Search Results (37)

Assessing highway pavement condition is crucial for ensuring transportation safety and optimizing infrastructure maintenance. In Xinjiang, China, extreme climatic and traffic conditions pose significant challenges to pavement performance. This study introduces a machine-learning-based framework to predict asphalt pavement performance in Xinjiang. We integrate various factors (design, materials, environment, traffic, and maintenance) into regression models, creating a region-specific pavement performance decay model. Our data preprocessing methodology effectively addresses outliers and missing data, ensuring the model’s robustness. The findings offer insights into asphalt pavement behavior in Xinjiang and provide guidance for maintenance strategies. The proposed model enhances highway infrastructure safety and cost-effectiveness. Future research will focus on refining the model with more data and exploring complex variable interactions. Full article

Internal soil erosion caused by water infiltration around defective buried pipes poses a significant threat to the long-term stability of underground infrastructures such as pipelines and highway culverts. This study employs a coupled computational fluid dynamics–discrete element method (CFD–DEM) framework to simulate the detachment, transport, and redistribution of soil particles under varying infiltration pressures and pipe defect geometries. Using ANSYS Fluent (CFD) and Rocky (DEM), the simulation resolves both the fluid flow field and granular particle dynamics, capturing erosion cavity formation, void evolution, and soil particle transport in three dimensions. The results reveal that increased infiltration pressure and defect size in the buried pipe significantly accelerate the process of erosion and sinkhole formation, leading to potentially unstable subsurface conditions. Visualization of particle migration, sinkhole development, and soil velocity distributions provides insight into the mechanisms driving localized failure. The findings highlight the importance of considering fluid–particle interactions and defect characteristics in the design and maintenance of buried structures, offering a predictive basis for assessing erosion risk and infrastructure vulnerability. 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

Predicting pavement performance is essential for highway planning and construction, considering traffic, climate, material quality, and maintenance. This study’s main objective is to evaluate Baosteel’s Slag Short Flow (BSSF) steel slag as a sustainable aggregate in pavement engineering by means of durability. The research integrates pavement performance prediction using BSSF and assesses its impact on fatigue resistance and percentage of cracked area (%CA). Using the Brazilian mechanistic-empirical design method (MeDiNa), eight scenarios were analyzed with soil–slag mixtures (0%, 25%, 50%, and 75% slag) in base and subbase layers under two traffic levels over 10 years. An asphalt mixture with 15% steel slag aggregate (SSA) was used in the surface layer and compared to a reference mixture. Higher SSA percentages were applied to the base layer, while lower percentages were used in subbase layers, facilitating field implementation. The resilient modulus (MR) and permanent deformation (PD) were design inputs. The results show that 15% SSA does not affect rutting damage, with %CA values below Brazilian limits for traffic of 1 × 10⁶. The simulations confirm BSSF as an effective and sustainable alternative for highway pavement construction, demonstrating its potential to improve durability and environmental impact while maintaining performance standards. Full article

With the continuous progress of traffic technology and the continuous improvements in traffic infrastructure, the maintenance and management of highway mechanical and electrical equipment has become a key factor affecting highway operation efficiency. However, at present, most of the mechanical and electrical systems of expressways cannot monitor the equipment continuously in terms of operation and maintenance, and most of the equipment operation and maintenance stay only in the stage of equipment failure. In addition, there are many kinds of highway mechanical and electrical equipment, and there are significant differences in the levels of parameters, so the parameter levels of highway mechanical and electrical equipment cannot fully meet the operation requirements of the area. Therefore, based on the basic theory of the Markov chain and the concept of daily operation and maintenance, this paper constructs a multistate Markov fault prediction model considering maintenance. Based on the historical data, the model realizes the prediction of the equipment failure rate and the formulation of the optimal maintenance strategy for the equipment, taking video surveillance equipment as an example, and verifies the improvement in the value of the equipment under this strategy through the value engineering theory. Based on the prediction results, more reasonable technical parameters are customized for equipment with a high failure rate to improve the practicability and reliability of the mechanical and electrical equipment in the area. Full article

