Search Results (1,002)

With the rapid development of China’s transportation network, the demand for bridge construction is increasing, the traffic volume is increasing yearly, and the average vehicle speed and the frequency of overloaded vehicles crossing bridges are soaring. When a vehicle passes over a highway bridge, it can easily form a coupling vibration between the vehicle and bridge due to the excitation of the expansion joint, the unevenness of the bridge deck, and the existing coating-hole. The impact effect is significant, which seriously affects the operation safety of both the vehicle and bridge, seriously damaging the service life of the bridge. Due to the influence of construction technology, it is common for the vibration to meet transverse and longitudinal expansion joints of a prefabricated girder bridge, where an aging bridge deck frequently results in bulges and potholes in asphalt pavement. The bridge vibration amplification effect under the dynamic load of heavy, high-speed vehicles is significant, and research about the large impact coefficient of bridges with local pavement deterioration is urgently needed. This study used SIMULINK simulation software and involved conducting several bridge model tests. Dynamic simulation analyses and running vehicle tests on scaled and real bridge models were carried out to study the coupling vibration response of bridge decks in the presence of different pothole sizes. The results show that the impact effect of low-speed vehicles passing through a larger-sized pothole is relatively significant, and the impact coefficient can be amplified to 214% of the original value under good road surfaces in extreme cases. The vehicle–bridge coupling impact effect of potholes is similar to bulges. This relevant work could provide suggestions for the operational performance evaluation and maintenance of bridges with local pavement deterioration. Full article

Pavement texture and skid resistance are pivotal surface features of roadway to traffic safety, especially under wet weather. Engineering interventions should be scheduled periodically to restore these features as they deteriorate over time under traffic polishing. While many studies have investigated the effects of traffic polishing on pavement texture and skid resistance through laboratory experiments, the absence of real-world traffic and environmental factors in these studies may limit the generalization of their findings. This study addresses this research gap by conducting a comprehensive field study of pavement texture and skid resistance under traffic polishing in the real world. A total of thirty pairs of pavement texture and friction data were systematically collected from three distinct locations with different levels of traffic polishing (middle, right wheel path, and edge) along an asphalt pavement in Oklahoma, USA. Data acquisition utilized a laser imaging device to reconstruct 0.01 mm 3D images to characterize pavement texture and a Dynamic Friction Tester to evaluate pavement friction at different speeds. Twenty 3D areal parameters were calculated on whole images, macrotexture images, and microtexture images to investigate the effects of traffic polishing on pavement texture from different perspectives. Then, texture parameters and testing speeds were combined to develop friction prediction models via linear and nonlinear methodologies. The results indicate that Random Forest models with identified inputs achieved excellent performance for non-contact friction evaluation. Last, the friction decrease rate was discussed to estimate the timing of future maintenance to restore skid resistance. This study provides more insights into how engineers should plan maintenance to restore pavement texture and friction considering real-world traffic polishing. Full article

The Qinghai–Tibet Plateau (QTP) is the highest altitude plateau in the world, characterized by strong solar radiation and large diurnal temperature differences and so on, which brings a great negative impact on the temperature and thermal stress field of asphalt pavement. The purpose of this study is to analyze the temperature field and thermal stress status of asphalt pavement in the QTP to provide a reference for pavement design and maintenance in high-altitude areas. The finite element method was applied to establish the temperature field model to study the distribution and variation of pavement temperature. On this basis, the influence of cooling amplitude on pavement thermal stress was studied during cold waves. In addition to this, the key internal factors affecting the thermal stress of pavement, such as surface thickness, surface temperature shrinkage coefficient, surface modulus, and base modulus, were analyzed by an orthogonal test. It was found that temperature and solar radiation have a significant effect on the pavement temperature field. When the cold wave came, the cooling rate had a considerable impact on the thermal stress of the pavement, that is, every 5 °C increase in cooling rate would increase the thermal stress by more than 50%. The temperature shrinkage coefficient and surface modulus of the surface layer material had the greatest influence on the pavement thermal stress. The thermal stress could be reduced by more than 0.4 Mpa for every 5 × 10⁻⁶/°C reduction in the surface temperature shrinkage coefficient or every 1000 Mpa reduction in the surface modulus. This study can provide a reference for improving the temperature field and thermal stress field of asphalt pavement in the plateau area. Full article

