Search Results (189)

Pavement performance models are considered a key element in pavement management systems since they can predict the future condition of pavements using historical data. Several indicators are used to evaluate the condition of pavements (such as the pavement condition index, rutting depth, and cracking severity), and the international roughness index (IRI), which is the most widely employed worldwide. This study aimed to develop an accurate IRI prediction model. Ten prediction methods were trained on a dataset of 35 independent variables. The performance of the methods was compared, and the light gradient boosting machine was identified as the best-performing method for IRI prediction. Then, the SHAP was synchronized with the best-performing method to prioritize variables based on their relative influence on IRI. The results suggested that initial IRI, mean annual temperature, and the duration between data collections had the strongest relative influence on IRI prediction. Another objective of this study was to determine the optimal uncertainty model for IRI prediction. In this regard, 12 uncertainty models were developed based on different conformal prediction methods. Gray relational analysis was performed to identify the optimal uncertainty model. The results showed that Minmax/80 was the optimal uncertainty model for IRI prediction, with an effective coverage of 93.4% and an average interval width of 0.256 m/km. Finally, a further analysis was performed on the outcomes of the optimal uncertainty model, and initial IRI, duration, annual precipitation, and a few distress parameters were identified as uncertain. The results of the framework indicate in which situations the predicted IRI may be unreliable. Full article

In recent decades, numerous studies have investigated the application of Artificial Intelligence (AI), and more precisely of Artificial Neural Networks (ANNs), in the prediction of complex technical parameters, particularly in the field of road infrastructure management. Among them, prediction of the widely used International Roughness Index (IRI) has attracted much attention due to its importance in pavement maintenance planning. This study focuses on predicting future IRI values using traditional regression models and neural networks, specifically Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) networks, on two highway sections, each analyzed in two experimental setups. The models consider only traffic and structural road characteristics as variables. The results show that the LSTM method provides significantly lower prediction errors for both highway sections, indicating better performance in capturing roughness trends over time. These results confirm that ANNs are a useful tool for engineers by predicting future IRI values, as they help to extend pavement life and reduce overall maintenance costs. The integration of machine learning into pavement evaluation is a promising step forward in ongoing efforts to optimize pavement management. Full article

Using a two-axle test vehicle, a new drive-by-based bridge damage identification method is proposed in this study. The method firstly obtains the vehicle acceleration response of a vehicle passing through an undamaged bridge and a damaged bridge; then, the acceleration response is processed using successive variational modal decomposition (SVMD) to obtain the intrinsic modal function (IMF) corresponding to the driving frequency; finally, the difference of the IMF is used to construct a damage indicator for damage identification of the bridge. The main findings of this study are as follows: (1) the constructed damage index can successfully identify single and multiple damages of bridges; (2) even in the case of pavement roughness, the proposed damage index is still able to identify the location of the damage; (3) the constructed damage index is not only applicable to simply supported bridges, but also applicable to the damage identification of continuous bridges; (4) the experiment shows that the proposed damage index can successfully identify the damage location, but the local vibration of the vehicle and the measurement noise interfere with the damage identification effect severely. Full article

The vehicle scanning method (VSM) is widely used for bridge damage identification (BDI) because it relies solely on vehicle dynamic responses. The recently introduced contact point response, which is derived from vehicle dynamics but devoid of vehicle-related natural frequencies, shows great potential for application in the vehicle scanning method. However, its application in bridge damage detection remains understudied. The aim of this paper is to propose a new bridge damage identification method based on the contact point response. The method uses variational modal decomposition (VMD) to solve the problem of mode mixing and spurious frequencies in the signal. The continuous wavelet transform (CWT) is then utilized for damage identification. The introduction of variational modal decomposition makes the extracted signal more accurate, thus enabling more accurate damage identification. Numerical simulations validate the method’s robustness under varying conditions, including the vehicle speed, wavelet scale factors, the number of bridge spans, and pavement roughness. The results demonstrate that variational modal decomposition eliminates signal artifacts, producing smooth variational modal decomposition–continuous wavelet transform curves for accurate damage detection. In this study, we offer a robust and practical solution for bridge health monitoring using the vehicle scanning method. Full article