To effectively mitigate service slope disaster risks in mountainous areas and enhance the overall safety of highway operations, based on the geological and structural characteristics of slopes, considering slope technical conditions, overall stability, and potential disaster consequences, 25 important influencing factors are systematically identified. The identification process integrates insights from the relevant literature, expert opinions, and historical disaster maintenance records of such slopes. An integrated approach combining Interpretive Structural Modeling (ISM) and Bayesian Networks (BNs) is utilized to conduct a quantitative analysis of the interrelationships and impact strength of factors influencing the disaster risk of mountainous service highway slopes. The aim is to reveal the causal mechanism of slope disaster risk and provide a scientific basis for risk assessment and prevention strategies. Firstly, the relationship matrix is constructed based on the relevant prior knowledge. Then, the reachability matrix is computed and partitioned into different levels to form a directed graph from which the Bayesian network structure is constructed. Subsequently, the expert’s subjective judgment is further transformed into a set of prior and conditional probabilities embedded in the BN to perform causal inference to predict the probability of risk occurrence. Real-time diagnosis of disaster risk triggers operating slopes using backward reasoning, sensitivity analysis, and strength of influence analysis capabilities. As an example, the earth excavation slope in the mountainous area of Anhui Province is analyzed using the established model. The results showed that the constructed slope failure risk model for mountainous operating highways has good applicability, and the possibility of medium slope failure risk is high with a probability of 34%, where engineering geological conditions, micro-topographic landforms, and the lowest monthly average temperature are the main influencing factors of slope hazard risk for them. The study not only helps deepen the understanding of the evolutionary mechanisms of slope disaster risk but also provides theoretical support and practical guidance for the safe operation and disaster prevention of mountainous highways. The model offers clear risk information, serving as a scientific basis for managing service slope disaster risks. Consequently, it effectively reduces the likelihood of slope disasters and enhances the safety of highway operation. Full article

Pavement deterioration is influenced by various factors with degradation rates varying widely depending on the type of pavement, its use, and the environment in which it is located. In Nepal, where the climate varies from alpine to subtropical monsoon, understanding pavement degradation is essential for effective road asset management. This study employs a Markov deterioration hazard model to predict pavement deterioration for the national highways managed by Nepal’s Department of Roads. The model uses Surface Distress Index data from 2021 to 2022, with traffic and cumulative monsoon rainfall as explanatory variables. Monsoon rainfall data from meteorological stations were interpolated using Inverse Distance Weighted and Empirical Bayesian Kriging 3D methods for comparative analysis. To compare the accuracy of interpolated values from the IDW and EBK3D methods, error metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Bias Error (MBE) were employed. Lower values for MAE, RMSE, and MBE indicate that EBK3D, which accounts for spatial correlation in three dimensions, outperforms IDW in terms of interpolation accuracy. The monsoon rainfall interpolated values using the EBK3D method were then used as an explanatory variable in the Markov deterioration hazard model. The Bayesian estimation method was applied to estimate the unknown parameters. The study demonstrates the potential of integrating the Markov deterioration hazard model with monsoon rainfall as an environmental factor to enhance pavement deterioration modeling. This model can be adapted for regions with a similar monsoon climate and pavement types making it a practical framework for supporting decision-makers in strategic road maintenance planning. Full article

Analytical models to predict distresses and service conditions of road pavements can greatly contribute to the development of an effective pavement management system. These models allow the transportation agencies to monitor and track the deterioration of pavements and consequently determine the needed maintenance operations to preserve the performance of the network. In this research, the pavement distresses and service conditions of the Indus Highway N-55 located in Karak district, Pakistan were examined. Distresses were identified by visual observation, and then their severity and extent were measured individually by using a Vernier caliper and a measuring scale. For each distress type, the corresponding PCR was calculated. The compaction densities of the base and wearing courses were considered as input parameters to develop an ordinal logistic regression model for two dominant distresses, namely rutting and potholes. Rutting severity and extent were divided into three levels, while pothole severity was divided into four levels. Bulk and maximum specific gravity were found to have a significant impact on the models of both distresses. The model can be used to predict their development in terms of severity and extent. The proposed formulation provides valuable insights into monitoring and predicting pavement distresses by assessing the densities of road construction materials. Full article

