surface caused by heavy rain, due to the humid climate. The presence of water within the asphaltic concrete mixture particles may impede their workability. Exposure towards natural phenomenon such as rain can be the main cause related to the presence of water inside the asphalt pavements. To improve road conditions for users, it is necessary to consider alternative road pavement designs. This study proposes using rubber crumb as modifier as its water-impermeable characteristic will be able to enhance the performance of standard asphalt mixtures to reduce asphalt mixtures moisture sensitivity. Subsequently, the Marshall Mix Design method was utilized to conduct Indirect Tensile Strength tests in order to achieve the objectives of this study. A total of 16 samples were prepared for this study, comprising of standard asphaltic combinations as well as asphaltic mixtures with different percentages of crumb rubber (0%, 4%, 6%, and 8%) relative to the overall weight of the asphalt mixtures. In comparison to the typical way of asphaltic mixture, the sample that utilized crumb rubber as a modifier had a substantially greater value of strength based on the Indirect Tensile Strength Test results. As expected from the findings of the study, the presents of water in asphalt mixtures reduces the asphalt mixtures strength and workability. To conclude, incorporating crumb rubber in asphalt mixtures enhances their effectiveness in resisting moisture sensitivity.

The Effect of Recycled Material and Buton Granular Asphalt (BGA) on Asphalt Concrete Mixture Performance

Pavement recycling is an appropriate technology for constructing and restoring road pavement structures due to the limited supply and high cost of pavement materials. This study aims to improve the recycled mix's performance by replacing the fine aggregate in the recycled mix for road pavement with Buton Granular Asphalt (BGA). The percentage of recycled material (Reclaimed Asphalt Pavement/RAP) in the mixture was limited to 20% and 30% by weight of the total mixture. BGA was added by 3%, 6%, and 9% in each mixture with the RAP variation. Mixture performance was obtained through Indirect Tensile Strength (ITS) testing. The ITS test results showed that the ITS value of the mixture with RAP and BGA increased by an average of 4.7–15% compared to the mixture without RAP and BGA. The Toughness Index (TI) value increased by 3.5–19.8% with the addition of RAP. With the addition of 3% BGA, the TI value tends to increase and subsequently decrease up to 9% BGA levels. The result indicated that adding 30% RAP and 3% BGA to the mixtures improved pavement performance and could be a solution to increase the elasticity and fracture resistance of the mixture. Doi: 10.28991/CEJ-2023-09-06-09 Full Text: PDF

YOLOv5s-M: A deep learning network model for road pavement damage detection from urban street-view imagery

Road pavement damage affects driving comfort markedly, threatens driving safety, and may even cause traffic accidents. The traffic management department conventionally captures pavement damage information mainly using manual and vehicle-mounted equipment, which is not conducive to the detection of large-scale road pavement distress. Street-view images can provide full-view images of urban roads where the data is updated regularly by navigation map service companies, making it possible to rapidly detect pavement damage in urban areas. This paper presents a new pavement damage detection approach that is built upon an improved YOLOv5 network and street-view images. The proposed model can deal with a large-scale detection layer to improve the detection precision of large distress targets, achieving thus both cross-layer and cross-scale feature fusion by using the Generalized Feature Pyramid Network (Generalized-FPN) structure. The improved network also involves a diagonal Intersection over Union loss for regression calculation of the boundary box and builds the decoupled Head structure to achieve the decoupling detection of prediction and regression. As a result, the fusion of the weak feature information in feature layers is enhanced at different spatial scales, a more suitable method is achieved for pavement damage detection in the complex context of multi-scale street-view images, and the accuracy of the modified network is much improved in the detection of pavement distress from street-view imagery. Furthermore, We created a large image sample set for model training and testing, and a total of 156,304 street-view images, obtained from Fengtai District, Beijing, China was used for demonstrating the usefulness of the proposed network. The findings indicated that the proposed approach could effectively achieve pavement damage detection of urban roads from street-view images, with a precision average of 79.8% on the test samples. Moreover, the developed model was applied for pavement damage detection for all the roads in Fengtai District, Beijing, indicating that our method can offer viable damage data for road maintenance planning.

