Highway In China Research Articles

Capturing uncertainty intuition in road maintenance decision‐making using an evidential neural network

AbstractDecision‐making of project‐level road maintenance is the process of mapping road information into a maintenance plan. Even though benefitting from deep learning, the decision‐making still faces the problem of maintenance data uncertainty. The data uncertainty derives from imperfect road information collection and arbitrary selection of maintenance plans. Such uncertainty always leads to unreasonable maintenance decision‐making. This study proposes an evidential approach using information entropy (IE) and Dempster–Shafer theory (DST) to capture and handle uncertainty in the decision‐making of project‐level road maintenance. The approach first uses an IE‐based judgment method (IE‐based method) to capture and observe quantitative data uncertainty. The DST‐based method is then developed to handle maintenance data uncertainty through utilizing evidential neural network and set‐valued decision‐making. A numerical experiment is performed on the maintenance data with 280 km of semi‐rigid base highways in China. The results indicate that the IE‐based method can measure the data uncertainty in the information of road sections. The DST‐based method captures the cautious intuition on the selection of maintenance plans, thereby reducing the decision error rate by over 14% under specific conditions when facing data uncertainty.

Investigation of Freeway Incident Duration Using Classification and Regression Trees Based on Multisource Data

Targeted contingency measures have proven highly effective at reducing the duration and harm caused by incidents. This study utilized the Classification and Regression Trees (CART) data mining technique to predict and quantify the duration of incidents. To achieve this, multisensor data collected from the Hangzhou freeway in China spanning from 2019 to 2021 was utilized to construct a regression tree with eight levels and 14 leaf nodes. By extracting 14 rules from the tree and establishing contingency measures based on these rules, accurate incident assessment and effective implementation of post-incident emergency plans were achieved. In addition, to more accurately apply the research findings to actual incidents, the CART method was compared with XGBoost, Random Forest (RF), and AFT (accelerated failure time) models. The results indicated that the prediction accuracy of the CART model is better than the other three models. Furthermore, the CART method has strong interpretability. Interactions between explanatory variables, up to seven, are captured in the CART method, rather than merely analyzing the effect of individual variables on the incident duration, aligning more closely with actual incidents. This study has important practical implications for advancing the engineering application of machine learning methods and the analysis of sensor data.

Study on the Effect of Root Density on the Shear Strength of Root-Soil Composite: A Case Study of Cynodon Dactylon

Since the Belt and Road Initiative was proposed, many slopes with poor stability have been formed in China's highway and railway infrastructure construction. In response to the national goals set during the 14th Five-Year Plan to strengthen the ecological security barrier and protect biodiversity, and in line with the philosophy that "lucid waters and lush mountains are invaluable assets," ecological protection technology has been widely applied in slope protection. However, current research and design have not considered the role of roots in retaining walls. This study uses an eco-friendly reinforced soil retaining wall project as an example to investigate the relationship between root density and the cohesion and internal friction angle of root-soil composites through direct shear tests. The results show that increasing root density significantly improves the cohesion and internal friction angle of root-soil composites. When the root density is 5%, the cohesion and internal friction angle increased by 40.57% and 51.41%, respectively, compared to the unreinforced soil sample, providing a theoretical reference for engineering design.

In-tunnel pollutant concentration measurement and ventilation control indexes for highway tunnels in mountainous area: A case study of no.1 Qinling tunnel, China

The pollutant concentration and hygiene standards in mountain tunnels are key factors determining the ventilation effectiveness of tunnel operation. Previous studies have mainly focused on the spatiotemporal distribution patterns of in-tunnel pollutants, while research on ventilation control indexes for pollutants in long mountain tunnels are relatively limited. Therefore, this paper aims to discussing the hygiene control indexes for operational ventilation of mountain tunnels. Field tests were conducted in the No.1 Qinling Tunnel of Xi'an to Han zhong Highway in China, and the distribution laws of traffic flow, wind speed, and concentrations of particulate matter (PM), carbon monoxide (CO), and nitrogen oxide (NOX) were on-site measured. The spatiotemporal distribution characteristics of pollutants in the long mountain tunnel were revealed, and the ventilation hygiene control indexes for harmful gases were calculated and compared to those of urban highway tunnels. The results show that the traffic volume of operational highway tunnels in mountainous areas of China is increasing rapidly, and the particulate matter emitted from diesel vehicle exhaust will pose a threat to the normal environmental quality in the tunnel. The average value of CO base emission factor in 2000 for the upline of No.1 Qinling tunnel was 0.0019 m3/(veh·km) by back-calculation, and the annual decline rate of CO in the ventilation design for mountain highway tunnels should be 6 %–8 % and revised every 8 years. It is recommended to consider the geographical location and traffic conditions of mountain tunnels when calculating the air demand required for diluting NO2. NOx has basically replaced CO as the primary pollutant with the most significant impact on human health in mountain tunnels. It is recommended that NOX be considered as the primary hygiene control indicator in the ventilation design of mountain tunnels in China. The pollutant distribution and ventilation control indexes of mountain and urban highway tunnels are quite different, which should be treated differently in engineering applications. The research results can provide a significant reference for ventilation design and environmental evaluation of highway tunnels in mountainous areas.

