Pavement Depth Research Articles

Analysis of the mechanical behavior of asphalt pavement under multiple coupling effects

To reveal the damage mechanism of asphalt pavement caused by the spatial rotation of the stress principal axis, and the influence range of excess pore water pressure on the pavement under the coupling action of multiple fields, the finite element software was used to build the three-dimensional highway asphalt pavement models. Under the consideration of gravity's impact on dynamic computation results, the study investigated the variation patterns of the direction cosines of the principal stress axes of asphalt structural pavement elements under the action of moving loads, as well as the distribution laws of excess pore water pressure. The results indicate that the influence of gravity on dynamic computation results increases gradually with the depth of the pavement, reaching an error of first principal stress of 40.1 % at the bottom of the lower layer. During the movement of loads, the pavement elements directly under the load undergo rotation of the principal stress axes in the xy, xz, and yz planes under different physical fields. With increasing pavement depth, the angle between the first principal stress and the z-axis primarily fluctuates between −90° to 0° and 90° to 180°. The cosine of the angle between the first principal stress and the x-axis and z-axis undergoes instantaneous positive and negative conversion, which can easily lead to transverse and longitudinal cracks on the road surface. The influence widths of excess pore water pressure along the longitudinal and transverse directions of the pavement increase with the pavement depth, with maximum widths of 5.426 m and 2.075 m, respectively. These findings offer new insights into the mechanisms behind the formation of transverse and longitudinal cracks on asphalt pavement and hold significant implications for the improvement of asphalt design methods.

Recognition and Characterization of Nanoscale Phases: Modulus Mapping of Asphalt Film in Pavement Mixture Cores.

The objective of this study is to recognize and characterize the nanoscale phase modulus mapping of the asphalt film in pavement mixture cores using atomic force microscopy quantitative nanomechanical technology. The pavement core samples from the upper and middle layers of four highways and laboratory samples were taken as the research object. The phase modulus-macro property correlation of recovered asphalt was analyzed using mathematical statistics. The results showed that the pavement core samples had more significant multi-phase and diversified phase characteristics compared to lab samples. This indicated that the asphalt in the pavement core had an obvious phase separation phenomenon due to aging. The phase modulus of each sample was distributed across a relatively wide numerical range, and there were also many numerical points with large fluctuations. Especially for the mixture sample containing SBS (Styrene-Butadiene-Styrene)-modified asphalt, the phase modulus distribution mappings presented a multi-peak phenomenon. Hence, considering the distribution characteristics of the data, the box plot method was introduced. Compared with quantified results from laboratory samples, the phase modulus of SBS-modified asphalt increased by 0.96 times, 1.18 times and 1.15 times, and that of base asphalt increased by 0.59 times, 0.56 times, 0.42 times, 1.24 times and 0.39 times, respectively. This indicates that the aging degree of asphalt in the upper layer was generally greater than that of the asphalt in the middle layer and that there was an aging gradient in the direction of pavement depth. All points were within the 95% confidence band in terms of correlation fitting, indicating a better fitting effect between phase modulus and complex shear modulus, as well as between phase modulus and penetration. This research provides innovative ideas for future multi-scale numerical simulation and cross-scale performance model development of asphalt binders.

Effect of Guar Gum Content on the Mechanical Properties of Laterite Soil for Subgrade Soil Application.