Presenting the Self-Healing Potential Model by Induction Heating Method in Warm Asphalt Mixtures by Artificial Neural Network

The increasing number of vehicles in the world and the increase in the dimensions of light and heavy vehicles with different combinations, including simple and compound axles, have caused various types of damage in the asphalt, which affects the performance of the roads and requires a suitable pavement. For this reason, engineers try to improve the performance of asphalt mixture in different ways and increase their useful life. Cracking is one of the main failure modes of asphalt pavements. Fatigue cracking is the most common form of asphalt failure and is one of the important factors in reducing the life of road pavements. The purpose of this research is to investigate the index of self-healing potential (HI) of asphalt mixtures containing activated carbon and zycotherm by induction heating method and modelling by artificial neural network. First, by making warm mixture asphalt samples containing activated carbon, self-healing by induction heating method was provided in the pavement, and then the effect of traffic loading and environmental conditions simultaneously in terms of changes in loading parameters, taking into account the conditions of the service life. The results show that the use of activated carbon increases the values of shear modulus. It also improves the properties of modified bitumen at all temperatures. The resistance of the asphalt mixture against breaking and cracking, as well as the maximum force tolerance at the temperature of -16°C is higher than at the temperature of 25°C, which is one of the reasons for the reduction of flexibility of bitumen at low temperature. Also, for modelling, perceptron multi-layer neural network (MLP) with 9 input variables, three hidden layers and one output layer has been used. The results showed that the explanation coefficient of the neural network model for training, validation and evaluation data was 0.99717 for training, 0.62053 for validation and 0.9301 for testing. These values correctly show the high fitting ability of the proposed neural network model. Also, the lowest value obtained for the mean squared error is 4.9223x10^(-5) and in epoch 14.

Factors associated with driver injury severity of motor vehicle crashes on sealed and unsealed pavements: Random parameter model with heterogeneity in means and variances

The effect of sealed or unsealed road pavements on motorist’s injury severities has not been extensively explored. This study collected a four-year crash dataset (2015–2018) from South Australia to explore this issue. The data shows 3,812 and 1,086 crashes at sealed and unsealed pavement surfaces, respectively, during those years. This study examines the consequence of sealed and unsealed pavements on driver injury severity outcomes of motor vehicle crashes. A mixed logit model was developed by accounting for heterogeneity in means and variances of the random parameters. The variables were distributed among several categories: driver, temporal, spatial, roadway characteristics, crash type, vehicle type, and vehicle movement. Four random parameters were observed in the sealed model, whereas five parameters were in the unsealed one. Moreover, the sealed pavements model showed substantial heterogeneity in means of four of the random parameters, while the unsealed pavements model has some heterogeneity in both means and variances of some of the random parameters. Marginal effect results indicate that two indicator variables have enlarged the likelihood of driver severe injury consequences in sealed, alcohol involvement and posted speed limit > 100 km/hr. Additionally, four other significant variables sustain the probability of severe injury outcomes at unsealed pavement like male drivers, middle-aged drivers, rollover crash types, and crashes at straight roads. Based on these variables, various countermeasures were recommended to enhance the safety of both types of pavements.

Development of a program for automatic identification of productivity of construction workers

In the Japanese construction industry, work environment and productivity data of construction projects are collected and analyzed by on-site engineers as individual data based on attendance and performance records. Therefore, productivity data are not uniformly collected and cannot be considered as highly reliable. Therefore, in this study, a prototype of a system is developed that automatically collects data regarding the workers’ activities using sensors and analyzes the working environment and productivity of construction projects. Although image analysis is typically employed for capturing workers’ activities, it is extremely costly and time-consuming for collecting and discriminating data. In addition, it is not practical because it requires adjusting the system to suit the changing environment of the construction site each time. Accordingly, we have conducted motion-detection using accelerometers, gyro-sensors, and GPS that can be attached to individual workers. We have constructed a hierarchical system that combines a phase to detect large acceleration changes that trigger motion, a phase to discriminate periodic motion, and a phase to classify motion by machine learning. The system classified motions into three productivity types: “direct work,” “support,” and “delay.” As a result of applying this system to the sensing data of workers performing road pavement construction-related works, the system discriminated with an overall accuracy rate of 42%. Furthermore, because of the wide range of behaviors that correspond to each productivity behavior category, a large amount of data must be read from the site adopting the system for obtaining training data to perform final behavior classification by machine learning based on the sensing data. Therefore, a system relying on machine learning may not be able to achieve a higher accuracy; thus, in the near future, we aim to automatically discriminate the nine categories of productivity behaviors shown in previous studies.

