Search Results (71)

Recycled asphalt pavement (RAP) can support traffic loads comparable to those of roads constructed with conventional materials. The structural evaluation of RAP is performed through the deflection generated by vehicles via recoverable deflection in the pavement layers. The deflection record is translated into a curve that geometrically interprets the behavior of the layers that make up the pavement. In this study, a falling weight deflectometer (FWD) was used to emulate transit loads and measure deflection in two models. Both contained 30% RAP, and one of them had fiberglass geogrid in the center of the asphalt layer. Through normalized maximum deflection (limit value based on constant stress), the structural index (SI), and the dynamic stiffness modulus (DSM), the structural behavior of the models under different load levels was evaluated. The pavement structure exhibited similarities in strength for both models subjected to impact. The presence of the geogrid reinforcement (Z1) showed structural index values ranging between 0.17 and 0.54, while the layer without geogrid (Z2) presented structural index values in a range of 0.23 to 0.78. In addition, the dynamic stiffness modulus presented a difference of 10 kN/mm between the maximums of the models in favor of reinforcement with glass fiber geogrid. Therefore, low structural index values are associated with the interaction between RAP and geogrid, highlighting this combination as an innovative and functional system for road surfaces, while the dynamic stiffness modulus indicates the stability and structural integrity of sustainable pavement, which has the potential to extend its lifespan. Full article

The accurate prediction of the pavement structural modulus is crucial for maintenance planning and life-cycle assessment. While recent deep learning models have improved predictive accuracy using Falling Weight Deflectometer data, challenges remain in effectively structuring time-series inputs and ensuring robustness against noise measurement. This paper presents an integrated framework that combines systematic time-step modeling with perturbation-based robustness evaluation. Five distinct input sequencing strategies (Plan A through Plan E) were developed to investigate the impact of temporal structure on model performance. A hybrid Wide & Deep ResRNN architecture incorporating SimpleRNN, GRU, and LSTM components was designed to jointly predict four-layer moduli. To simulate real-world sensor uncertainty, Gaussian noise with ±3% variance was injected into inputs, allowing the Monte-Carlo-style estimation of confidence intervals. Experimental results revealed that time-step design plays a critical role in both prediction accuracy and robustness, with Plan D consistently achieving the best balance between accuracy and stability. These findings offer a practical and generalizable approach for deploying deep sequence models in pavement modulus prediction tasks, particularly under uncertain field conditions. Full article

Traditional damage detection methods of prestressed concrete box girder bridges have low efficiency and cannot quantify the structure’s internal damage. We used an inversion method and a falling weight deflectometer to estimate the mechanical parameters of prestressed box girder bridges. A finite element model of the bridge dynamics under impact loading was established. A perturbation-based update was conducted, and a multi-parameter inversion algorithm was constructed. The measured data were used for the efficient identification of the bridge’s elasticity modulus and the prestressing tensile force. The theoretical validation indicated a high modeling accuracy and inversion efficiency, with a convergence accuracy within 1%. The initial value had a minimal influence on the inversion results. The engineering application showed that the maximum error of the elastic modulus between the inversion and the rebound methods was 1.55%. The loss rates of the deck slab’s elastic modulus and the prestressing force obtained from the inversion were 4.39% and 7.64%, respectively. The proposed method provides a new strategy for evaluating damage to prestressed box girder bridges. Full article

Smart construction technology integrates artificial intelligence, Internet of Things, UAVs, and building information modeling to improve productivity and quality in construction. In road embankment earthworks, ground compaction quality is critical for structural stability and maintenance. This study proposes a methodology combining UAV photogrammetry with intelligent compaction quality management systems to evaluate surface flatness and compaction homogeneity in real-time. High-resolution UAV images were used to generate digital elevation models, from which surface roughness was extracted using terrain element analysis and fast Fourier transform. Local terrain changes were interpreted through contour gradient, outline gradient, and tangential gradient curvature analysis. Field tests were conducted at a pilot site using a vibratory roller, followed by four compaction quality assessments: plate load test, dynamic cone penetration test, light falling weight deflectometer, and compaction meter value. UAV-based flatness analysis revealed that, when surface flatness met the standard, a strong correlation was observed, with results from conventional field tests and intelligent compaction data. The proposed method effectively identified poorly compacted zones and spatial inhomogeneity without interrupting construction. These findings demonstrate that UAV-based terrain analysis can serve as a nondestructive real-time monitoring tool and contribute to automated quality control in smart construction environments. Full article