Quantifying the impact of rutting on traffic safety contributes to the development of objective models for evaluating pavement performance. However, the existing literature shows significant discrepancies in the impact of rutting on traffic safety. To this end, this study analyzed about 40 studies to comprehensively understand the impact of rutting on traffic safety in field observations and simulation studies. This study analyzed the influence of ten factors that may impact the relationship between rutting and traffic safety, such as weather, speed, and road type. It also established rutting limits and developed machine learning-based prediction models for accident rates caused by rutting under varying conditions. These findings reveal distinct trends, with simulation studies generally suggesting a higher impact of rutting on safety compared to field observations. This discrepancy is attributed to the limitations of simulation models in capturing human factors, such as drivers’ ability to anticipate and adjust their behavior to mitigate risks. These results provide valuable insights for highway agencies and policymakers to develop more accurate rut limits and maintenance guidelines. These results also underscore the importance of considering rutting in the development of autonomous vehicles to ensure effective handling of rutting under varying conditions. This study highlights the need for more comprehensive field studies using larger datasets that account for various environmental and traffic factors. Additionally, integrating real-world driver behavior into simulation models could improve their accuracy. Full article

Road maintenance is essential for supporting road safety and user comfort. Developing predictive models for road surface conditions enables highway agencies to optimize maintenance planning and strategies. The international roughness index (IRI) is widely used as a standard for evaluating road surface quality. This study compares the performance of deep neural networks (DNNs) and graph convolutional networks (GCNs) in predicting IRI values. A unique aspect of this research is the inclusion of additional predictor features, such as the type and timing of recent roadwork, hypothesized to affect IRI values. Findings indicate that, overall, the DNN model performs similarly to the GCN model across the entire highway network. Given the predominantly linear structure of national highways and their limited connectivity, the dataset exhibits a low beta index, ranging from 0.5 to 0.75. Additionally, gaps in IRI data collection and discontinuities in certain highway segments present challenges for modeling spatial dependencies. The performance of DNN and GCN models was assessed across the network, with results indicating that DNN outperforms GCN when highway networks are sparsely connected. This research underscores the suitability of DNN for low-connectivity networks like highways, while also highlighting the potential of GCNs in more densely connected settings. Full article

The evaluation of tunnel service conditions is a core problem in the maintenance of tunnel structures during their life cycles. To address this problem, machine learning algorithms were applied to the National Tunnel Inventory (NTI) database of the Federal Highway Administration of the United States to predict the service states of the structural, civil, and non-structural sections of a tunnel, respectively. The results indicate that ensemble learning algorithms such as Light Gradient Boosting Machine (LGBM) and Random Forest outperform Support Vector Machine, Multi-Layer Perceptron, Decision Tree, and K-Nearest Neighbor in solving imbalanced classification problems presented in the NTI database. The machine learning models established using the LGBM algorithm exhibited prediction accuracies of 90.9%, 96.4%, and 77.3% for the structural, civil, and non-structural sections, respectively. The importance sorting of features influencing the tunnel’s service state was then performed based on the LGBM model, revealing that the features with a significant impact on the service states of the structural, civil, and non-structural sections are service time, tunnel length and width, geographic position (longitude and latitude), minimum vertical clearance, annual average daily traffic (AADT), and annual average daily truck traffic (AADTT). Data-driven LGBM models identified human factors such as AADT and AADTT as key features influencing the service states of tunnels’ structural sections, and these factors should be taken into consideration in further research to elucidate the potential physical mechanisms. Full article

The stabilization of asphalt pavement bases with granular soil and aggregates emulsified with asphalt is a widely used technique in road construction and maintenance. It aims to improve the mechanical properties and durability of the lower pavement layers. Currently, there is no consensus on the most suitable method for designing emulsified granular aggregates with reclaimed asphalt pavement (RAP), as it is very complex. Therefore, the methodology is generally based on compliance with one or more volumetric or mechanical parameters established in the highway regulations for conventional asphalt mixtures, which does not guarantee the optimization and characterization of the recycled mixture in the base course. In this study, granular mixtures were developed, including five with emulsion and one emulsion-free as a control mix. Granular RAP mixes were designed in this study, including five with emulsion and one emulsion-free as a control mix. The five mixes ranged from 1% to 5% emulsion and were characterized by multi-stage triaxial tests with repeated load resilient modulus (RM) and permanent deformation (PD) to evaluate their mechanical behavior. The results showed that the mixes had RM values between 350 and 500 MPa, consistent with literature values. However, they showed similar levels of accumulated deformation to the control mix without RAP emulsion. The sample with 1 % RAP emulsion exhibited a satisfactory RM value and better performance in PD than the control mix (5 mm) and showed accumulated PD values of up to 4 mm. In contrast, the other samples exhibited deformations of up to 6 mm. In this study, the multi-stagge triaxial RM and PD tests were found to be an effective predictive method for characterizing the behavior of RAP materials in base courses, regardless of the types of admixtures contained. Multi-stage resilient modulus and PD tests can be considered as a predictive method for the behavior of milled material in base courses. They were able to provide initial data for interpreting the behavior of ETB mixtures. Full article