Comparison of Asphalt Concrete-Wearing Course (AC-WC) characteristics using 60/70 asphalt penetration and Elvaloy modified asphalt

The performance of the asphalt mixture is affected by the asphalt characteristics and the gradation of the aggregate. Polymer-modified asphalt has been widely used to overcome road pavement damage such as cracking, spalling, yielding, and damage due to deformation due to grooves, curling, or rutting. Polymer-modified asphalt has been widely used, both from plastomers and elastomeric types. Elvaloy modification asphalt resulted from the conventional asphalt mixing process of 60/70 with Elvaloy additives from Dupont that are elastomer using RET (Reactive Elastomer Terpolymer) technology. This study aimed to compare the characteristics of the AC-WC mixture using 60/70 penetration asphalt and Elvaloy modified asphalt. The study began by making a paved mixture using 60/70 penetration asphalt with the middle-graded aggregate. The Elvaloy modified asphalt has three aggregate gradations: lower limit, middle, and upper limit gradation. Test objects were made as many as 156 test objects to obtain Optimum Asphalt Content (OAC) values, then using OAC values, made 36 test objects for the Marshall test; 4 test objects for Wheel Tracking Machine (WTM) testing. The Marshall and WTM tests determine the performance of 60/70 penetration asphalt and Elvaloy modified asphalt on the AC-WC mixture. The research results of the properties test showed that Elvaloy modified asphalt has a better penetration value, softening point and ductility than 60/70 penetration asphalt. Marshall’s test results provided the highest Stability and flow value in the Elvaloy modified asphalt mixture. From the results of the WTM test, only the middle grade of Elvaloy modified asphalt meets the specification.

Material characterization of coconut midrib strip - a sustainable material for ground improvement

The tensile behavior of soil is of utmost importance in civil engineering as tensile failure can cause cracking earthen dam construction and road pavement subgrade. To address this issue, natural fiber reinforced geotextiles are increasingly being utilized in various geotechnical applications. This study aims to investigate the effect of gauge length, strain rate, and Young's modulus of a single coconut midrib taken from a leaf by conducting a series of uni-axial tensile strength tests. Additionally, the characteristic tensile strength is determined using Weibull statistical distribution analysis to evaluate the consistency of test results. Overall, this article contributes to a better understanding of the tensile behavior of natural fiber reinforced geotextiles and can aid in the design and construction of geotechnical structures that are resistant to tensile failure. According to the study's findings, an increase in gauge length and strain rate led to a decrease in the Weibull modulus. Moreover, for larger gauge lengths and strain rates, inconsistent variations in the coefficient of variation were observed. The experiments also showed that midribs immersed in water for seven days exhibited lower tensile strength, and consistent outcomes were obtained when using a gauge length of 150 mm and a strain rate of 0.3%/min. These results contribute to a better understanding of the factors that affect the tensile behavior of coconut midribs, particularly when used in geotechnical applications, and can inform the selection of appropriate testing parameters for future studies.

Access to paved roads, gender, and youth unemployment in rural areas: Evidence from sub‐Saharan Africa

AbstractThe poor development of road infrastructure is one of the main obstacles to economic activity and therefore to employment in rural areas of sub‐Saharan Africa (SSA). Using cross‐sectional data from 31 SSA countries, this study examines, from a gender perspective, the effect of access to paved roads on youth unemployment in rural areas in the region. The seemingly unrelated bivariate probit framework has been adopted in analysing the data. This approach is used for addressing the possible endogeneity bias that occurs when the dependent variable and regressor are both endogenous and binary. The major finding of this study reveals that access to paved roads in rural areas reduces the probability that young people will face unemployment. The aforementioned is more pronounced for young women than for young men. Moreover, the greater the proximity to the road increases, the greater its reducing effect on the probability of young people being unemployed. It is therefore important that the development of road infrastructure in rural areas be at the centre of public policies to address youth unemployment in SSA countries. This will significantly increase women's empowerment, through their participation in the labour market, and the full economic participation of young people.

Economical Formation Level for Road Pavement

Abstract: To establish the most economical estimate for a stretch of road by conducting a survey for RL of different points, calculating a formation level for the road where the quantity of Earthwork i.e., cutting and filling is equal, choosing such a material for pavement design which is gives more strength and is also economical. Selecting the site of a non-constructed road form MGM Institute of Hotel Management to MGM Hospital Chhatrapati Sambhaji Nagar, Maharashtra, India. Analysing the cost difference in estimate if we change the depth of formation level. Calculating the cost of construction for formation level from depth 98.00m to 99.00m. The cost calculation for earthwork varies with change in depth of formation level. The cost of pavement design is constant for all formation bed. Thus, the only variation is in the cost of construction of pavement surface. We have selected water bound macadam (WBM) pavement surface. As a future scope we will design different pavement surfaces like flexible, rigid and mixed. Designing of these pavement, different layers of the pavement as per IRC specification. The most economical formation level with most economical pavement will become the most durable and economical road design. We would use dumpy level, chain, arrows, tape, ranging rods, tripod, levelling staff for the survey of Reduce levels. Comparing the cost of construction of two types of pavements surfaces.