FWD is an important non-destructive testing instrument in the field of highways. It evaluates the pavement bearing capacity by continuously hammering the ground. However, due to noise interference, the current identification and extraction of the impact signals generated by the hammering are not accurate enough, which affects the calibration accuracy of the FWD results. To address this issue, this work proposes a novel method for impact point identification. The method integrates frequency domain filtering with gradient detection. Firstly, by analyzing the frequency domain characteristics of FWD impact signals using fast Fourier transform (FFT) and short-time Fourier transform (STFT), the primary response frequency band of the impact was identified. Subsequently, the impact signal segment was reconstructed using inverse fast Fourier transform (IFFT) to effectively suppress noise interference. Furthermore, gradient detection was employed to precisely determine the initiation moment of the impact. To validate the proposed method, a simulated acceleration signal incorporating interference noise was constructed. Comparative experiments were also conducted between traditional identification methods and the proposed method under high-noise conditions. The results demonstrate that the proposed method can accurately identify the impact point even under strong noise, thereby providing reliable data support for FWD measurements. This method exhibits strong environmental adaptability and can be extended to other engineering tests involving impact events and impact point identification. Full article

In contrast to maintaining asphalt pavements, maintaining healthy and functional concrete pavements is a much greater challenge due to the especially brittle nature of concrete, which may require a more complex rehabilitation plan. Thanks to nondestructive testing, noninvasive on-site inspections can be carried out to assess a pavement’s condition, with the falling weight deflectometer (FWD) being the most representative example. In this study, five toll stations with concrete pavements in operation, for which no long-term monitoring protocols existed yet, were evaluated mainly with deflectometric tests using the FWD. The objective of the study was to propose a methodological framework to support responsible decision-makers in the strategic management of concrete pavements at toll stations. To meet this aim, a test campaign was organized to evaluate the pavement condition of individual slabs or lanes, assess the durability of the slabs, and determine the efficiency of load transfer across joints and cracks. As a key finding, pavement slab deflections were found to exhibit a considerable range; in particular, a range of 50–1450 μm for the maximum deflection of the FWD was observed. This finding stimulated a distribution fitting analysis to estimate characteristic values and thresholds for common deflection indicators that were validated on the basis of pavement design input data. Finally, the study proceeded with the development of a conceptual approach proposing evaluation criteria for individual slab assessment and the condition mapping of in-service concrete pavements. Full article

In the context of the rapid advancement of reconstruction and expansion projects, there has been a significant increase in the demand for the inspection and evaluation of existing asphalt pavements. In order to enhance the efficiency and effectiveness of joint detection using 3D ground-penetrating radar and falling weight deflectometers, this study investigates non-destructive testing methods for existing asphalt pavements based on geostatistical correlation principles. The relationship between crack rate and deflection is analyzed using group average values. The characteristic sections division method based on the crack rate guideline was realized. Research on the prediction method for deflection using Kriging interpolation has been conducted. Research has revealed that there is a positive correlation between the crack rate and the deflection index. The principle of the singularity index can be employed to divide characteristic sections. The falling weight deflectometer is capable of conducting targeted testing in accordance with characteristic sections. Furthermore, the superior performance of Kriging interpolation in predicting deflection compared with linear interpolation has been demonstrated. According to the Kriging interpolation principle, the detection interval of slow lane deflection should not be more than 100 m. Kriging interpolation on one way lane of matrix data has the best effect, and it can predict deflection using a limited amount of slow lane and hard shoulder data. This facilitates analysis of the changing trend of the deflection index in cases where detection conditions are constrained. This method is of great significance for grasping the true performance status of the existing asphalt pavement structure. Full article