Research on the method of identifying vertical earth pressure of hinged prefabricated culvert box culvert on the top slab

In China, several expressways have been designed as prefabricated box culverts with hinge connections, which have different structural features from the prefabricated culverts in other countries. The difference would contribute to the culvert–soil interaction of prefabricated box culverts, which could affect the earth pressure on the culvert. Based on the field test and numerical simulation method, a hinged prefabricated box culvert (HPBC) with a span of 4 m and a rise of 4 m was investigated, which was applied to the Xi-Yu expressway in China. The objective of this research was to investigate the vertical earth pressure on the top slab of the HPBC culvert at different backfill heights through the field tests. The FLAC3D software was employed to conduct further analysis of the effects of backfill height, backfill modulus, and foundation modulus on the vertical earth pressure on the top slab of HPBC. The differences between the HPBC and monolithic box culvert (MBC) were also examined. Furthermore, a revised method for calculating the vertical earth pressure on the top slab was put proposed and compared with the AASHTO method for calculating earth pressure on the top of culverts and the values taken from the Chinese culvert design code. The proposed method is capable of improving the accuracy of the earth pressure approach, making it more representative of actual conditions. Subsequently, the sensitivity analysis of backfill height, backfill modulus and foundation modulus to the vertical earth pressure concentration coefficient of the top slab was carried out by using the principle of orthogonal array analysis and the modified calculation method proposed in this paper. The findings of this study offer valuable insights into the determination of culvert top earth pressure of HPBC.

Study on ring-road incident duration based on latent class accelerated hazard model.

Accurately estimating the duration of freeway incidents can enhance emergency management practices and reduce the likelihood of secondary incidents. To investigate the mechanisms through which key factors influence incident duration, this study sorted out the characteristics and variables of the incident duration on a special freeway in Zhejiang Province, that is, the ring road, and developed a latent class accelerated hazard model. Heterogeneity was incorporated into the model. Three distributions (Weibull, Log-normal, and Log-logistic) were compared, and the Log-logistic distribution exhibited superior performance. The analysis revealed two distinct latent classes: Latent Class 1 and Class 2, had class membership probability of 0.53 and 0.47, respectively, with a total of 11 variables being statistically significant at the 0.05 significance level. It is worth noting that, some neglected explanatory variables are discussed in depth in this study. For example, the mechanism of which specific lane is closed has an impact on the incident duration, rather than a general discussion of the number of lane closures. Furthermore, the way in which the driver involved in the incident reports to the police has a significant impact on the duration of incidents. Notably, potential heterogeneity and its influencing mechanism are captured in the model. Additionally, by predicting class membership using posterior probabilities, it was determined that most data points were more likely to belong to Class 1, and the incident duration primarily ranged between 0 and 60 minutes. These findings are helpful to reduce the duration of incidents on ring-roads and freeways in China, and provide theoretical support for the formulation of freeway incident management and treatment policies.

Correlation between carbon emissions, fuel consumption of vehicles and speed limit on expressway

This paper aimed to investigate the correlation between carbon emissions, fuel consumption, and speed limit. A theoretical model was derived based on the energy conservation law, which expresses the relationship between vehicle's fuel consumption and speed. Subsequently, a total of 40 sets of fuel consumption data were collected through field tests to verify the accuracy of the theoretical model at different speeds and different road longitudinal slope combinations. The fuel consumption was then converted to carbon emissions according to the carbon emission factors specified by Intergovernmental Panel on Climate Change (IPCC). In the field experiment, two types of cars and trucks, which are most common on the expressways in China, were selected. Finally, the travel speed under different posted speed limits was obtained through the previously established model, and the carbon emission changes of different vehicle types at different limited speeds are calculated. The results show that the speed limit has a significant impact on fuel consumption and carbon emissions. When the speed limit increased from 80 to 120 km/h, average vehicle speeds increased about 21%–27%, and fuel consumption and carbon emissions increased from approximately 33%–38%. Another interesting result was that the vehicle's fuel consumption and carbon emissions are only affected by speed. The results of the study explore the effect of speed limits on carbon emissions and provide evidence for road managers to set reasonable speed limits.