Using biopolymers for soil stabilization is favorable compared to more conventional methods because they are more environmentally friendly, cost-effective, and long-lasting. This study analyzes the physical properties of guar gum and laterite soil mixes. A comprehensive engineering study of guar gum-treated soil was conducted with the help of a brief experimental program. This study examined the effects of soil-guar gum interactions on the strengthening behavior of guar gum-treated soil mixtures using a series of laboratory tests. The treated laterite soil's dry density increased marginally, while its optimum moisture content decreased as the guar gum increased. Treatment with guar gum significantly enhanced the strength of laterite soil mixtures. For laterite soil with 2% guar gum, the unsoaked CBR increased by 148% and the soaked CBR increased by 192.36%. The cohesiveness and internal friction angle increased by 93.33% and 31.52%, respectively. These results show that using guar gum dramatically improves the strength of laterite soil, offering a more environmentally friendly and sustainable alternative to traditional soil additives. Using guar gum in T8 subgrade soil requires a 1395 mm pavement depth and costs INR 3.83 crores, 1.52 times more than laterite soil. For T9 subgrade soil, the depth was 1495 mm, costing INR 4.42 crores, 1.72 times more than laterite soil. This study introduces a novel approach to soil stabilization by employing guar gum, a biopolymer, to enhance the physical and mechanical properties of laterite soil. Furthermore, this study provides a detailed cost-benefit analysis for pavement applications, revealing the financial feasibility of using guar gum despite it requiring a marginally higher initial investment.

Performance characterization of long-term aged bitumen: Field and laboratory investigation

Aging of binder affects the service life of flexible pavements. Aging involves change in physical, rheological, chemical and morphological characteristics of binder making it hard and brittle resulting in pavement deterioration. Oxidation and volatalization are the two main mechanisms involved in aging. Aging stiffens the binder as it increases the viscosity of binder resulting in pavement distresses like fatigue cracking, ravelling and thermal cracking. Hence, it is obligatory to examine the aging effects on binder to predict the service life of flexible pavements. In this study, core samples are collected from 15 years aged in-service pavement section and fresh binder was aged for varying aging duration in the laboratory to simulate field aging condition. Field aged and laboratory aged binders were subjected to physical, chemical, rheological and morphological investigations for evaluating the variation of these properties with long-term aging. It is evident that the traffic speed influences the dynamic viscosity of binders. With aging, the rutting resistance of binders improved and fatigue resistance of binders diminished considerably. The severity of aging in binder increases with pavement depth in the field and 8 days of normal oven aging in the laboratory simulates 15 years field aging condition.

Cooling optimization of asphalt pavement by topology optimization and cooling mechanism analysis

The higher temperature in the hot summer area will seriously damage the safety and comprehensive performance of the asphalt pavement, thus it must be cooled down quickly and orderly. At present, the cooling pavements are designed mainly based on engineering experience, and the quantitative relationship between design objective and variable is not constructed during the design process. In addition, it is difficult to determine whether the material distribution of cooling pavement is optimal. These limit the further improvement of the pavement cooling effect. In this study, the relationship between the design objective (minimum average temperature of upper layer) and design variable (thermal conductivity of pavement) of cooling pavement was built by numerical simulation, and the cooling pavement with the best cooling effect was obtained by topological optimization technology. The influence of the initial value and distribution of the thermal conductivity on the average temperature were explored during the design process. The thermal effect analysis and thermal resistance were used to determine cooling mechanism of optimized cooling pavement. The topology optimization results demonstrated that the minimum average temperature could be obtained when the initial value of the thermal conductivity gradually increased with the increase in pavement depth. The optimized cooling pavement showed good cooling effect at night. The thermal effect analysis results showed that the design of optimized cooling pavement reduced the net heat absorption and the heat transfer efficiency of the pavement, and increased the thermal resistance inside the pavement, thus achieving the pavement cooling. Compared with the ordinary asphalt pavement, the pavement surface temperature and interior temperature could be reduced by up to 9.18 °C and 13.29 °C, respectively. The maximum heat absorption, dissipation and net heat flux absorption of the optimized cooling pavement were decreased by 65.59 %, 63.87 % and 59.40 %, respectively. The reduction of the net heat flux absorption and heat release in the optimized cooling pavement are conducive to mitigating the high temperature rutting and the heat island effect at night, respectively. Finally, the indoor irradiation test was carried out to evaluate the cooling effect of optimized model. The temperatures of optimized group at 7 cm and surface were 5.29 °C and 2.5 °C lower than those of control group, respectively. The effectiveness of the topology optimization design for cooling pavement were verified.

