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A recurrent challenge on cycle paths are edge cracks, which affect the traffic safety and accessibility of cyclists and produce high maintenance costs. Being both structurally thinner and narrower structures than roads, the cycle paths are extra prone to this problem. A few passages of heavy vehicles in unfavourable conditions might be enough to break the edge. The load-bearing capacity of eight municipal cycle paths in Linköping, Sweden, were assessed by falling weight deflectometer (FWD) and light falling weight deflectometer (LWD) measurements during a year-long cycle. A set of alternative Deflection Bowl Parameters (DBPs), better adapted to the structural design of cycle paths, were suggested and evaluated. The results of the FWD measurements showed that these suggested DBPs are a promising approach to evaluate the load-bearing capacity of cycle paths. From the results of the LWD measurements, it was found that the load-bearing capacity varies considerably with lateral position. The conclusion is that it might be more fruitful to measure the load-bearing capacity by LWD close to the edge, rather than the traditional approach of FWD measurements along the centre line of the cycle path. [ABSTRACT FROM AUTHOR]

Non-destructive testing (NDT) systems are essential tools and are widely used for assessing the condition and structural integrity of pavement structures without causing any damage. They are cost-effective, provide comprehensive data, and are time efficient. The bearing capacity and structural condition of a flexible pavement depends on several interrelated factors, with asphalt layers stiffness being dominant. Since asphalt mix is a viscoelastic material, its performance can be fully captured by the dynamic modulus master curve. However, in terms of evaluating an in-service pavement, although a dynamic load is applied and the time history of deflections is recorded during testing of FWD, only the peak deflection is considered in the analysis. Therefore, the modulus of stiffness estimated by backcalculation is the modulus of elasticity. While several methods have been introduced for the determination of the field dynamic modulus master curve, the MEPDG approach provides significant advantages in terms of transparency and robustness. This study focuses on evaluating the methodology's accuracy through an experimental study. The data analysis and validation process showed that routine measurements with the FWD and GPR, within the framework of a pavement monitoring system, can provide valuable input parameters for the evaluation of in-service pavements. [ABSTRACT FROM AUTHOR]

The successful management of road pavement maintenance requires the existence of suitable monitoring procedures for assessing pavement condition. A powerful tool for this is the use of non-destructive testing technologies. Non-destructive testing (NDT) aims to support the monitoring of pavement condition, as it enables constant and rapid collection of in situ data. Analyzing NDT data can result in the development of useful indexes that can be related to trigger values (criteria) to define pavement condition. This information can be used to assess the "health" of the pavement to decide whether intervention is required. However, to effectively support the implementation of pavement management measures, it is sometimes necessary to implement a pavement monitoring and assessment framework that can be adapted by road authorities on a case-by-case basis. To this end, this study addresses the development of a pavement monitoring and assessment procedure by integrating different NDT technologies to collect and evaluate data. The procedure, referred to as Integrated Testing and Evaluation (ITE), is proposed as an algorithm to find optimal strategies for prioritizing potential pavement interventions, considering the budget constraints for the required investigations. [ABSTRACT FROM AUTHOR]

In recent years, the increased use of heavy commercial vehicles with higher axle weights has required the development of innovative technologies to improve the mechanical properties of asphalt concrete conglomerates, such as fatigue resistance and rutting. This study offers a comprehensive comparative analysis of different types of asphalt concrete tested in four trial sections (S1, S2, S3, S4) of the SP3 Ardeatina rural road in Rome, under actual traffic and operational conditions. More precisely, the pavement technologies applied include modified asphalt concrete with graphene and recycled hard plastics for S1, asphalt concrete modified with styrene–butadiene–styrene (SBS) for S2, asphalt concrete with a standard polymeric compound for S3, and traditional asphalt concrete for S4. The evaluation approach involved visual inspections in order to calculate the pavement condition index (PCI) and falling weight deflectometer (FWD) tests. In addition, back-calculation analyses were performed using ELMOD software to assess the mechanical properties. The laboratory tests revealed superior properties of M1 in terms of its resistance to permanent deformations (+13%, +15%, and +19.5% compared to M2, M3, and M4, respectively) and stiffness (10,758 MPa for M1 vs. 9259 MPa, 7643 MPa, and 7289 MPa for M2, M3, and M4, respectively). These findings were further corroborated by the PCI values (PCIS1 = 65; PCIS2 = 17; PCIS3 = 28; PCIS4 = 29) as well as the FWD test results after 5 years of investigation, which suggests greater durability and resistance than the other sections. [ABSTRACT FROM AUTHOR]

