Surface Curvature Index Research Articles

Unraveling the optimal strategies for asphalt pavement longevity through preventive maintenance: A case study in South Korea

This study provides a comprehensive evaluation of three pavement surface treatments—Re-Ascon, Micro-surfacing, and Fog-seal—aimed at enhancing the longevity of asphalt concrete pavement. Utilizing visual inspection, bearing capacity measurements, and linear regression modeling, this study rigorously assesses the effectiveness of these strategies in real-world road environments. Notably, Re-Ascon emerges as the least damaged method in visual inspection, offering crucial insights into initial pavement conditions. Visual inspections consistently underscore Re-Ascon's outstanding performance in mitigating pavement distress, particularly in addressing cracking. Quantitative analysis using the Surface Curvature Index (SCI) from Falling Weight Deflectometer (FWD) measurements highlights Re-Ascon's superior elastic modulus (E1) and resistance to deflection compared to alternatives. Re-Ascon's 20 % SCI reduction compared to Fog-seal highlights its superior deflection resistance, supported by logarithmic elastic modulus values. Correlation analysis confirms Re-Ascon's resilience, maintaining low SCI even under higher loads and adverse weather conditions. Geographical variations and method-specific influences are revealed through PCI analysis, emphasizing the vital impact of preventive maintenance. The identified PCI threshold of 60 % guides timely interventions for major maintenance, while service life analysis forecasts approximately 3.0, 2.5, and 1.5 years for Micro-surfacing, Re-Ascon, and Fog-seal methods, respectively. In conclusion, this research furnishes critical insights into preventive maintenance methods, advocating a holistic assessment through visual, structural, and predictive perspectives, and emphasizing the need for ongoing research to advance sustainable strategies.

Enhancing pavement performance prediction with traffic speed deflection data

ABSTRACT Pavement performance modelling is an essential part of pavement management. Past research has shown that a structural indicator can be included in pavement performance models to capture differences in performance between weak and strong pavements. Weak pavements are expected to deteriorate more rapidly than strong pavements, as consistently shown in past research. The work presented in this paper uses the Traffic Speed Deflectometer (TSD) to calculate multiple structural indexes and compares their ability to differentiate between poor and well performing pavements. Historical condition data from the US National Parks pavement network is used in this assessment. Two approaches for calculating the subgrade resilient modulus and two approaches for calculating the effective structural number from the TSD results are compared. The temperature corrected centre deflection, surface curvature index and ratio of effective to required structural number are all calculated from the TSD data. Performance models are developed for two condition indexes and the International Roughness Index (IRI). In all cases, the addition of the structural indexes improved the quality of predictions, and the IRI prediction showed the most improvement from including the indexes. Ultimately, it was shown that the TSD testing results can be used to inform the pavement management process.

Evaluation of Effect of Structural Condition on Surface Deterioration for Flexible Pavements Using Traffic Speed Deflection Device

With the introduction of traffic speed deflection devices (TSDD), collecting network-level surface deflection data becomes more efficient and convenient. State highway agencies (SHAs) are paying more attention to the incorporation of structural information into pavement management systems to support maintenance and rehabilitation (M&R) analysis. For those SHAs who conduct M&R analysis based solely on surface condition data, it is crucial to understand how the inclusion of structural condition data may affect the performance model, which structural indices may be well-suited for their needs, and where these indices may be placed in their current flowchart of M&R analysis. To address these issues, in this study, the effects of eight structural indices obtained from TSDD on surface condition deterioration were investigated using TSDD data and surface condition data collected in Tennessee. The effects of structural indices on surface deterioration were evaluated using multiple regression, classification and regression tree analysis, and 1-year deterioration curve analysis. Surface curvature index, SCI_12, was found to be a significant factor influencing surface deteriorations. Structural indices representing condition of base layers and subgrade were less sensitive to the surface deterioration. To facilitate implementation, a flowchart of how to incorporate structural indices into M&R analysis procedure was proposed.