Evaluation method for health state of highway tunnel structure based on adaptive comprehensive weighting

Accurately evaluating the health state of tunnel structure is of great significance for ensuring the operation safety of tunnel. Based on engineering investigation, fuzzy mathematics and statistical analysis, this paper proposes a new method to evaluate the health state of highway tunnel structure, which considers the multi-disease characteristics and adaptive comprehensive weighting theory. Firstly, by investigating the health state of 20 operation highway tunnels in China, a tunnel disease database with 539 samples was constructed. Considering some new tunnel diseases such as floor heave and lining spalling, a health evaluation index system of highway tunnel was established further. Secondly, utilizing the subjective-objective weighting theory and the principle of variable weight, a new adaptive comprehensive weighting method was proposed to calculate the indexes’ weights. This method overcomes the defect that the constant weight cannot be dynamically updated according to the actual situation of disease development. Then, according to the established four-level classification standard suitable for health evaluation of highway tunnels, the normal distribution membership functions of second-level indexes were determined. Combining with the fuzzy theory, the quantitative evaluation model of tunnel health state was constructed further, which was applied to a case study of the Nanyangshan Tunnel of Kanglin Expressway in China. Furthermore, a comparative analysis with the traditional subjective-objective combination weighting method was conducted, demonstrating the advantages of the proposed evaluation method in terms of engineering applicability and result accuracy. The research findings can provide a decision-making basis for the health maintenance and scientific management of highway tunnels in China.

Transformer-based modeling of abnormal driving events for freeway crash risk evaluation

A crash risk evaluation model aims to estimate crash occurrence possibility by establishing the relationships between traffic flow status and crash occurrence. Based upon which, Proactive Traffic Safety Management (PTSM) systems have been developed and implemented. The current crash risk evaluation models relied on high dense traffic detectors, which limited the applications of PTSM to infrastructures with enough sensing devices. To address such application limitation issue, this study employed the widespread abnormal driving event information that is generated by emerging driving monitoring and vehicle connection techniques to develop the crash risk evaluation model. Specifically, to characterize abnormal driving events, a six-tuple embedding method was proposed to store their space, time and kinetics features. Given their irregular and discrete distributions on roadways, a Transformer model with self-attention mechanism was proposed to extract the spatial distribution characteristics. In addition, a time-decay function was integrated to fit the temporal impacts of abnormal driving events on crash risk. Empirical data from a freeway in China were utilized for the analyses. The results showed that abnormal driving events with lower speed, larger acceleration and duration are more likely to cause crashes. The accumulation of multiple events in the time period of less than 3 min would lead to a sharp increase of crash risk. Besides, compared to the average metrics of the widely adopted Convolutional Neural Network (CNN), XGBoost, and logistic regression models, the proposed model achieved higher accuracy (0.841) and AUC (0.777), with average improvement of 2.5 % and 9.1 % respectively.

Analysis of the Impact of the Former Abandoned Dregs of Mountain Highways on the Construction and Safety of New Bridge Pile Foundations

In highway reconstruction and expansion projects, the impact of the abandoned dreg body produced by the original highway on the new bridge project cannot be ignored. If the abandoned dregs are not handled properly, they will cause great threat to the operation of the new highway as well as to the safety of the surrounding environment and personnel. In this paper, based on a South China highway reconstruction and expansion project, a model of the abandoned dreg body, bridge pile and anti-slide pile is established with the help of finite difference software FLAC3D 5.0. The calculation results show that compared with burying the anti-slide pile, slope cutting is more effective for improving the stability of the abandoned dreg body. Under the action of vertical force intensity, the displacement of the top of the pile increases continuously, and the maximum bending moment of the pile body increases firstly and then decreases. The pile displacement and bending moment of the bridge pile increase significantly in the storm condition compared with the normal condition. In addition, the abandoned dreg body hazard prevention and control technical measures are studied. The impacts of different locations of anti-slide pile burial on bridge piles are analyzed and compared, and it is determined that the optimal location of anti-slide pile burial is the middle of the secondary slope.