Asphalt pavements are fundamental to modern transportation infrastructure, requiring elasticity, firmness, and longevity. However, traditional asphalt, based on bitumen, faces several limitations. To improve pavement performance, polymer resins are being used to substitute bitumen and improve requirements. Therefore, a deep understanding of the material behavior is required. This study presents the analysis of the relaxation behavior of a poly(methyl methacrylate)-based pavement and the influence of mineral fillers. An approach using a linear elastic–viscoelastic material model was selected based on evidence and validated across the linear and nonlinear deformation range. The results reveal no influence of the mineral fillers on the relaxation behavior. The presented modification of the linear elastic and viscoelastic modeling reveals accurate results to predict long-term pavement performance. This approach offers a practical method for forecasting asphalt behavior. Further research is needed to incorporate deformation behavior into the model. Full article

Detection of pavement diseases is crucial for road maintenance. Traditional methods are costly, time-consuming, and less accurate. This paper introduces an enhanced pavement disease recognition algorithm, MS-YOLOv8, which modifies the YOLOv8 model by incorporating three novel mechanisms to improve detection accuracy and adaptability to varied pavement conditions. The Deformable Large Kernel Attention (DLKA) mechanism adjusts convolution kernels dynamically, adapting to multi-scale targets. The Large Separable Kernel Attention (LSKA) enhances the SPPF feature extractor, boosting multi-scale feature extraction capabilities. Additionally, Multi-Scale Dilated Attention in the network’s neck performs Spatially Weighted Dilated Convolution (SWDA) across different dilatation rates, enhancing background distinction and detection precision. Experimental results show that MS-YOLOv8 increases background classification accuracy by 6%, overall precision by 1.9%, and mAP by 1.4%, with specific disease detection mAP up by 2.9%. Our model maintains comparable detection speeds. This method offers a significant reference for automatic road defect detection. Full article

Pavement condition monitoring is an important task in road asset management and efficient abnormal pavement condition detection is critical for timely conservation management decisions. The present work introduces a mobile pavement condition monitoring approach utilizing low-cost sensor technology and machine-learning-based methodologies. Specifically, an on-board unit (OBU) embedded with an inertial measurement unit (IMU) and global positioning system (GPS) is applied to collect vehicle posture data in real time. Through a comprehensive analysis of both time domain and frequency domain data features for both normal and abnormal pavement conditions, feature engineering is conducted to identify how the most important features affect abnormal pavement condition recognition. Six machine learning models are then developed to identify different types of pavement conditions. The performance of different algorithms and the significance of different features are then analyzed. Moreover, the influence of vehicle speed on pavement condition assessment is further examined and classification models for different speed intervals are developed. The results indicate that the random forest (RF) model that considers vehicle speed achieves the best performance in pavement condition monitoring. The outcomes of the present work would contribute to cost-effective pavement condition monitoring and provide an important reference for pavement maintenance sectors. Full article

Abrasion wear is a significant concern for cutting tools, particularly when milling asphalt concrete due to the presence of hard mineral aggregate particles. The pressure exerted on the cutting tool by the chipped material and the resulting cutting forces directly influence tool wear. To estimate the cutting forces in asphalt milling, the authors propose using either laboratory experiments or cost-effective Discrete Element Method (DEM) modeling—by simulating the real conditions—as direct measurement under real conditions is challenging. This article presents results from an original experimental program aimed at determining the cutting forces during asphalt pavement milling. A specialized stand equipped with a moving plate and recording devices was designed to vary milling depth, rotational speed, and advance speed. The experimental results for horizontal force values were compared with numerical results from DEM modeling. It was found that both increasing the milling depth and the advance speed lead to higher cutting forces. Generally, DEM modeling trends align with experimental results, although DEM values are generally higher. The statistical analysis allowed identification of the milling depth as the most significant parameter influencing cutting force and the optimal combination of milling parameters to achieve minimum horizontal force acting on cutting tooth, namely, 15 mm milling depth and 190 mm/min advanced speed. Full article

The mounting impacts of climate change on infrastructure demand proactive adaptation strategies to ensure long-term resilience. This study investigates the effects of predicted future global warming on asphalt binder performance grade (PG) selection in the United States using a time series method. Leveraging Long-Term Pavement Performance (LTPP) data and Superpave protocol model, the research forecasts temperature changes for the period up to 2060 and calculates the corresponding PG values for different states. The results reveal significant temperature increases across the majority of states, necessitating adjustments in PG selection to accommodate changing climate conditions. The findings indicate significant increases in average 7-day maximum temperatures across the United States by 2060, with 38 out of 50 states likely to experience rising trends. Oregon, Utah, and Idaho are anticipated to face the largest temperature increases. Concurrently, the low air temperature has risen in 33 states, with notable increases in Maine, North Carolina, and Virginia. The widening gap predicted between required high and low PG poses challenges, as some necessary binders cannot be produced or substituted with other grades. The study highlights the challenge of meeting future PG requirements with available binders, emphasizing the need to consider energy consumption and CO₂ emissions when using modifiers to achieve the desired PG properties. Full article