Evaluation of Asphalt Pavement Texture Using Multiview Stereo Reconstruction Based on Deep Learning

To quickly obtain texture information and accurately evaluate the skid resistance of asphalt pavement, a multiview stereo reconstruction method based on deep learning is proposed for evaluating the texture depth of asphalt pavement. First, a depth camera and a digital camera are used to collect RGB-D (i.e., Red, Green, Blue, Depth) and RGB (i.e., Red, Green, Blue) multiview image datasets on different types of pavements for model training and validation respectively. Then, the model precision is validated by calculating the overlap (intersection over union, IoU) between the ground truth point cloud and the reconstructed point cloud. The model performances under different training strategies are compared to obtain the best model, and the effects of image resolution, number of views, and types of pavement material on the model precision are analyzed. Finally, image processing methods and texture depth characterization indexes are proposed to obtain pavement texture information from the reconstructed depth map, thus validating the effectiveness of the model. The results show that the trained model using the transfer learning strategy achieves a reconstruction precision of 0.77 when the image resolution is 3024×3024 and the number of views is 7. Furthermore, the reconstruction performance remains stable across different pavement materials, suggesting the model is suitable for accurately reconstructing depth maps for asphalt pavements. The errors between the predicted texture depth value calculated based on the volume-based index (MTDp) and the profile-based index (MPDp) using the depth maps after image processing and the measured texture depth value (MTDe) using the sand patch method are 11.72% and 18.85%, respectively. It is believed that the pavement texture depth can be effectively evaluated from the reconstructed depth map using the volume-based metric (MTDp). In contrast to traditional testing methods, this approach requires using only a digital camera and a personal computer, making it a lightweight and intelligent analysis method for obtaining pavement texture depth information.

Effects of heavy truck braking on inverted asphalt pavement considering vehicle dynamics

Inverted asphalt pavement is an innovative pavement structure where the unbound aggregate base (UAB) is sandwiched between the asphalt concrete (AC) layer and cement-treated base (CTB). This structure has the potential to improve rutting resistance of pavement and prevent propagation of reflective cracks. However, its implementation has been limited due to the lack of experience. In addition, the braking condition of the vehicle applied on the pavement surface plays a significant role on the pavement responses, thereby affecting its service life. This study conducted a simulation using finite layer method to investigate the effects of heavy truck braking on inverted asphalt pavement considering vehicle dynamics. The results indicated that truck braking would generate a significant horizontal load and change the magnitude of the vertical load at each axle. Furthermore, the impact of vertical load on mechanical responses would gradually enhance and eventually dominate with increasing pavement depth. It was also observed that when the truck was treated as a whole, vehicle braking would result in a 3.2% to 10.2% increase in critical responses, but cause a 0.023% and 31.7% decrease in predicted rutting and life respectively. Among three truck axles, the effect of driving axle was close to that of the entire truck, but the effect of other axles was greater or lower than that of the entire truck. A comparison of three inverted pavements found that a thick AC layer could effectively reduce the critical responses and extend the pavement life. Furthermore, a thick CTB layer could also achieve most of the above purposes due to higher integral stiffness of pavement.

Falling weight deflectometer dispersion curve method for pavement modulus calculation.

The falling weight deflectometer (FWD) test is a common non-destructive testing method for evaluating the structural capacity of pavements. At present, data processing of the FWD test mainly focuses on the deflection data, while paying less attention to the deflection-time history. Because a FWD is equipped with impulse loads and geophones, which allow for the generation and capture of surface wave signal propagation, it is hypothesized that Rayleigh wave dispersion theory can be applied to calculate the modulus profile along the pavement depth by analysing the dispersive properties of the deflection signal measured during FWD tests. To test this hypothesis, we develop a new methodology for the FWD test and data analysis, referred to as the FWD dispersion curve method. We first introduce the concept of the new method, followed by an illustration of the procedure and the experimental set-up. Case studies on three concrete pavement segments are then presented to evaluate the effectiveness of the FWD dispersion curve method. Modifications to the existing FWD device are further recommended for the impact loading sources and signal collection process so that the modulus of a much shallower layer, such as the concrete slab and upper asphalt layers, can be obtained. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Modification of MEPDG rutting model based on RIOHTrack data