Traffic Speed Deflectometer for Network-Level Pavement Management in Tennessee

The traffic speed deflectometer (TSD) overcomes the limitations of the falling weight deflectometer (FWD) with respect to traffic interruption and test inefficiency and is considered the preferred method for network-level pavement structural evaluation. However, some challenges exist when applying the TSD to routine survey work. This study evaluated the long-term repeatability of TSDs and compared TSD measurements with FWD measurements to examine device-related errors in TSD measurements. In addition, a framework for incorporating TSD measurements into the pavement management system (PMS) was developed by introducing deflection bowl parameters (DBPs). All data analyses were based on TSD data collected in Tennessee. From the current study, the repeatability of the TSD has not been fully validated because of device-related errors. Since TSD measurements and FWD measurements are broadly similar, periodic validation of TSDs by FWDs is recommended for quality control. TSDs provide valuable information about pavement structural conditions that are not reliably or adequately reflected in the current PMS (which focuses mainly on pavement functional conditions). DBPs are a simple and fast method for making initial estimates of pavement structural conditions without the need for layer thickness. In the proposed DBP system, the surface curvature index (SCI_12) and D60 (TSD deflection at 60 in. before the load center) represent the structural strength of the asphalt layer and subgrade, respectively. The combination of pavement functional conditions and TSD-measured structural conditions (PMS+DBPs) may help to make more accurate predictions of pavement performance and to make more appropriate decisions on maintenance and rehabilitation strategies.

A new methodology to assess the remaining service life of motorway pavements at the network level from traffic speed deflectometer measurements

ABSTRACT Road agencies are looking for efficient pavement management tools to maintain adequate service levels and preserve the asset value. The traffic speed deflectometer (TSD) allows to continuously assess the structural condition of the pavement while moving at traffic speeds, thus overcoming the typical drawbacks of traditional non-destructive testing equipment. However, managing the huge amount of data deriving from TSD surveys is challenging, and there is no established method to use those data for identifying the remaining service life (RSL) of the pavement network. This study aimed at developing a new practical and reliable methodology to assess the RSL of motorway pavements at the network level from TSD measurements. The methodology is based on the use of the surface curvature index SCI300 and includes the temperature adjustment, the definition of a representative SCI300 value for each pavement section and the estimation of the residual fatigue resistance (considering bottom-up cracking as the critical distress). The methodology was implemented using the deflection data deriving from a TSD survey carried out on about 400 km of motorway pavement. The obtained RSL predictions can be used to identify the structural ranking of different pavement sections and guide budget forecasting, according to the actual maintenance and rehabilitation priorities.

Predicting Pavement Structural Condition Using Machine Learning Methods

State departments of transportation recognize the need to incorporate pavement structural condition in their pavement performance models and/or decision processes used to select candidate projects for preservation, rehabilitation, or reconstruction at the network level. However, pavement structural condition data are costly to obtain. To this end, this paper develops and evaluates the effectiveness of two machine learning methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), for predicting a flexible pavement’s structural condition. The aim is to be able to predict whether a pavement section’s structural condition is poor or not based on Annual Average Daily Traffic (AADT), truck percentage, and speed limit. The structural condition of a pavement is considered poor if the Surface Curvature Index (SCI12) is above 3.3. The models are developed using 950 miles of Traffic Speed Deflectometer (TSD) data collected along 8 primary routes in South Carolina. The performance of the machine learning models was compared with that of a logistic regression model. When the trained models are applied to the test data, the prediction results indicated that the XGBoost and RF models outperform the logistic regression model by 12% and 8%, respectively. XGBoost outperformed RF by 4%. With XGBoost found to be the best among the three models evaluated, its performance was examined using other poor structural condition threshold values; its prediction accuracy is found to be robust across the different scenarios. AADT and truck percentages are found to be significant factors whereas speed limit has no effect on a pavement’s structural condition.

Field investigation and numerical analysis of an inverted pavement system in Tennessee, USA

Inverted pavement system has been introduced for many years with field investigations, laboratory studies and numerical simulation works. Field investigations of inverted pavement structures have not been widely conducted and are very limited in the USA. This study presents a comprehensive field study on a full-scale inverted pavement as well as a conventional control pavement section constructed in Tennessee, USA. A combination of multiple non-destructive testing (NDT) methods, including ground penetration radar (GPR), Benkelman beam test, 3D road profiling test by laser crack measurement system (LCMS), and falling weight deflectometer (FWD) tests were utilized to assess the actual thickness, structural capacity and surface conditions of the pavement structures. The road surface profiling test results showed that the inverted pavement outperformed the conventional section in roughness, cracking and rutting conditions. Deflection Basin Parameters (DBPs) such as surface curvature index (SCI), base damage index (BDI), base curvature index (BCI) and W7 (7th sensor’s value of FWD) values were compared to study the layers’ condition of the inverted and conventional pavement sections. In addition, a preliminary simulation analysis by a Finite Element Method (FEM) model was conducted to compare the performance of inverted and conventional pavement sections under FWD load. The simulation results showed that the inverted pavement structure could reduce the potential of crack initiation and propagation due to smaller tensile stress at the bottom of the asphalt concrete layer (AC). Also, the smaller deflection was measured at the top of subgrade soil (SG), which reduced the total deflection of the inverted pavement structure. Furthermore, the cement-treated base (CTB)-constrained unbound aggregate base (UAB) acted as a cushion to relieve the tension from CTB and support the AC layer. The stress-dependent property of UAB in the inverted pavement contributed to the performance and ductility of inverted pavement but had little influence on the conventional pavement.