The Use of Steel Slags in Asphalt Pavements: A State-of-the-Art Review

Steel slag is a by-product obtained through the separation of molten steel from impurities in steel-making furnaces. It can be produced by different types of furnaces (blast, basic oxygen, electric arc, ladle furnaces). The reuse of metallurgical slags in road pavements can pursue aims of recycling and environmental sustainability. Based on the extensive literature, the paper presents a state-of-the-art review concerning the use of slags in asphalt pavements, discussing the main controversial literature findings. Slag manufacturing processes, chemical, morphological, and physical characteristics, affect its contribution to the asphalt mixture, when it partially or fully substitutes natural aggregates. Legislative state-of-the-art environmental issues, weathering, and leaching aspects are also discussed. The main mechanical and durability properties of pavements containing different types of slags are analyzed based on laboratory and field studies. Generally, the higher mechanical properties of steel slag suggest that its inclusion in asphalt mixtures can provide high-performance pavement layers (excellent strength and stiffness, superior rutting and fatigue resistance, low moisture susceptibility). However, several research gaps still exist (e.g., mix design and seasoning procedure, bitumen–aggregate affinity, low-temperature behavior, brittleness); they are discussed to direct possible future study efforts to clarify specific technical aspects, such as, for example, the effect of slag morphology and physical properties on the final mix properties and the development of specific mix design guidelines.

Effect of Blasting Waste as An Additive in Bitumen Mixtures

The use of blasting waste in road pavement mixes is one of the new alternatives in Malaysia. Therefore, this study aimed to look at the potential of blasting waste as an additive in bitumen mixtures in road construction. Several percentages of blasting waste additive were studied, namely 0%, 5%, 10%, 15% and 17%. The parameter values of each of these mixtures were obtained through the Marshall test. A total of five sets of specimens were prepared for the Marshall test, with each set containing three samples. From the test result, all the percentages of blasting waste as an additive in the bitumen mixture passed the Public Works Department (Jabatan Kerja Raya or JKR) specifications. It was also concluded that 10% of blasting waste as an additive is the optimum value, with a stability value of 82.5kN as compared to the stability value of the control sample at 37.7kN. The flow and Marshall Quotient values for the 10% sample were 4mm and 20kN/mm, respectively. All values obtained from this study were in accordance with JKR specifications, indicating that blasting waste has great potential in improving the specification values of bitumen mixtures.

Experimental and Machine Learning Approach to Investigate the Mechanical Performance of Asphalt Mixtures with Silica Fume Filler

This study explores the potential in substituting ordinary Portland cement (OPC) with industrial waste silica fume (SF) as a mineral filler in asphalt mixtures (AM) for flexible road pavements. The Marshall and indirect tensile strength tests were used to evaluate the mechanical resistance and durability of the AMs for different SF and OPC ratios. To develop predictive models of the key mechanical and volumetric parameters, the experimental data were analyzed using artificial neural networks (ANN) with three different activation functions and leave-one-out cross-validation as a resampling method. The addition of SF resulted in a performance comparable to, or slightly better than, OPC-based mixtures, with a maximum indirect tensile strength of 1044.45 kPa at 5% bitumen content. The ANN modeling was highly successful, partly due to an interpolation-based data augmentation strategy, with a correlation coefficient RCV of 0.9988.

Using Cement as Filler to Enhance Asphalt Mixes Performance in Hot Climate Regions

This paper investigates the addition of different percentages of ordinary Portland cement as a filler in conventional asphalt concrete for a range of heavy traffic. Road pavement agencies in hot areas face the daunting challenge of preserving their pavements in a fair to good condition to increase their lifespan. This challenge is due to the high occurrence of permanent pavement deformation via rutting, which is one of the major distress factors influencing pavements. This is a particularly serious issue in hot and arid countries which are closely associated with various aggravating factors. These aggravating factors include the choice of bitumen binder viscosity, the type of bitumen, the available low-quality materials, and the high environmental temperatures. Ultimately, poor performance will show within the first few years of service as permanent deformations such as rutting, shoving, and depressions. The examined properties include the resilient modulus and the resistance to rutting. Findings indicate that the resistance to rutting and the rigidity of the asphalt concrete are both substantially increased as the cement content is increased. Moreover, to meet the heavy traffic spectrum requirements, increasing the embedded cement content in the asphalt concrete improves pavement structural capacity. Finally, based on the rigidity expected for different cement levels, design curves are provided for pavement design in hot climates using low quality aggregate materials.