Research on Performance Evaluation of Full Process Cost Management in Expressway Projects

The research purpose of this article is to optimize the performance evaluation of cost management throughout the entire process of highway projects. Currently, China's highway construction is booming, but cost management is complex and requires the construction of a scientific performance evaluation system to comprehensively supervise and evaluate engineering costs. This article used literature induction method for literature review, followed by expert scoring method to assign scores to indicators, and finally used Analytic Hierarchy Process to determine indicator weights. From the results, this study identified key factors that affect the overall cost management performance of highway projects, and constructed relevant evaluation models and weight hierarchy tables. Therefore, the results of this article are helpful in accurately controlling project costs, evaluating benefits, and optimizing fund management, which is of great significance for improving the level of highway project management and investment return rate.

Novel Instance-Based Transfer Learning for Asphalt Pavement Performance Prediction

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.

Innovative Application of Highway Traffic Safety Hazard Investigation and Accident Prevention Technology

With the continuous improvement of China s economic level, China has increased its efforts in the construction of transportation infrastructure. The number and mileage of highways in China are constantly increasing, which requires us to strengthen the investigation of road safety hazards on highways. Once road safety hazards are found, they must be dealt with in a timely manner to effectively ensure the road safety of highways and provide guarantees for the driving safety of the people.

Most suitable plant communities for the slope reclamation of the Zhengzhou-Xinxiang section of the Beijing-Hong Kong-Macao expressway.

The construction of expressways in China has produced diverse habitats along slopes characterized by steep gradients, uneven water distribution, poor soil conditions, and no routine maintenance. Manually planting beneficial species is an essential method of effectively improving slope soils to prevent soil erosion. However, few studies have evaluated the reclamation effects and plant community composition and structure used to restore slopes along expressways. This study focused on the Zhengzhou-Xinxiang section of the Beijing-Hong Kong-Macao Expressway. A total of 10 representative plant communities were evaluated using the analytic hierarchy process (AHP)-fuzzy integrated evaluation method. The sites were divided into four layers, namely, plant communities, soil nutrients, soil physical properties, and other ecological factors, and 14 indicators were assessed. The evaluation results showed that four of these plant communities (PCs) were excellent, three PCs were good, one PC was normal, two PCs were poor. The four excellent PCs had high Shannon-Wiener index, pielou index, richness index or community productivity. It is worth noting that most excellent plant community structures were tree + shrub + herb. Based on these results, we recommend that fill slopes should be restored using a combination of trees, herbs, and shrubs; also, the vegetation should include native plants, such as B. papyrifera, U. pumila, A. fruticosa, and Cynodon dactylon (L.). This study could provide ideas for plant community composition and structure of new highway slopes in similar climate environment, and provide theoretical support for plant community composition and structure and soil improvement for the existing slope.

Research on intrusion and large arch bulge in lining structure for highway’s mudstone tunnel

During the construction of a highway in northwest China, large deformation of mudstone caused severe deformation of and damage to side walls, initial support, and secondary lining to various extents. To reveal the causes of mudstone’s large deformation in the tunnels of this highway, a comprehensive study was conducted by using engineering geological survey, on-site monitoring and measurement, indoor rock mechanics test, numerical simulation, and macroscopic analysis. For the problem of large deformation of this highway’s tunnel section from YK209 + 500 m to YK210 + 030 m, the 3D finite difference method FLAC3D was used to simulate the large deformation of the wall rock and compare the deformation of the tunnel and the mechanical characteristics of the lining structure under different conditions by means of inverse analysis of the rheological characteristics of the mudstone and simulation of the softening of the mudstone in water. The research results provide a reference and basis for the construction design of similar mudstone tunnel projects. For the management of tunnel deformation, it is recommended to enhance the tunnel’s drainage measures, thereby mitigating the intensification of mudstone softening when exposed to water.

Incorporating spatio-temporal correlation and interaction into a generalized ordered logit model for examining injury severities of traffic accidents

Spatio-temporal correlation and interaction are significant features of traffic accident data. To account for the spatio-temporal correlation and interaction and the ordinal nature simultaneously, spatio-temporal generalized ordered logit models with different temporal treatments are advocated to examine accident injury severities. In the context of Bayesian inference, we estimate the advocated models and compare them with traditional generalized ordered logit model and spatial generalized ordered logit model using six-year (2014-2019) accident data from the Dongguang section of Guangzhou-Shenzhen Coastal Freeway in China. The results indicate that there are significant spatio-temporal effects and interaction in the accident severity data, and that the spatio-temporal generalized ordered logit models yield low values of deviation information criterion and more reasonable parameter estimates than the generalized ordered logit and spatial generalized ordered logit models do. The estimation results reveal that covariates related to vehicle type, accident type, season, time, horizontal curvature, and bridge have significant effects on accident injury severities. The findings above demonstrate the superiority of the proposed spatio-temporal generalized ordered logit models for predicting accident severities.