Deriving Pavement Deflection Indices from Layered Elastic Theory

Pavement deflection indices such as the surface curvature index or base damage index are widely used to characterize the condition of pavements. Often, the indices are constructed to provide an estimate of a particular strain component somewhere in the pavement structure. Since the pavement damage is a function of the strain, structural indices provide a simple way of estimating the damage rate in a pavement. Despite their widespread use, it has so far proven difficult to derive deflection indices from first principles; instead, new indices are found by evaluating a range of candidates using a trial-and-error approach. In this paper, a systematic method of deriving deflection indices is presented. The method is based on applying the convolution theorem for Hankel transforms to the solution of a layered elastic problem. Besides allowing for easy derivation of new deflection indices, the method can be used to account for the impact of varying layer thicknesses in the pavement.

Prediction of airfield pavement responses from surface deflections: comparison between the traditional backcalculation approach and the ANN model

This study investigated traditional and new approaches for predicting airfield pavement responses from surface deflections measured under Heavy Weight Deflectometer (HWD) testing conducted at National Airport Pavement Testing Facility (NAPTF). In the traditional approach, pavement layer moduli were backcalculated and then pavement responses were predicted based on the multilayer elastic (MLE) theory and the finite element (FE) method. In the new approach, an Artificial Neural Network (ANN) model was developed to predict the pavement response directly from surface deflections without backcalculation. The ANN model was trained using the synthetic database that was built based on the FE simulation results using different combinations of material property, layer thickness, HWD loading magnitude, and pavement temperature. It was found that the backcalculated moduli of the asphalt surface layer were similar between MLE and FEM methods; however, discrepancies were observed for the backcalculated moduli of unbound materials. In general, the traditional approach of backcalculation and forward calculation overestimated tensile strain in asphalt layers, especially for the pavement section with a thin asphalt layer. On the other hand, the prediction accuracy of the ANN model was found better than the traditional method regarding field measurements. Further analysis of the ANN model showed that the Area Under Pavement Profile (AUPP) and Surface Curvature Index (SCI) had good correlations with critical tensile strain and shear strain in the asphalt layer, respectively.

Численное моделирование теплопередачи и переноса металла при электронно-лучевом аддитивном формообразовании с подачей присадочной проволоки

The development of a mathematical model provides an analysis of heat transfer and metal flow during wire-based electron-beam additive manufacturing described. The procedure for solving the heat equation for the metal in the solid phase and the Navier-Stokes equations in the liquid phase, based on the use of the finite-difference method and the predictor-corrector procedure is described. An algorithm for numerical approximation of the motion of the free surface of the melt, using the concept of the volume of fluid (VOF) is describer. The original method for calculating the effect of surface tension forces, based on the numerical calculation of the surface curvature index is proposed. The results of simulating the melting of a wire element localized above a substrate made of 316L steel are described

Temperature adjustment of Surface Curvature Index from Traffic Speed Deflectometer measurements

ABSTRACT Pavement structural condition evaluation plays an important role in assessing rehabilitation needs and making prudent investment decisions. Recently the use of Traffic Speed Deflectometer (TSD), is being considered for pavement structural evaluation especially for network level pavement management applications. Among various TSD data measurements and indices, Surface Curvature Index (SCITSD), is a widely-used index for assessing the structural condition of the AC layers. Since asphalt modulus is temperature sensitive, measured deflections and consequently the computed SCITSD are also affected by pavement temperature at the time of testing. For consistent pavement evaluation, SCITSD computed across sections and over time should be adjusted to a reference temperature. This study presents an applied method for adjusting SCITSD to a reference temperature. The method was based on a model developed from a dataset built from responses computed with dynamic-viscoelastic analyses under TSD moving load in a wide range of pavement properties. The method was field evaluated with TSD data from three locations within the US and one location each in Europe and Australia. The results showed that the proposed method can reasonably adjust SCITSD to a reference temperature which enables its application in pavement structural assessment and subsequent use in pavement management activities.