The falling weight deflectometer (FWD) detection system benefits from its outstanding characteristics of no damage, fast speed, and high precision. The warping deformation of cement concrete pavement occurs due to the temperature difference along the depth of the slab, which makes FWD detect different results under different temperature fields. In this study, we systematically carried out the cement pavement's temperature field and deflection test. The experimental data were analyzed to obtain the temperature variation law of the top and bottom of the pavement slab every day. By establishing a three-dimensional finite element model of cement pavement with a multi-layer elastic foundation type, the influence of the temperature difference at the bottom of the slab on the deflection of the center point of the slab corner load under different working conditions, different seasons, different loads and whether there is polymer filling in the void area was studied. We summarize the correlation between the temperature difference and the influence coefficient and propose the cement pavement void identification and polymer grouting effect evaluation method considering the temperature effect. [ABSTRACT FROM AUTHOR]

This paper highlights the subgrade resilient modulus (MR), which is recognized as an important parameter to characterize the stiffness of the subgrade soil for designing flexible pavement. In this study, 18 thin-walled Shelby tube samples of fine-grained subgrade soils were collected from two sites in South Carolina (Laurens/SC-72 and Pickens/SC-93) and tested in the laboratory using AASHTO T307-99 to obtain the MR. In addition, falling weight deflectometer (FWD) tests were performed on the same pavement sections to obtain the back-calculated MR(FWD) per the AASHTOWare 2017 back-calculation tool. A subgrade MR catalog was established and used to select hierarchical Input Level 2 for Pavement Mechanistic-Empirical design (PMED) analysis (v 2.6.1). The PMED analysis was run for 20 years. The Mechanistic-Empirical Pavement Design Guide (MEPDG) and global calibration values were used to predict asphalt concrete (AC) pavement distresses (e.g., rutting, bottom-up fatigue, top-down fatigue, and transverse cracking) and International Roughness Index (IRI) for each pavement section. The predicted values were compared to the field-measured values to determine bias and the standard error of the estimate to validate each distress prediction model for local calibration. [ABSTRACT FROM AUTHOR]

The deep forest is a powerful deep-learning algorithm that has been applied in certain fields. In this study, a deep forest (DF) model was developed to predict the central deflection measured by a falling weight deflectometer (FWD). In total, 11,075 samples containing information related to pavement structure, traffic conditions, and weather conditions were extracted from the LTPP dataset. The performance of the DF model with custom backend settings was compared with that of models random forest (RF), multilayer perceptron (MLP), and DF built on the sklearn backend. All four deep-learning algorithms could identify the complex relationship between central deflection and relevant feature variables with high accuracy and stability. The learning and generalization abilities of DF was stronger than those of MLP and RF. The predictive performance and computation time of DF (custom) were better than those of DF (sklearn), indicating that the custom model was superior to the highly encapsulated model with sklearn as the backend. Feature importance analysis indicated that the drop load of FWD was the key factor influencing deflection. In addition, structural number, annual precipitation, and annual kilo equivalent standard axle load (kESAL) are very important features related with deflection. The feature importance of rehabilitation improvement thickness was less than the drop load, climatic factors, kESAL, structural number, and layer thickness. [ABSTRACT FROM AUTHOR]