Fatigue cracking is a major issue in asphalt pavements, reducing their lifespan and increasing maintenance costs. This study develops an artificial neural network (ANN) model to predict the onset and progression of fatigue cracking. The model is calibrated utilizing Falling Weight Deflectometer (FWD) testing data, alongside essential pavement characteristics such as layer thickness, air void percentage, asphalt binder proportion, traffic loads (Equivalent Single Axle Loads or ESALs), and mean annual temperature. By analyzing these factors, the ANN captures complex relationships influencing fatigue cracking more effectively than traditional methods. A comprehensive dataset from the Long-Term Pavement Performance (LTPP) program is used for model training and validation. The ANN’s ability to adapt and recognize patterns enhances its predictive accuracy, allowing for more reliable pavement condition assessments. Model performance is evaluated against real-world data, confirming its effectiveness in predicting fatigue cracking with an overall R² of 0.9. This study’s findings provide valuable insights for pavement maintenance and rehabilitation planning, helping transportation agencies optimize repair schedules and reduce costs. This research highlights the growing role of AI in pavement engineering, demonstrating how machine learning can improve infrastructure management. By integrating ANN-based predictive analytics, road agencies can enhance decision-making, leading to more durable and cost-effective pavement systems for the future. Full article

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. Full article

Pavement service life is an important factor that affects both the whole life cost and carbon footprint of a pavement. The service life of a pavement is affected by several different parameters which can be broadly classified into climate conditions, binder and mixture properties, pavement design, workmanship, and maintenance strategies. The current practice for determining service life of pavements involves the use of pavement design tools, which are used while constructing a new pavement or performing a reconstruction/resurfacing or pavement maintenance. In addition, field measurements using ground penetration radar, falling weight deflectometer, traffic speed deflectometer, and other techniques are also used to assess the condition of an existing pavement. The information from these measurements is then combined with pavement design software to predict potential pavement service life. The accuracy of the predicted pavement service life is affected by the associated uncertainties in the parameters that affect pavement life. The following paper presents various approaches that could be potentially used to determine the associated uncertainties in the estimation of pavement service life. The various uncertainty quantification techniques have been applied to a specific design, and the outcomes are discussed in this paper. The Monte Carlo simulation method, a system-level uncertainty quantification technique, can estimate a probabilistic pavement service life. The other uncertainty quantification schemes are software specific and provide probabilistic life factors by assumed statistical distributions. Hence, the Monte Carlo simulation technique could be one potential method that can be used for estimating a generalized pavement service utilizing predictions from various design software. Full article

The article investigates the impact of applying a dynamic computational model that considers inertia forces on pavement deflections under rapidly changing loads over time. This study is particularly relevant to the modelling of falling weight deflectometer (FWD) testing. Initially, the article examines the deflection values obtained from computational models under loads with varying frequencies. In this context, considering inertia forces was significant for load durations shorter than 0.04 s. In such cases, the results of static and dynamic analyses differed considerably. One application of FWD measurement results is determining the stiffness moduli of pavement layers using backcalculation. The study explored the impact of incorporating inertia forces into the pavement model on the estimated values of stiffness moduli obtained via backcalculation. The results revealed differences of several percent between the stiffness moduli calculated using dynamic and static numerical models. Subsequently, the key pavement deformations and fatigue life were determined using the obtained moduli. Again, significantly different results were observed between dynamic and static cases. Based on these findings, it can be concluded that dynamic effects should not be ignored when using FWD testing for backcalculation. Additionally, the article addresses the sensitivity of backcalculation results, which is crucial for the accurate interpretation of the obtained data. Full article