ABSTRACT Rutting is one of the most prevalent road distresses in asphalt pavement (AP), it is therefore critical to accurately predict the development of rutting to guide pavement design and maintenance. Based on the measured rutting data obtained from the RIOHTrack in China, the the most widely used MEPDG rutting model is evaluated for its applicability. Due to the original MEPDG rutting model being highly sensitive to a rapid increase of the number of load repetitions (NLR), a modified MEPDG rutting model is proposed by introducing a growth coefficient ( α ) for the NLR to reduce the effect of the rapid increase of NLR on the rutting prediction. The applicability of the modified rutting model is then verified by various pavement structures tested in the RIOHTrack, and the value of coefficient ( α ) based on the different annual average growth rates of traffic volume is provided for facilitating application. Moreover, the growth rate of the flow ruts is indicated as the critical parameter to describe the rutting development and used for rutting prediction, and the correlations between the pavement structures and the parameter are analyzed. The findings can guide for predicting the rut depth of the asphalt pavement within the whole service life.

Data-driven approach for investigating and predicting rutting depth of asphalt concrete containing reclaimed asphalt pavement

The aim of this paper is to investigate and predict the rutting depth of asphalt concrete containing Reclaimed Asphalt Pavement (RAP) content by the data-driven approach with aid of 6 six tree algorithm including Random Forest (RF), Gradient Boosting (GB), Adaptive Boosting (AdaB), Extreme Gradient Boosting (XGB), Light Gradient Boosting (LightGB) and Categorial Gradient Boosting (CatB) using default hyperparameters. A database containing 396 data samples derived from Hamburg Wheel Tracking Test (HWTT) and consisting of 11 input variables such as Corrected Optimum Asphalt Content (COAC), Reclaimed Asphalt Pavement, Nominal Maximum Aggregate Size (NMAS), Flashpoint, m-Value, Mass loss, Specific gravity, Viscosity, Creep stiffness, Temperature, and Number of wheels and 1 output Rutting depth are created. 10 times of 10-Fold CV and four metrics such as R2, RMSE, MAE and MAPE are used to verify the robustness of ML models. XGB model has highest performance with the mean value of R2, RMSE, MAE and MAPE to be equal to 0.9792, 1.0767 mm, 0.7085 mm, 0.1034% respectively. Comparing with other factors like the number of load cycles (number of wheels) and mix design considerations, temperature has a dominant impact on the rutting depth of asphalt pavement (type of binder, NMAS, RAP content). The rutting depth value rises with increasing temperature and COAC. NMAS has a negligible impact on asphalt pavement's resistance to rutting. To increase the rutting resistance when building the asphalt pavement, the chosen asphalt binder has high Viscosity and Flashpoint values. The local interpretation clearly demonstrates each factor's contribution to the rutting depth value in each unique scenario. Engineers can design the rutting resistance of asphalt pavement in advance thanks to the mapping produced by the Partial Dependence Plot 2D under grid contour.

Mechanical response of interlayer structural shear performance of asphalt pavement with functional layer considering interlayer contact state

To explore the mechanical response of asphalt pavement structure with functional layer, and make asphalt pavement have better service performance, a structural combination form of asphalt pavement with functional layers is proposed in this study, and the interlayer structural mechanical response analysis is carried out based on the theory of a multi-layer flexible layered system. The study first suggests a combined method of stress-strain and displacement at each point of the three-dimensional asphalt pavement structure under the combined compressive-shear load. Secondly, the shear strength of asphalt pavement structure with functional layer under the influence of compressive-shear combined loads, different longitudinal slopes, overload loads, and temperature-elastic modulus under different interlayer contact states are analyzed. Finally, the distribution of structural shear strain of asphalt pavement with a functional layer under different conditions is investigated. The results show that in the asphalt pavement structure with a functional layer, the functional layer is the weak layer of the base-surface layer, and the interlayer shear stress will change abruptly under the combined compression-shear load. The maximum shear stress and extreme strain values are mainly concentrated in the upper and middle surface layers, and they show a consistent trend of first increasing and then decreasing with the increase of the pavement depth. The functional layer can be used as a temperature buffer zone so that the asphalt pavement has good contact performance between the surface and base at 76 ℃ and good shear strength. The results of this study provide a theoretical foundation for studying the interlaminar shear resistance of asphalt pavement with functional layers.