Stabilisasi Tanah Gambut Dengan Menggunakan Campuran Kapur Studi Kasus : Ruas Jalan Rengat - Bukit Meranti Kecamatan Rengat Kabupaten Indragiri Hulu

Riau is one of three provinces with dominant peatlands. Indragiri Hulu Regency is the sixth largest district with the largest area of ​​peatland with a value of 222 thousand hectares. Particularly in Sekip Hilir Village, Rengat District, Indragiri Hulu Regency, there is quite extensive peatland and in this location there is infrastructure in the form of Jalan Rengat – Bukit Meranti. The construction of road infrastructure on peatlands has experienced many problems. This is because peat soil has a very low carrying capacity so that the settlement that occurs is large and unable to support the work load. Stabilization efforts are needed to improve the carrying capacity of peat soils. Stabilization on peat soil will be carried out chemically using lime as a stabilizing mixture with variations of 5%, 10% and 15%. From the results of the tests carried out, namely the addition of lime variations to the peat soil stabilization mixture is proven to increase the CBR (California Bearing Ratio) value. The CBR value of peat soil mixture with the addition of 5% lime was obtained at 17.28%. At the addition of 10% lime the CBR value increased to 21.81% and at the addition of 15% lime it increased to 30.04%. From the CBR value of peat soil with variations in lime mixture obtained, the authors prove that an addition of ≥ 5% has been able to achieve a laboratory CBR standard value of 17.28% and has met the Bina Marga specifications for road pavement subgrade, namely ≥ 6%. .
 

Assessing the effects of unit cost uncertainty on flexible road pavement economics under the influence of climate change

Pavement researchers typically adopt life cycle cost analysis (LCCA) to quantify changes in the economic performance of road pavements due to the effects of climate change. As uncertainty exists in the unit cost of materials, fuels, and machinery operation, the assessment of climate change-induced pavement costs invariably involves uncertainty. If such uncertainties remain unaddressed, the assessment of pavement costs will not be accurate. Therefore, this study develops a stochastic pavement LCCA framework to account for the effects of such uncertainties on climate change-induced pavement life cycle cost. This is achieved by integrating a sensitivity analysis methodology and Monte Carlo simulation. To demonstrate the applicability of the framework case studies are performed for standard interstate and standard primary road pavement sections in four climate zones in the United States under a high climate change Representative Concentration Pathway (RCP8.5) for four different periods between 1981 and 2100. The results show that pavement maintenance, end-of-life (EOL), and transportation costs are most affected by climate change. To assess climate change-induced pavement costs more accurately, it is important to improve the accuracy of gasoline, diesel, and hot mix asphalt (HMA) unit costs, as they are the most sensitive input to the pavement LCCA model.

A new load bearing capacity assessment method of the road pavement based on surface strain and the spectrum of the acceleration

Based on the coupling res model of vehicular and road, the influences of surface layer modulus, surface layer thickness and equivalent modulus of elasticity on the spectrum of the acceleration, surface deflection under wheels, and surface strain at the distance of 0.4 m to 0.8 m away from wheels are analyzed. Results show that the fundamental frequency of the road pavement increases with the growth of surface layer modulus and equivalent modulus of elasticity, and decreases with the increase of surface layer thickness. Both surface deflections and surface strains decrease with the increase of equivalent modulus of elasticity as well as surface layer modulus and thickness. Moreover, when the equivalent modulus of elasticity is more than 2500 MPa and surface layer modulus is more than 4000 MPa, surface deflections and surface strains have a little variation with the enhance of the surface layer modulus and equivalent modulus of elasticity. The solution method of the spectrum of the acceleration and equivalent modulus of elasticity, surface deflection under wheels, surface strain at the distance of 0.4 m to 0.8 m away from wheels, and surface deflection based on surface strain are established. The load bearing capacity assessment method of the road pavement based on surface strain and the spectrum of the acceleration is provided. Finally, the feasibility and accuracy of this method is verified by comparing measured values in field testing. The method in this paper provides a theoretical support for the design, construction and maintenance of road engineering.