Precision of back-calculation analysis and independent parameters-based models in estimating the pavement layers modulus-Field and experimental study

The main objectives of this research were to present new method of back-calculation analysis and to compare the obtained results with destructive testing. Several sections in Shush-Andimeshk and Semnan-Damghan highways were evaluated by ground penetrating radar for estimating the layer thickness and falling weight deflectometer (FWD) at different load levels for estimating the elastic moduli of pavement layers. At the same sections, some cores were extracted and tested to measure the elastic modulus using the indirect tensile and dynamic triaxial methods for bound and unbound layers, respectively. The FWD data were analyzed by ELMOD6.0 software as a conventional back-calculation method. Furthermore, a new method was proposed by implementing a code using BASIC programming language and the obtained results were compared with those from ELMOD6.0 and laboratory results. Based on these investigations the proposed method had higher conformity to destructive testing results than conventional back-calculation method. In addition to back-calculation based models, models were developed based on the independent variables such as surface curvature index and base damage index. Using the latter models the layer modulus can be estimated without using the complicated back-calculation analysis methods.

Limits of agreement method for comparing TSD and FWD measurements

This article uses the limits of agreement (LOA) method to compare the falling weight deflectometer (FWD) and the traffic speed deflectometer (TSD), two pavement structural evaluation devices. The TSD measures deflection slope, whereas the FWD measures deflection. For this reason, measurements were converted to the surface curvature index (SCI) and the base damage index (BDI), which can be obtained from each device. The SCI and BDI agreement between the two devices was then evaluated. Although the relationship between the calculated SCI and BDI using both equipments is reasonably close to the line of equality, there is a significant variation and a bias in this relationship. For example, for an average SCI or BDI value of 300 μm, the bias was 30 μm (FWD values lower than TSD values), and the LOA was 380 μm.

Change of Bearing Capacity Characteristics of Asphalt Pavement

Time succession of data for monitored variable parameter of pavement state is the first step in development of degradation models and determination of degradation functions. Procedure used for characteristics of bearing capacity is described in the paper.Data from long time monitored (since 1995) test section on road network were used. First of all homogeneity of subgrade was tested. The deflections at distance of 1500 mm from load centre were used for this purpose. Data that met criteria of homogeneity were corrected taking into account influence of temperature. Two characteristics of bearing capacity (central deflection and surface curvature index) were evaluated in relation to number of heavy vehicles that had passed test section.Results showed that investigated characteristics of pavement bearing capacity changed in time. It was stated that tendency of development was not very clear because values oscillated in the narrow range. Generally saying the bearing capacity of tested pavement has not changed since 1995 significantly.

Use of FWD Deflection Basin Parameters (SCI, BDI, BCI) for Pavement Condition Assessment

Current research studies relationship between Falling Weight Deflectometer (FWD) deflection basin parameters and road pavement structural condition indicators, such as fatigue cracking and permanent deformations induced by low bearing capacity of pavement, and pavement equivalent E modulus (Eeq). The primary aim of the research was to develop limit values for deflection basin parameters: surface curvature index (SCI), base damage index (BDI) and base curvature index (BCI). Analyses of data derived from the Estonian Road Databank disproved the hypothesis of relationship between deflection basin parameters and pavement defects or rutting. Deflection basin parameters and back-calculated Eeq were found to be in good correlation. Strong relationships were found between upper layers indicators (SCI and BDI) and Eeq. Relationship between subgrade indicator BCI and Eeq, found in the research, was not very strong. Based on the aforementioned relationships, and the required min equivalent modulus of particular pavement, the equations to determine the limit values of deflection basin parameters for different types of pavements were developed. As the statistical analyses of such extensive database have been done for the first time in Estonia, the determined limit values have to be evaluated in practice and, if needed, corrected.

Implementation of Spring Load Restrictions Using a Deflection-Calibrated Thaw Index

The City of Ottawa, Canada, uses a Dynaflect to monitor the change in pavement strength during the spring thaw at 11 test sites selected to represent its arterial road network. As the average deflection of the sites approaches a threshold value, load restrictions are publicized by signage and advertisements in local newspapers. The restrictions remain in effect until deflections decrease below the threshold value. Local police equipped with portable weigh scales enforce the restrictions. The goal is to allow for the shortest possible restriction period and still ensure that the road infrastructure is protected. However, the decision to implement load restrictions is traditionally made too late, because prediction of thaw progression between weekly deflection testing is difficult. The city's pavements are occasionally subjected to full-load hauling during the initial and most critical portion of the thaw period. To predict the onset and progression of the spring thaw better, the thaw index (TI) was used in addition to deflection testing. The TI (as calibrated separately by the Minnesota Department of Transportation and Manitoba Transportation and Government Services, Canada) was analyzed to determine whether it would predict the same implementation date as did deflection data. Results suggested that calibration factors provided by either agency are sufficiently accurate in the Ottawa environment but the Manitoba calibration is slightly more precise. Future investigation will involve use of surface curvature index, as well as direct temperature measurements from the city's Road Weather Information Systems.