Temperature Prediction Model for Asphalt surface Layer

In order to explore the distribution law of asphalt pavement temperature, based on the Nanjing to Yancheng(Ning-yan) Expressway in Jiangsu Province, and obtain the meteorological environment data of the expressway, temperature sensors are buried at different depths of the asphalt pavement to continuously track the temperature at different depths of the asphalt pavement and the atmospheric temperature. The relationship between pavement temperature and meteorology at different depths was analyzed. According to the pavement depth, a temperature prediction model suitable for asphalt surface layer was established, and the model predicted temperature was compared with the measured temperature.The results show that the temperature-time distribution of asphalt pavement surface has a significant periodicity, which is an approximate sine function curve. In the error range of ± 1 ℃, the accuracy of the asphalt surface temperature prediction model is 64.1 %.

Study of Velocity, Flow Depth and Froude Number of HDPE Diagonal Modular Pavement Using FLOW 3D

Modular pavement studies traditionally using physical modelling appear to be relatively expensive and risk substantial losses. Because of that, numerical simulation studies are a smart way to either create a new design or build a modular pavement on site before starting. This research examined the correlation of xyz velocity, flow depth and Froude number in a diagonal HDPE modular pavement using FLOW 3D. The simulations use Malaysian actual rainfall data to assess the ability of diagonal pavements to diffuse rainfall. The parameters xyz velocity, flow depth and Froude number were taken to see the absorbed rainfall capacity and the results show that the velocities x and y are greater than the velocities z. The flow depth was maintained at 425.6522 mm during the 600s and 6000s throughout the rainfall events for rainfall intensities of 5 mm/h and 85mm/h. Froude’s number is lower than zero. This FLOW 3D software is suitable as a modular pavement modelling since the velocity, flow depth and Froude number are closely related to the previous study. FLOW 3D can be used as an preliminary method for designing modular pavements.

Modification of rutting prediction model for with semi-rigid base in service based on accelerated loading test

With the rapid increase in vehicles, the load on the asphalt pavement increases, which aggravates the appearance of rutting disease. In order to establish an effective rutting depth prediction model, this study carried out accelerated loading tests (ALT) of asphalt pavement at three different temperature environments of 60 °C, 45 °C, and 30 °C for Ji-Qing Expressway at the end of service. The rutting depth of the test was measured using a Lr-70/700 laser rut measuring instrument, and the relationship between the rutting depth of asphalt pavement and the temperature change was examined. At the ambient temperature of 60 °C, three different loading speeds of 3.5 km/h, 4.5 km/h, and 5.5 km/h were tested to analyze the influence of load frequency on rutting depth. With the SPSS analysis software, the regression analysis of the collected test data was carried out to modify the rutting prediction model established by the indoor improved MTS test. The results show that when the loading speed is increased from 3.5 km/h to 4.5 km/h, the rutting depth can be reduced by 11%. When the loading speed increases from 4.5 km/h to 5.5 km/h, the rut depth can be reduced by 15.8%. When the loading temperature increases from 30 °C to 45 °C, the rut depth increases by 2.15 times. When the loading temperature increased from 45 °C to 60 °C, the rutting depth increased by 0.46 times.