Warm mix flame retardant asphalt mixture can reduce the energy dissipation and harmful gas emissions during asphalt pavement construction, as well as mitigate the adverse effects of road fires. For this, this paper studies the design and performance of a mixture modified with a combination of warm mix agent and flame retardant, and the pavement performance and flame retardancy of the modified mixture are evaluated. Additionally, a flame retardancy prediction model based on the radial basis function (RBF) neural network model is established. On this basis, the principal components analysis (PCA) model is used to analyze the most significant evaluation indicators affecting flame retardancy, and finally, a three-dimensional finite element model is developed to analyze the effects of loading on the pavement structure. The results show that compared to virgin asphalt mixture, the modified mixture shows a reduction in mixing and compaction temperatures by approximately 12 °C. The high-temperature performance of the mixture is improved, while the low-temperature performance and moisture stability slightly decrease, but its flame retardancy is significantly enhanced. The RBF neural network model revealed that the established flame retardancy prediction model has a high accuracy, allowing for precise evaluation of the flame retardancy. Finally, the PCA model identified that the combustion time has a significant effect on the flame retardancy of the asphalt mixture, and the finite element model revealed that the displacements of the warm mix fire retardant asphalt mixture were lower than virgin asphalt mixture in all directions under the loading. Full article

The effect of moisture on the fracture resistance of asphalt concrete is a significant concern in pavement engineering. To investigate the effect of the water vapor concentration on the fracture properties of asphalt concrete, this study first designed a humidity conditioning program at the relative humidity (RH) levels of 2%, 50%, 80%, and 100% for the three types of asphalt concrete mixtures (AC-13C, AC-20C, and AC-25C).The finite element model was developed to simulate the water vapor diffusion and determine the duration of the conditioning period. The semi-circular bending (SCB) test was then performed at varying temperatures of 5 °C, 15 °C, and 25 °C to evaluate the fracture energy and tensile strength of the humidity-conditioned specimens. The test results showed that the increasing temperature and the RH levels resulted in a lower peak load but greater displacement of the mixtures. Both the fracture energy and tensile strength tended to diminish with the rising temperature. It was also found that moisture had a significant effect on the tensile strength and fracture energy of asphalt concrete. Specifically, as the RH level increased from 2% to 100% (i.e., the water vapor concentration rose from 0.35 g/m³ to 17.27 g/m³), the tensile strength of the three types of mixtures was reduced by 34.84% on average, which revealed that the water vapor led to the loss of adhesion and cohesion within the mixture. The genetic expression programming (GEP) model was developed to quantify the effect of water vapor concentrations and temperature on the fracture indices. Full article

In recent years, applying slag micro-powder as a substitute for cement in preparing alkali-activated slag cement stabilized sand (AASCSS) mixtures has become increasingly widespread. In the severe cold regions of Xinjiang, multi-objective optimization of the mechanical and frost resistance properties of AASCSS is particularly crucial. This paper adopts slag micro-powder to replace Portland cement, together with lime and desulfurization gypsum as activators, to explore the effects of activator type and dosage on the mechanical and frost-resistance properties of AASCSS. A prediction model for the mechanical and frost-resistance properties of AASCSS based on projection-pursuit regression (PPR) was proposed and established. Using the developed PPR model, contour plots of the comprehensive performance were calculated, simplifying the multi-objective problem into two single-objective problems focusing on mechanical and frost resistance properties for analysis. This method avoids subjective weighting and hypothesis-based modeling. By analyzing the contour plots of comprehensive performance, the optimal performance indices for mechanical and frost–thaw properties and the corresponding types and dosages of activators can be directly determined. When the required 7-day unconfined compressive strength in road engineering is 5.6 MPa, the optimal value of the freeze–thaw performance index (BDR) is 94.08%. At this point, the corresponding lime content is 2.1%, and the desulfurization gypsum content is 3.3%. The research results provide a reference for applying slag to road-based materials. Full article