Surface roughness is a key factor influencing the safety, comfort, and overall quality of bicycle lanes, which are increasingly integrated into urban transportation systems worldwide. This study aims to assess and quantify the roughness of bicycle lanes in Sejong City, Republic of Korea, by utilizing accelerometer-based sensor technologies. Five study sections (A–E) were selected to represent a range of road surface conditions, from newly constructed roads to severely deteriorated surfaces. These sections were chosen based on bicycle traffic volume and prior reports of pavement degradation. The evaluation of road surface roughness was conducted using a smartphone-mounted accelerometer to measure the vertical, lateral, and longitudinal accelerations. The data collected were used to calculate the Bicycle Road Roughness Index (BRI) and Faulting Impact Index (FII), which provide a quantitative measure of road conditions and the impact of surface defects on cyclists. Field surveys, conducted in 2022, identified significant variation in roughness across the study sections, with values of BRI ranging from 0.2 to 0.8. Sections with a BRI greater than 0.5 were considered unsafe for cyclists. The FII showed a clear relationship between bump size and cycling speed, with higher bump sizes and faster cycling speeds leading to significantly increased impact forces on cyclists. These findings highlight the importance of using quantitative metrics to assess bicycle lane conditions and provide actionable data for maintenance planning. The results suggest that the proposed methodology could serve as a reliable tool for the evaluation and management of bicycle lane infrastructure, contributing to the improvement of cycling safety and comfort. Full article

In this study, the feasibility of using three-dimensional (3D) printing technology to investigate the impact of macrotexture and microtexture on the skid resistance of asphalt pavement was verified. The macrotexture characteristics of the five types of real asphalt mixtures were captured, reconstructed, and printed. The comparison analysis of the skid resistance between the pavement and printed specimens was conducted, and the correlations and contribution proportions of the macrotexture and microtexture on skid resistance were also calculated. Results show that five printed asphalt mixtures present good consistency in the microtexture with a roughness of about 100 nm. The impact of thin water film on the skid resistance is insignificant for real asphalt mixtures, while it is significant for printed mixtures. The printed specimens under dry conditions show a similar British pendulum number (BPN) with the real pavement specimens under wet conditions, while the BPN under wet conditions for printed specimens are much smaller than the real ones but follows a similar variation trend. Mean profile depth (MPD) values of four printed asphalt concrete (AC) mixtures are well linearly correlated with their BPN under dry and wet conditions, especially for wet conditions with the R² of 0.91. The contribution proportion of macrotexture to the skid resistance is nearly 90% for the dry condition and about 50% for the wet condition. Full article

This work aims to investigate the effectiveness of road maintenance interventions by analyzing changes in the International Roughness Index (IRI) by means of crowdsourced connected vehicle data. For this purpose, 136 pavement maintenance interventions on a single lane were considered over a period between 2021 and 2024. A multiple linear regression model (R² = 0.780) has been employed as statistical tool to assess the relationship between pre/post-intervention IRI scores and various factors. Despite the fact that results showed a general improvement in pavement condition, the effectiveness of the interventions was found to be influenced by several factors. In particular, intervention on the middle lane appears to be the most effective for enhancing section characteristics, and the effectiveness of maintenance on the overall condition of the section tends to be reduced as the number of lanes increases. Additionally, maintenance appears to be less effective in improving post-maintenance performance as the initial IRI value increases; this suggests that severely deteriorated sections may require more extensive rehabilitation strategies. The ultimate aim of study is to provide evidence to support the inclusion of crowdsource vehicle data in Pavement Management Systems (PMSs) to optimize maintenance planning and resource allocation. Full article

Flooding can damage pavement infrastructure significantly, causing both immediate and long-term structural and functional issues. This research investigates how flooding events affect pavement deterioration, specifically focusing on measuring pavement roughness by the International Roughness Index (IRI). To quantify these effects, we utilized 20 years of pavement condition data from TxDOT’s PMIS database, which is integrated with flood event data, including duration and spatial extent. Statistical analyses were performed to compare IRI values before and after flooding and to calculate the deterioration rates influenced by flood exposure. Moreover, we applied explainable artificial intelligence (XAI) techniques, such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to assess the impact of flooding on pavement performance. The results demonstrate that flood-affected pavements experience a more rapid increase in roughness compared to non-flooded sections. These findings emphasize the need for proactive flood mitigation strategies, including improved drainage systems, flood-resistant materials, and preventative maintenance, to enhance pavement resilience in vulnerable regions. Full article