The dynamic modulus of in-service asphalt pavements serves as a critical parameter for the computation of residual life and the design of overlays. However, its acquisition is currently limited to laboratory dynamic modulus testing using a limited number of core samples, necessitating a reassessment of its representativeness. To facilitate the prediction of dynamic modulus design parameters through Falling Weight Deflectometer (FWD) back-calculated modulus data, an integrated approach encompassing FWD testing, modulus back-calculation, core sample dynamic modulus testing, and asphalt DSR testing was employed to concurrently acquire dynamic modulus at identical locations under varying temperatures and frequencies. Dynamic modulus prediction models for in-service asphalt pavements were developed utilizing fundamental model deduction and gene expression programming (GEP) techniques. The findings indicate that GEP exhibits superior efficacy in the development of dynamic modulus prediction models. The dynamic modulus prediction model developed can enhance both the precision and representativeness of asphalt pavement’s dynamic modulus design parameters, as well as refine the accuracy of residual life estimations for in-service asphalt pavements. Concurrently, the modulus derived from FWD back-calculation can be transmuted into the dynamic modulus adhering to a uniform standard criterion, facilitating the identification of problematic segments within the asphalt structural layer. This is of paramount importance for the maintenance or reconstruction of in-service asphalt pavements. Full article

Over prolonged exposure to groundwater conditions, semi-rigid base asphalt pavements can undergo significant changes in their internal moisture field, resulting in substantial variations in the pavement’s stiffness and, consequently, affecting the overall load-bearing capacity and stability of the road structure. This paper employs FWD non-destructive testing equipment to assess its mechanical performance and conduct data analysis and conducts a mechanical response study of asphalt road surfaces, considering the influence of roadbed moisture levels. Using the dynamic analytical theory, the fundamental equations and stiffness matrices for a linear elastic half-space model were established, leading to the development of a computational model for the mechanical response of semi-rigid base asphalt pavements under FWD dynamic loading, with an examination of the surface deflection in relation to changes in groundwater levels. Numerical examples and engineering applications were employed to validate the proposed model. The research findings indicate: With the passage of time, surface deflection values initially increase and then decrease, exhibiting a sinusoidal variation pattern similar to that of the applied load. As the distance from the loading center increases, the moment of peak deflection continually lags behind. The average absolute relative error between the results obtained using the method proposed in this study and the traditional ABAQUS finite element method was only 0.70%. The correlation coefficient between the theoretically computed deflection curve and the measured deflection curve using the spectral element method was greater than 0.9, with an average absolute relative error of 4.92% between the theoretical peak deflection and the measured peak deflection. As the groundwater level rises, surface deflection noticeably increases, with an approximately 40% increase in deflection values at the loading center. These research findings can be utilized to analyze the dynamic deflection of semi-rigid base asphalt pavements under various groundwater conditions, providing significant practical value for assessing road structural performance and serviceability. Full article

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 (PCI_S1 = 65; PCI_S2 = 17; PCI_S3 = 28; PCI_S4 = 29) as well as the FWD test results after 5 years of investigation, which suggests greater durability and resistance than the other sections. Full article

To calculate and analyze the equivalent resilient modulus of a submerged subgrade, a constitutive model considering the effect of saturation and matrix suction was introduced using ABAQUS’s user-defined material (UMAT)subroutine. The pavement response under falling weight deflectometer (FWD) load was simulated at various water levels based on the derived distribution of the resilient modulus within the subgrade. The equivalent resilient modulus of the subgrade was then calculated using the equivalent iteration and weighted average methods. Based on this, the influence of the material and structural parameters of the subgrade was analyzed. The results indicate that the effect of water level rise on the tensile strain at the bottom of the asphalt layer and the compressive strain at the top of the subgrade is obvious, and its trend is similar to an exponential change. The equivalent resilient modulus of the subgrade basically decreases linearly with the rise in the water level, and there is high consistency between the equivalent iteration and weighted average methods. The saturated permeability coefficient and subgrade height have the most significant effect on the resilient modulus of the subgrade, which should be emphasized in the design of submerged subgrades, and the suggested values of the resilient modulus of the subgrade should be proposed according to the relevant construction conditions. Full article