The deep learning method has been widely used in the engineering field. The availability of the training dataset is one of the most important limitations of the deep learning method. Accurate prediction of pavement performance plays a vital role in road preventive maintenance (PM) and decision-making. Pavement performance prediction based on deep learning has been widely used around the world for its accuracy, robustness, and automation. However, most of the countries in the world have not built their pavement performance historical database, which prevents preventive maintenance using the deep learning method. This study presents an innovative particle swarm optimization (PSO) algorithm-enhanced two-stage TrAdaBoost.R² transfer learning algorithm, which could significantly increase the pavement performance prediction database. The Long-Term Pavement Performance (LTPP) database is used as the source domain data, and one of the highways in China is chosen as the target domain to predict pavement performance. The results show that the proposed PSO-Two-stage TrAdaBoost.R² model has the highest accuracy compared with AdaBoost.R² model and traditional regression decision tree model. The validation case study shows significant consistency between the predicted International Roughness Index (IRI) and the whole-year measurement data with an R² of 0.7. This study demonstrates the great potential of the innovative instance-based transfer learning method in pavement performance prediction of a region’s lack of data. This study also contributes to other engineering fields that could greatly increase the universality of deep learning. Full article

This research delves into the intricate dynamics of landslides, emphasizing their consequences on transportation infrastructure, specifically highways and roadway bridges in North Carolina. Based on a prior investigation of bridges in Puerto Rico after Hurricane Maria, we found that bridges above water and situated in valleys can be exposed to both landslide and flooding risks. These bridges faced heightened vulnerability to combined landslides and flooding events due to their low depth on the water surface and the potential for raised flood heights due to upstream landslides. Leveraging a dataset spanning more than a century and inclusive of landslide and bridge information, we employed logistic regression (LR) and random forest (RF) models to predict landslide susceptibility in North Carolina. The study considered conditioning factors such as elevation, aspect, slope, rainfall, distance to faults, and distance to rivers, yielding LR and RF models with accuracy rates of 76.3% and 82.7%, respectively. To establish that a bridge’s location is at the bottom of a valley, data including landform, slope, and elevation difference near the bridge location were combined to delineate a bridge in a valley. The difference between bridge height and the lowest river elevation is established as an assumed flooding potential (AFP), which is then used to quantify the flooding risk. Compared to traditional flood risk values, the AFP, reported in elevation differences, is more straightforward and helps bridge engineers visualize the flood risk to a bridge. Specifically, a bridge (NCDOT ID: 740002) is found susceptible to both landslide (92%) and flooding (AFT of 6.61 m) risks and has been validated by field investigation, which is currently being retrofitted by North Carolina DOT with slope reinforcements (soil nailing and grouting). This paper is the first report evaluating the multi-hazard issue of bridges in valleys. The resulting high-fidelity risk map for North Carolina can help bridge engineers in proactive maintenance planning. Future endeavors will extend the analysis to incorporate actual flooding risk susceptibility analysis, thus enhancing our understanding of multi-hazard impacts and guiding resilient mitigation strategies for transportation infrastructure. Full article

The technical condition of bridges has become a crucial issue for organizing the maintenance and repairs in bridge management systems. It is of great practical engineering significance to construct an effective model for predicting the technical condition degradation of the bridge through the use of the historical inspection data. Based on the semi-Markov random process, this paper proposes a useful deterioration prediction model for bridges in the highway network. From the historical inspection data of the prefabricated concrete box girder bridges, the degradation curves of technical condition rating are obtained. The effect of bridge length on degradation rate of the prefabricated concrete box girder bridges is analyzed. According to the Weibull distribution parameters of different condition grades, the technical state degradation models for a bridge group and an individual bridge are proposed to predict the performance of the overall bridge and superstructure of the bridge. The results show that with the increase in bridge length, the degradation rate of bridge technical condition increases. The degradation rate of the technical condition of the superstructure is faster than that of the overall bridge. The proposed semi-Markov stochastic degradation model for the bridge group can not only predict the different condition ratings of the bridges at any time, but also predict the future deterioration trend of an individual bridge under any ratings. Full article