Improving the durability of flexible pavements and constructing new roads on weak soil foundations present significant challenges, prompting designers to explore alternative methods to prolong pavement lifespan. Geosynthetics have emerged as a promising solution for soil stabilization, with various materials developed for this purpose. The current study concentrates on using the finite element (FE) method to examine the effectiveness of geogrid-incorporated flexible pavements on soft soil substrates. A three-dimensional layered pavement is constructed with an FE model, incorporating subgrade layers of varying strengths based on their California bearing ratio (CBR) values, with a geogrid layer implemented to enhance subgrade stability. Additionally, attention is also given to investigating the effect of base course thickness. The findings reveal that the geogrid layer primarily influences the formation of plastic strains in the subgrade rather than resilient strains, effectively reducing vertical compressive strain by approximately 40%. With increasing CBR values, there is a reduction in vertical strain, although the influence zone extends up to a depth of 300 mm within the subgrade. At the surface of the subgrade, vertical strain decreases by around 17%, 39%, and 49% as the CBR values increase from 1% to 3%, 5%, and 8%, respectively. Full article

In China, a substantial portion of highway asphalt pavements are no longer capable of accommodating increasing traffic volumes and necessitate renovation and expansion. Prior to commencing such activities, it is crucial to evaluate the performance of the existing asphalt pavements. This study developed a novel normal cloud framework integrating a comprehensive weighted indicator system for existing asphalt pavement. Five key performance indicators including riding quality index (RQI), rutting area ratio (R_r), cracking area ratio (C_r), patching area ratio (P_r), and pavement structural strength index (PSSI) were selected to holistically represent the pavement condition in highway renovation and expansion projects. Subsequently, a method was proposed to determine the weights of these indicators by integrating the analytic hierarchy process (AHP) and entropy. A normal cloud model was constructed to address data characteristics and representation of indicator fuzziness/randomness through digital cloud modeling. The model was applied to 12 sections of the Jingjintang Expressway (Tianjin section). The results revealed only one section where the normal cloud model differed from the pavement maintenance quality assessment (PQI) model. The 3D ground-penetrating radar detection results of this different section indicated that the normal cloud model more closely aligned with the road structure condition. Compared to absolute pass/fail criteria of the traditional PQI model, the cloud model offered enhanced sensitivity to define graded condition assessments essential for reconstruction planning and decision analysis. Therefore, the normal cloud model is more suitable for assessing the performance of existing asphalt pavements in highway reconstruction and extension projects compared to the PQI model. Full article

A roadway path is most commonly perceived as a 3-D element structure placed within its surrounding environment either within or outside urban areas. Design guidelines are usually strictly followed to ensure safe and comfort transportation of people and goods, but in full alignment with the terrain configuration and the available space, especially in urban and suburban areas. In the meantime, vehicles travelling along a roadway consume fuel and emit pollutants in a way that depends on both the driving attitude as well as the peculiar characteristics of road design and/or pavement surface condition. This study focuses on the environmental behavior of roadways in terms of fuel consumption, especially of heavy vehicles that mainly serve the purpose of freight transportation within urban areas. The impact of horizontal and vertical profiles of a roadway structure is theoretically considered through the parameters of speed and longitudinal slope, respectively. Based on theoretical calculations with an already developed model, it was found that the slope plays the most critical role, controlling the rate of fuel consumption increase, as an increase ratio of 2.5 was observed for a slope increase from 2% to 7%. The variation was less intense for a speed ranging from 25 to 45 km/h. The investigation additionally revealed useful discussion points for the need to consider the environmental impact of roadways during the operation phase for a more sustainable management of freight transportation procedures, thereby stimulating an ad hoc development of fuel consumption models based on actual measurements so that local conditions can be properly accounted for and used by road engineers and/or urban planners. Full article

In the structure of composite pavement, the formation of voids beneath concrete panels poses significant risks to structural integrity and operational safety. Ground-Penetrating Radar (GPR) detection serves as an effective method for identifying voids beneath concrete pavement panels. This paper focuses on analyzing the morphological features of GPR echo signals. Leveraging the GprMax numerical simulation software, a numerical simulation model for void conditions in concrete pavement is established by setting reasonable pavement structure parameters, signal parameters, and model space parameters. The reliability of the numerical simulation model is validated based on field data from full-scale test sites with pre-fabricated voids. Various void conditions, including different void thicknesses, sizes, shapes, and filling mediums, are analyzed. The main conclusions of the study are as follows: the correlation coefficient between measured and simulated signals is above 0.8; a noticeable distinction exists between echo signals from intact and voided structures; signals exhibit similar phase and time delays for different void thicknesses and sizes but significant differences are observed in the A-scan signal intensity, the signal intensity, and the width of the B-scan signal; the impact of void shape on GPR echo signals mainly manifests in the variation of void thickness at different measurement points; and the relationship between the dielectric properties of the void-filling medium and the surrounding environment dictates the phase and time delay characteristics of the echo signal. Full article