Remaining life is an important indicator of pavement residual effective service time and is directly related to maintenance decision-making with limited funds. This paper proposes a fast and non-destructive model to predict the remaining life of rigid PCC (Portland cement concrete) pavement, with or without asphalt overlay. Firstly, a model was constructed according to the current Chinese design specifications for concrete pavement integrating an inverse design concept. Secondly, the prediction model was applied to three typical pavement sections with 1430, 1250 and 1000 slabs, respectively. Ground penetrating radar (GPR) was utilized to determine the geometric parameters in the predictive model and the physical state of the pavement. A falling weight detector (FWD) was utilized for determination of the mechanical parameters. A more reasonable equivalent elastic modulus of foundation was back-calculated instead of using the limited model in the design specification. Thirdly, the remaining life was predicted based on the current mechanical and geometric parameters. The distributions of the remaining life of the three pavement sections was statistically analyzed. Finally, a decision-making system to inform maintenance strategy was proposed based on the remaining life and the technical condition of each slab. The results showed that the relationship between the remaining life and the mechanical parameters, geometric parameters and the physical state of the pavement was highly consistent with engineering experience. The success rate of the prediction model was as high as 96%. The proposed fast and non-destructive prediction model showed good engineering applicability and feasibility. The decision-making system was shown to be feasible in terms of economic benefits. [ABSTRACT FROM AUTHOR]

The falling weight deflectometer (FWD) is a widely used nondestructive test (NDT) device in pavement infrastructure. A FWD test measures the surface deflections subjected to an applied impact loading and the modulus of pavement layers can be determined by back-calculating the measured deflections. However, the modulus of asphalt layers is significantly influenced by temperature; hence, the temperature correction must be considered in back-calculation to evaluate the moduli of asphalt layers at a reference temperature. In addition, the in situ temperature at various pavement depths is difficult to measure. A model for evaluating the temperature at various depths must be established to estimate the in situ temperature of asphalt layers. This study collected the temperature data from a FWD test site to establish a temperature-evaluation model for various depths. The cored specimens from the test site were obtained to conduct dynamic modulus tests for asphalt layers. The FWD tests were applied at the FWD test site and the back-calculation was performed with temperature correction using the frequency-temperature superposition principle. The back-calculated moduli of asphalt layers were compared with the master curve of dynamic modulus to verify the application of the frequency-temperature superposition principle for FWD back-calculation. The results show that the proposed temperature-evaluation model can effectively evaluate the temperature at various depths of pavement. Moreover, the frequency-temperature superposition principle can be effectively employed to conduct temperature correction for FWD back-calculation. [ABSTRACT FROM AUTHOR]

Nondestructive tests of road pavements are among the most widely used methods of pavement condition diagnostics. Deflections of road pavement under a known load are most commonly measured in such tests, e.g., with the use of falling weight deflectometer (FWD). Measured values allow to determine the material parameters of the road structure, corresponding to the obtained results, by means of backcalculations. Among the factors that impact on the quality of results is the accuracy of deflection measurement. Deflection basins with small differences of displacement values may correspond to significantly different combinations of material parameters. Taking advantage of them for mechanistic calculations of road pavement may eventually lead to incorrect estimation of the remaining fatigue life and then inadequate selection of pavement reinforcement. This study investigated the impact of measurement errors on the change of the obtained values of stiffness moduli of flexible road pavement layers. Additionally, the influence of obtained material parameters on the values of key pavement strain, and consequently on its design fatigue life was presented. [ABSTRACT FROM AUTHOR]

The last 20-year announcement predicts a 3.5% increase in the number of yearly passengers which will result in the doubling of the number of passengers in air transport by 2037. Such anticipation indicates the need for efficient monitoring of airport infrastructure as the support of opportune and efficient maintenance works. The novelties of this article are a process model of maintenance and monitoring, suitable for smaller and less burdened airports, and the methodology of monitoring of runways by implementation of the geodetic and geomechanics falling weight deflectometer (FWD) method. In addition, the results confirm the assumption that a specific environment such as an airport allows for sufficiently reliable determination of deformation areas or areas of vertical deviations of runways in a relative short time period available for measurements by using geodetic methods only or by combining other methods; our research model includes the FWD method. With the research, we have also shown there is an interaction between deformations or areas of vertical deviations on the surface and anomalies in the runway lower constructure which will, hereinafter, allow the development of the prediction, creating a vertical deviations or deformation model. [ABSTRACT FROM AUTHOR]