Viscoelastic Dynamic Response of Asphalt Pavement in Long Slope Sections

To deeply understand the performance of asphalt pavement in long slope sections, the object of this paper is to obtain the viscoelastic dynamic response of asphalt pavement in long slope sections considering interface bonding conditions. An asphalt pavement model is built, and then for solving the problem, the Laplace-Hankel transformation is used to transfer the partial differential equations to the ordinary differential equations. Transfer matrices are adopted to present the relationship of stress and displacement between the pavement surface and any pavement depth, and a special transfer matrix characterizes the interface bonding condition. By this method, the analytical solution of the stress and displacement at any pavement position is received. After that, an example of asphalt pavement in a long slope section is introduced considering the distribution of stress and displacement, the magnitude of the horizontal load, and the interface bonding strength. The horizontal vehicle load affects the distribution of shear stress τrz in the pavement. The interface bonding strength is more important in long slope sections than in any other normal section of asphalt pavement.

Optimizing asphalt mix design through predicting the rut depth of asphalt pavement using machine learning

Generally, when asphalt concrete (AC) is in the design phase, the rutting development of the actual pavement is always not considered. Traditional simulative wheel-tracking tests, which are used to evaluate the rutting resistance of the designed AC, are difficult to accurately predict the rut depth of actual pavement in the realistic climate condition and traffic. To develop an asphalt mix design approach and avoid early serious pavement damage (rutting), the data relating to rut depth of AC pavements were extracted from the LTPP program,. 27 input features about climate condition, traffic, pavement structure, and pavement materials properties were selected based on collinearity diagnostics and impurity-base feature importance methods. This study also analyzed the effect of different imputation methods used to handle missing variables of the dataset on the performance of ML models. Four different ML algorithms including Support Vector Regression (SVR), Random Forest, Artificial neural networks (ANN), Gradient boosting, and multiple linear regression were trained to predict rut depth. The performances of these models were evaluated by calculating different performance measurement scoring metrics, such as coefficient of determination (R2), root means square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and scatter index (SI). Finally, an asphalt mix design procedure based on the best ML model was proposed. The results showed that compared to other imputation methods, the mean imputation method can result in the best performance of ML models. Compared with other ML models, Gradient boosting was the optimum model to predict rut depth for the imputation dataset (R2 = 0.87) and full dataset (R2 = 0.92). Additionally, each ML model except for Random Forest can achieve better accuracy than that of the calibrated rutting prediction models of HDM-4 and MEPDG from the literature. The proposed mix design procedure was implemented to design AC for the surface layer of a pavement project. The rut depth of the pavement, whose surface AC was the same as the recommended mixture design by the proposed method, is lower than the pavement failure criterion (MEPDG) after 20 years. It implicated that the proposed procedure based on the ML model can effectively avoid early intolerant rutting damage of flexible pavement.

A Study on the Design Depth of Permeable Road Pavement through Dynamic Load Experiment.

This study investigated vertical strain and stress through a dynamic load experiment at the testing area of Ke-Da Road, Pingtung, Taiwan. A thirty-five-ton truck was moved at constant speeds of 40, 60, and 80 km/h to simulate heavy load conditions to study the mechanical variations. From the results, it was found that the strain and stress curves of the permeable road pavement showed asymmetry due to the viscoelastic property of the open-grade friction course. The results showed that vertical strains and vertical stresses of permeable road pavement were greatly affected by the axle configuration and the change in traffic speed. Furthermore, to propose the design thickness of a permeable road pavement, the pavement strain and stress were modelled with respect to depth using regression based on these collected data. According to the stress regression models and considering the construction uncertainty, the recommend design depth of a permeable pavement is 30 cm. The findings of this study would be helpful in determining the permeable road pavement depth when subjected to heavy traffic load, and the material combination of open-graded friction concrete, porous asphalt concrete, and permeable cement concrete was proposed in this study during the design period.