Comprehensive analysis of renewable hybrid energy systems in highway tunnels

In order to explore the feasibility of a renewable hybrid energy system in highway tunnels, a scenario-coupled construction method for a highway tunnel renewable hybrid energy system is proposed. Based on this method, a tunnel on a highway in southern China serves as an example, and a renewable hybrid energy system for the highway tunnel is constructed. Using HOMER, the hybrid energy system is simulated, optimized, and analyzed, studying the characteristics of the operation of the highway tunnel hybrid renewable energy system in an off-grid mode. The optimal configuration for the hybrid energy system is proposed. The research results indicate the feasibility of constructing a highway tunnel renewable hybrid energy system by utilizing natural resources within the road area (solar energy, wind energy). The hybrid renewable energy system for this tunnel can provide 1112392 kWh of electrical energy annually, with the lowest energy cost being $0.17/kW. Furthermore, the hybrid energy system can reduce greenhouse gas emissions, contributing to the sustainable development of the environment.

Estimation and prediction of freeway traffic congestion propagation using tagged vehicle positioning data

Effective estimation and prediction of freeway congestion propagation is the basis for formulating a traffic management strategy. A new estimation and prediction model for congestion propagation based on coordinated vehicle positioning data collected from fixed detectors is proposed. A shockwave-based method is established to model the propagation trajectory of congestion in time and space using data filtered by the Savitzy-Golay. Bayesian ridge regression is applied to determine the probability range of the propagation path of the congestion. The proposed method was tested with Wi-Fi positioning data from a section of the Beijing-Kunming Freeway in China. The results of our method were compared with filed traffic conditions estimated by loop data. The results show a difference of approximately 200s between the start time of the estimated congestion and the observed time on the selected road section. The predicted congestion start time differs from the observed results by around 100s.

Use of condition-based valuation approach to evaluate the maintenance decision of pavement assets: a case study of Yunnan Province in China

Pavements are critical components of highway assets, but accurately estimating their value can be challenging due to the lack of connection between valuation and long-term service performance degradation. To address this issue, the replacement cost and condition-based valuation methods were introduced using the Yunnan province highway in China as an example, which divided pavement into four performance states based on service stages. The rationality of maintenance decisions and the impact of preventive maintenance investment on life cycle assessments were also considered. The results indicate that there are issues of irrational allocation of maintenance funds of the highway in Yunnan province, and a higher proportion of preventive maintenance investment is required. Moreover, it is recommended to balance the maintenance funds for bridge and tunnel engineering in the following year, focusing on bridge engineering with a rapid decline in the newness rate. The implementation of preventive maintenance has a positive correlation with the replacement cost and newness rate of the road sections. Through comparison, it was found that the road surface renewal rate in 2019 was lower than that in 2018, and some highway management departments in certain regions need to adjust their preventive maintenance strategies. Overall, the condition-based pavement asset valuation method comprehensively considers each stage of pavement operation and can serve as an effective tool for evaluating pavement asset depreciation. This research finding can promote the sustainable development of road infrastructure.

Time-series prediction of settlement deformation in shallow buried tunnels based on EMD-SSA-GRNN model

Tunnel settlement deformation monitoring is a complex task and can result in nonlinear dynamic changes. To overcome the disturbances caused by historical data and the difficulty in selecting input parameters during deformation prediction, a decomposition, reconstruction and optimization method for tunnel settlement deformation prediction is proposed. First, empirical mode decomposition (EMD) is used to decompose the in-situ monitoring data and reduce the interactions among information at different scales in sequences. Then, the monitoring data are decomposed into intrinsic mode functions (IMFs). Secondly, the smoothing factor of the generalized regression neural network (GRNN) is optimized by using the sparse search algorithm (SSA). An EMD-SSA-GRNN deformation prediction model is developed using the optimized GRNN algorithm and is used to predict the changes in the decomposed IMFs. Finally, using the measured deformation data from a shallowly buried tunnel along the Kaizhou-Yunyang Highway in Chongqing, China, the reliability and accuracy of different models are analysed. The results show that tunnel settlement deformation exhibited a trend and a slow change in the early stage, a rapid change in the middle stage and a slow change in the late stage, and the rate of change was significantly influenced by the excavation time and the upper and lower geological layers. The prediction accuracy of the EMD-SSA-GRNN model after EMD improved from 19.2 to 59.4% relative to that of the SSA-GRNN and single GRNN models. Moreover, we find that the three error evaluation indicators of the EMD-SSA-GRNN model are lower than those of the other models and that the results of the proposed model and are more strongly correlated with measured data.