Part 1: Low-Volume Roads: Climatic and Seasonal Effects on Pavements and Safety: Implementation of Spring Load Restrictions Using a Deflection-Calibrated Thaw Index

The City of Ottawa, Canada, uses a Dynaflect to monitor the change in pavement strength during the spring thaw at 11 test sites selected to represent its arterial road network. As the average deflection of the sites approaches a threshold value, load restrictions are publicized by signage and advertisements in local newspapers. The restrictions remain in effect until deflections decrease below the threshold value. Local police equipped with portable weigh scales enforce the restrictions. The goal is to allow for the shortest possible restriction period and still ensure that the road infrastructure is protected. However, the decision to implement load restrictions is traditionally made too late, because prediction of thaw progression between weekly deflection testing is difficult. The city's pavements are occasionally subjected to full-load hauling during the initial and most critical portion of the thaw period. To predict the onset and progression of the spring thaw better, the thaw index (TI) was used in addition to deflection testing. The TI (as calibrated separately by the Minnesota Department of Transportation and Manitoba Transportation and Government Services, Canada) was analyzed to determine whether it would predict the same implementation date as did deflection data. Results suggested that calibration factors provided by either agency are sufficiently accurate in the Ottawa environment but the Manitoba calibration is slightly more precise. Future investigation will involve use of surface curvature index, as well as direct temperature measurements from the city's Road Weather Information Systems.

Eight-Year Field Performance of Secondary Road Incorporating Geosynthetics at Subgrade-Base Interface

In June 1994 an instrumented 150-m-long secondary road pavement section was built in Bedford County, Virginia. This pavement section was composed of nine individual segments each 15 m long. The nine sections include three groups with aggregate base layer thicknesses of 100, 150, and 200 mm. Three sections from each group were stabilized with geotextiles and three were stabilized with geogrids at the base-subgrade interface. The remaining three sections were kept as control sections. As part of the structural analysis, deflection data parameters such as the base damage index and surface curvature index calculated from falling weight deflectometer (FWD) data were analyzed after being corrected for temperature variations from the time of construction until October 2001. Performance criteria such as rutting measurements were also collected over the whole period. A nonlinear exponential model was used to describe the development of rutting versus cumulative equivalent single-axle loads for the 100-mm base course. A linear elastic program incorporating constitutive material properties was used to calculate vertical compressive stresses, which were used with FWD deflections to predict rutting rates with a mechanistic equation. The rutting rate results confirmed the separation function of geosynthetics that prevented the migration of fines from the subgrade to the base course layer and the penetration of the aggregate base layer into the subgrade. Rutting results, deflection data, and service life analysis showed that geosynthetically stabilized sections significantly improved the performance of the 100-mm base course sections.

A spherical representation for recognition of free-form surfaces

Introduces a new surface representation for recognizing curved objects. The authors approach begins by representing an object by a discrete mesh of points built from range data or from a geometric model of the object. The mesh is computed from the data by deforming a standard shaped mesh, for example, an ellipsoid, until it fits the surface of the object. The authors define local regularity constraints that the mesh must satisfy. The authors then define a canonical mapping between the mesh describing the object and a standard spherical mesh. A surface curvature index that is pose-invariant is stored at every node of the mesh. The authors use this object representation for recognition by comparing the spherical model of a reference object with the model extracted from a new observed scene. The authors show how the similarity between reference model and observed data can be evaluated and they show how the pose of the reference object in the observed scene can be easily computed using this representation. The authors present results on real range images which show that this approach to modelling and recognizing 3D objects has three main advantages: (1) it is applicable to complex curved surfaces that cannot be handled by conventional techniques; (2) it reduces the recognition problem to the computation of similarity between spherical distributions; in particular, the recognition algorithm does not require any combinatorial search; and (3) even though it is based on a spherical mapping, the approach can handle occlusions and partial views. >