This study investigates the vehicle–bridge coupling vibration performance of corrugated steel web box girder bridges under three-dimensional pavement roughness conditions. To effectively account for these roughness characteristics, a three-dimensional contact constraint method is proposed. The accuracy of the proposed method is first verified, followed by an analysis of a 30 m span corrugated steel web box girder bridge to evaluate the influence of vehicle speed, pavement grade, roughness dimensions, and box girder configurations on the impact factor. The results show that the impact factor does not consistently increase with vehicle speed. As pavement conditions worsen, the impact factor shows an upward trend, with each grade of road surface deterioration resulting in an average 19.1% increase in the impact factor. In most scenarios, three-dimensional pavement roughness results in smaller impact factors compared to two-dimensional pavement roughness, with average reductions of 2.4%, 7.3%, and 13.5% for grade A, B, and C roads, respectively. Replacing the corrugated steel web with a flat steel web leads to an average reduction of 4.2% in the mid-span dynamic deflection of the bridge, despite the impact factors of both configurations being relatively similar. Substituting the concrete bottom slab with an equivalent steel bottom slab increases the mid-span dynamic deflection by an average of 28.4% and nearly doubles the impact factor. The impact factors determined by most national standards generally fall within the range for grade A pavement, suggesting that the calculation methods in these standards are mainly suited for newly constructed bridges or those in good maintenance. Full article

The increasing demand for sustainable road infrastructure necessitates alternative materials that enhance soil stabilization while reducing environmental impact. This study investigated the application of organosilane-based nanotechnology to improve the structural performance and durability of road corridors in Peru, offering a viable alternative to conventional stabilization methods. A comparative experimental approach was employed, where modified soil and asphalt mixtures were evaluated against control samples without nanotechnology. Laboratory tests showed that organosilane-treated soil achieved up to a 100% increase in the California Bearing Ratio (CBR), while maintaining expansion below 0.5%, significantly reducing moisture susceptibility compared to untreated soil. Asphalt mixtures incorporating nanotechnology-based adhesion enhancers exhibited a Tensile Strength Ratio (TSR) exceeding 80%, ensuring a superior resistance to moisture-induced damage relative to conventional mixtures. Non-destructive evaluations, including Dynamic Cone Penetrometer (DCP) and Pavement Condition Index (PCI) tests, confirmed the improved long-term durability and load-bearing capacity. Furthermore, statistical analysis of the International Roughness Index (IRI) revealed a mean value of 2.449 m/km, which is well below the Peruvian regulatory threshold of 3.5 m/km, demonstrating a significant improvement over untreated pavements. Furthermore, a comparative reference to IRI standards from other countries contextualized these results. This research underscores the potential of nanotechnology to enhance pavement resilience, optimize resource utilization, and advance sustainable construction practices. Full article

The influence of sidewalk paving materials on pedestrian safety and comfort remains an underexplored topic within the walkability literature. This pilot study aims to address this gap by evaluating the role of five surface-related attributes—roughness, friction, texture, heat retention, and maintenance—through a qualitative approach complemented by a simplified quantitative evaluation. The study was conducted along a pedestrian route in Braga, Portugal, where pedestrian perceptions were collected via a questionnaire and compared with objective measurements obtained at seven testing points with different paving materials. The results indicate a strong preference for concrete and mortar pavements due to their slip-resistant surfaces, smoothness, and overall regularity. Quantitative tests confirmed that these materials exhibited the highest slip resistance and surface regularity, reinforcing the general alignment between pedestrian perceptions and measured performance. Participants rated paving attributes higher than others, such as sidewalk width or obstacle-free paths. Notable demographic differences also emerged: women rated sidewalk attributes more highly than men, seniors preferred traditional stone pavements more, and adults favored concrete. These findings highlight the importance of integrating surface-related sidewalk attributes into walkability assessments and urban design strategies to promote safer, more comfortable, and more inclusive pedestrian environments. Full article