Impacts of Field and Laboratory Long-Term Aging on Asphalt Binders

Asphalt binder aging accelerates cracking of flexible pavement. Quantifying the extent of binder aging allows the formulation and selection of appropriate binders for flexible pavement applications. Asphalt binder aging depends on climatic exposure, asphalt concrete field-surface density, and binder chemistry. The aim of this work was to select a laboratory-aging protocol to represent realistic field aging in Illinois. Binders were extracted from field cores aged from eight to 31 years. Extracted binders were tested for rheology and chemical characteristics. Small- and large-strain rheological parameters were determined. In addition, chemical functional groups and molecular weight distribution of extracted binders were evaluated. Effects of aging across pavement depth were also investigated. Double pressure-aging vessel (PAV) was identified as a suitable laboratory long-term aging protocol to represent eight to 12 years of aging for flexible pavements in Illinois. Thresholds were proposed for selected small- and large-strain rheological parameters representing realistic field aging. These thresholds may be used for binder selection and procurement to ensure the binder’s long-term performance. Furthermore, chemical analysis on extracted binders showed that only specific carbonyl functional groups are affected by field aging.

Experimental and Statistical Analysis of Bitumen’s Field Ageing in Asphalt Pavements

Field ageing gradients of bitumen samples recovered from 14 asphalt road sections were investigated via rheological and chemical characterizations and statistical analysis. The effects of air voids and environmental factors on the ageing gradient were evaluated using the field ageing and climate data. The effectiveness of the pressure ageing vessel (PAV) test was assessed in accelerating the bitumen’s long-term ageing by comparing with the field ageing data. Critical factors for field ageing were identified using statistical methods. A statistical model for the Glover-Rowe (G-R) parameter was formulated and verified by incorporating the screened key factors. Results indicate that a threshold air void content (around 6%) exists in differentiating the field ageing gradient patterns in the asphalt pavements. An increasing tendency is observed between the ageing gradient and annual days below 0°C. The chemical indices, stiffness-related indices, and G-R parameter can quantify the field ageing gradient of asphalt pavements. The PAV test can condition the bottom slices’ bitumen to the same ageing level as that in a pavement depth of 0.5–2 cm after 8 years’ field service. Pavement service life, binder content, minimum temperature, days above 32°C, and days below 0°C are the critical material and environmental factors that significantly affect bitumen’s complex shear modulus, crossover frequency, G-R parameter, and Δ Tc. The statistical model is verified with an acceptable mean absolute error of 28.1% and a R2 value of 0.95.

An improved computation method for asphalt pavement texture depth based on multiocular vision 3D reconstruction technology

The depth of an asphalt pavement structure is an important index used to reflect the anti-skid performance of the pavement, which directly affects the driving safety of vehicles. In the current specifications (e.g., China, European and American standards), the mean texture depth (MTD) of an asphalt pavement is generally measured by the sand-patch method (SPM). However, SPM is easily affected by the operator’s subjective experience and experimental environment, resulting in low accuracy and high scatter in the data. In view of such shortcomings, a fast and accurate method to obtain the texture depth of asphalt pavement was proposed by designing a new test tool and a computer-aided calculation method. The multiocular vision theory was adopted to reconstruct a three-dimensional (3D) point cloud model of pavement texture. To reflect the concavity and convexity of pavement texture and eliminate the influence of pavement slope, the iterative closest point (ICP) algorithm was employed to obtain the datum reference plane (DRP). On this basis, the pentahedral volume calculation method suitable for evaluating the texture depth of an asphalt pavement was proposed. Laboratory experiment results using SPM were inverted by equal volume calculation to obtain the texture reference plane (TRP) and MTD'. Meanwhile, a 3D average maximum elevation (Avg.EL) algorithm was obtained by the evolution of two-dimensional mean profile depth (MPD). Furthermore, three parameters, namely MTD, MTD', and Avg.EL, were used to measure the texture depth of two field pavement sections in service. The experimental results showed that the correlation between MTD' and MTD was better than that between Avg.EL and MTD, and the degree of scatter was smaller. MTD' could be used directly as a reference value for MTD. In the present work, the 3D volume calculation method of asphalt pavement texture depth was proposed, which integrates the technical flow of image acquisition, model reconstruction, and algorithm analysis. The developed method provides a technical reference for the application of computer vision technology in the analysis of pavement texture depth.