The characteristics of pavement texture are key determinants of skid resistance, directly affecting tire-pavement interactions. This study examines the relationship between separated pavement textures and friction coefficients under dry and wet conditions. Using 3D laser scanning, texture data were collected from 40 asphalt pavement sections in Nanchang. The data were processed through Fourier Transform and Butterworth filtering, enabling separation of macro- and micro-textures. Based on ISO 25178-2, 16 parameters—including S_a (Arithmetic Mean Height), S_tr (Texture Aspect Ratio), V_mc (Core Material Volume), and S_sk (Skewness)—were selected to represent macro- and micro-texture features. These parameters were analyzed against dry and wet friction coefficients, and regression models were developed to predict F_Dry and F_Wet. The results show significant effects of both macro- and micro-texture parameters on friction coefficients. Among macro-texture parameters, S_a and V_mc strongly correlate with F_Dry, suggesting that greater surface roughness and core material volume enhance friction in dry conditions. Conversely, S_sk negatively correlates with F_Dry, indicating that negatively skewed profiles improve skid resistance. Other macro-texture parameters also influence F_Dry to varying extents. For micro-texture, S_dc (Material Height Difference), S_pd (Peak Density), and V_vv (Valley Void Volume) primarily affect F_Wet, with all showing significant positive correlations. This indicates that sharp peaks and void structures in micro-texture enhance skid resistance in wet conditions. The regression models effectively predict both friction coefficients, reducing field testing complexity and cost. These models provide an efficient tool for evaluating skid resistance and supporting pavement performance and maintenance management. This study highlights the distinct roles of macro and micro-texture in skid resistance, offering insights for optimizing pavement design and maintenance. Full article

This study proposes an explainable extreme gradient boosting (XGBoost) model for predicting the international roughness index (IRI) and identifying the key influencing factors. A comprehensive dataset integrating multiple data sources, such as structure, climate and traffic load, is constructed. A voting-based feature selection strategy is adopted to identify the key influencing factors, which are used as inputs for the prediction model. Multiple machine learning (ML) models are trained to predict the IRI with the constructed dataset, and the XGBoost model performs the best with the coefficient of determination ($R^2$) reaching 0.778. Finally, interpretable techniques including feature importance, Shapley additive explanations (SHAP) and partial dependency plots (PDPs) are employed to reveal the mechanism of influencing factors on IRI. The results demonstrate that climate conditions and traffic load play a critical role in the deterioration of IRI. This study provides a relatively universal perspective for IRI prediction and key factor identification, and the outputs of the proposed method contribute to making scientific maintenance strategies of roads to some extent. Full article

Existing models for predicting the international roughness index (IRI) of a road surface often lack adaptability, struggling to accurately reflect variations in climate, traffic, and pavement distresses—factors critical for effective and sustainable maintenance. This study presents a novel dual-model approach that integrates pavement condition index (PCI), pavement distress types, climatic, and traffic data to improve IRI prediction. Using data from the Long-Term Pavement Performance database, a dual-model approach was developed: pavements were classified into groups based on key factors, and tailored regression models were subsequently applied within each group. The model exhibits good predictive accuracy, with R² values of 0.62, 0.72, and 0.82 for the individual groups. Furthermore, the validation results (R² = 0.89) confirm that the combination of logistic regression and linear regression enhances the precision of IRI value predictions. This approach enhances adaptability and practicality, offering a versatile tool for estimating IRI under diverse conditions. The proposed methodology has the potential to support more effective, data-driven decisions in pavement maintenance, fostering sustainability and cost efficiency. Full article

The global asphalt production growth rate exceeded 10% in the past decade, and over 90% of the world’s road surfaces are generated from asphalt materials. Therefore, the issue of asphalt aging has been widely researched. In this study, the aging of asphalt thin films under various natural conditions was studied to prevent the distortion of indoor simulated aging and to prevent the extraction of asphalt samples from road surfaces from impacting the aged asphalt. The aging of styrene–butadiene–styrene (SBS)-modified asphalt was simulated at four different locations on an asphalt road surface. The aging characteristics of asphalt binders across various structural layers were revealed using Fourier transform infrared spectroscopy (FTIR), atomic force microscopy (AFM), and linear amplitude scanning (LAS). The results indicate that the aging behavior of the asphalt functional group on the road surface differs from other conditions; the asphalt fatigue life of 4 months equates to the 16-month aging life of asphalt within the dense-graded asphalt road surface. After 8 months of aging, the surface smoothness of the asphalt was significantly compromised. Inside of the porous pavement, the asphalt functional group is more likely to interact with water molecules than inside the dense pavement with cracks, and the variations in roughness and the reduction in fatigue life are also more significant. Full article