Road Surface Deterioration Research Articles

Modeling Rollover Crash Risks: The Influence of Road Infrastructure and Traffic Stream Characteristics

Rollover crashes are among the most prevalent types of accidents in developing countries. Various factors may contribute to the occurrence of rollover crashes. However, limited studies have simultaneously investigated both traffic stream and road-related variables. For instance, the effects of T-intersection density, U-turns, roadside parking lots, the entry and exit ramps of side roads, as well as traffic stream characteristics (e.g., standard deviation of vehicle speeds, speed violations, presence or absence of speed cameras, and road surface deterioration) have not been thoroughly explored in previous research. Additionally, the simultaneous modeling of crash frequency and intensity remains underexplored. This study examines single-vehicle rollover crashes in Yazd Province, located in central Iran, as a case study and simultaneously evaluates all the variables. A dataset comprising three years of crash data (2015–2017) was collected and analyzed. A crash index was developed based on the weight of crash intensity, road type, road length (as dependent variables), and road infrastructure and traffic stream properties (as independent variables). Initially, the dataset was refined to determine the significance of explanatory variables on the crash index. Correlation analysis was conducted to assess the linear independence between variable pairs using the variance inflation factor (VIF). Subsequently, various models were compared based on goodness of fit (GOF) indicators and odds ratio (OR) calculations. The results indicated that among ten crash modeling techniques, namely, Poisson, negative binomial (NB), zero-truncated Poisson (ZTP), zero-truncated negative binomial (ZTNB), zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), fixed-effect Poisson (FEP), fixed-effect negative binomial (FENB), random-effect Poisson (REP), and random-effect negative binomial (RENB), the FENB model outperformed the others. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) values for the FENB model were 1305.7 and 1393.6, respectively, demonstrating its superior performance. The findings revealed a declining trend in the frequency and severity of rollover crashes.

Examining Open-Top Culverts Impact on Forest Road Surface Deteriorations via UAVs

The life and robustness of forest roads depend on their protection from the harmful effects of water coming into the road surface. In particular, the deterioration of the road surface affects the safe navigation of vehicles and traffic safety. This situation requires that the surface be stable on forest roads. The aim of the study is to examine whether surface deterioration (erosion and accumulation) on forest roads due to the drainage problem of water falling on the road surface can be minimized by open-top culverts and to determine their effectiveness. These are used in three separate trial blocks every 25 m (A parcels; total of 3 parcels), every 50 m (B parcels; total of 3 parcels) and control block (C). Volumetric erosion and accumulation in these blocks was compared by UAV for about 3 years and the effectiveness of the open-top culverts was examined by this method. A 500 m section of the forest road coded 001 of the Kardüz Forest Operations Directorate (Düzce/Türkiye) was examined in the study. As a result, erosion and accumulation in all blocks have been found to have a dynamic course. It was determined that this mobility was greater in the control block than in the blocks with open-top culverts installed at intervals of 25 m and 50 m. The mean Z values for the blocks showed that the deterioration in the control block (C) was higher than in the blocks with 25 m and 50 m open culverts. The volumetric deterioration rate was 5 times higher in the control block than in the block installed at 25 m interval (A plots) and 2 times higher than in the block installed at 50 m interval (B plots). Similarly, the areal deterioration rate was 3.3 times higher in the control block than in the block installed at 25 m interval (A plots) and 1.4 times higher than in the block installed at 50 m interval (B plots). These results showed the effectiveness of open-top culverts and it was also determined that the open-top culverts installed at 25 m intervals were more effective than the open-top culverts established at 50 m intervals. In addition, according to the statistical analysis, a statistically significant difference was found between the erosion volume in the blocks. Open-top culverts should be used against forest road surface deterioration and UAV technology should be used for deterioration detection.

Numerically simulating the interconnected nature of the road-soil-pipe infrastructure

Successful operation of infrastructure, such as the road, rail, and utility networks, are fundamental to modern living, and failure of these can have significant consequences. Yet they are likely, when in proximity, to interact, and failure of one can cause cascade failure of others. Equally, all are supported by the ground, which can also experience changes in properties as the infrastructure deteriorates. Research into the interconnected nature of these infrastructure is not new, but much focuses on the structural behaviour of the human-made components (road structures, pipes, etc). This paper presents a numerical model developed to systematically evaluate the impacts of simulated pipe leakage on the surrounding ground and pavement layer above. The model outputs indicate that the road surface experiences increased strains due to weakening ground conditions around the leaking pipe and these are exacerbated by asymmetrical traffic loading. Findings indicate that leakage can cause differential settlements (observed both at the surface and pipe levels), which could cause localised deterioration of the pavement material (due to tensile cracking, etc.) precipitating surface anomalies like cracks or potholes. This suggests that understanding the root-cause of road surface deterioration is critical to efficient long-term management of road networks so that the symptoms are not remediated whilst the root-cause remains. This research provides a crucial step towards enhancing predictive maintenance and calls for further investigation into the long-term geotechnical impacts of leakage, to develop robust repair and maintenance frameworks that address underlying causes of road surface anomalies.

Pothole detection in the woods: a deep learning approach for forest road surface monitoring with dashcams

ABSTRACT Sustainable forest management systems require operational measures to preserve the functional design of forest roads. Frequent road data collection and analysis are essential to support target-oriented and efficient maintenance planning and operations. This study demonstrates an automated solution for monitoring forest road surface deterioration using consumer-grade optical sensors. A YOLOv5 model with StrongSORT tracking was adapted and trained to detect and track potholes in the videos captured by vehicle-mounted cameras. For model training, datasets recorded in diverse geographical regions under different weather conditions were used. The model shows a detection and tracking performance of up to a precision and recall level of 0.79 and 0.58, respectively, with 0.70 mean average precision at an intersection over union (IoU) of at least 0.5. We applied the trained model to a forest road in southern Norway, recorded with a Global Navigation Satellite System (GNSS)−fitted dashcam. GNSS-delivered geographical coordinates at 10 Hz rate were used to geolocate the detected potholes. The geolocation performance over this exemple road stretch of 1 km exhibited a root mean square deviation of about 9.7 m compared to OpenStreetMap. Finally, an exemple road deterioration map was compiled, which can be used for scheduling road maintenance operations.

Research on the effect of some factors into abrasive of cement concrete

Cement concrete pavement after a period of use will gradually degrade due to the impact of many factors such as weather, climate, mechanical agents ... One of the common causes of road surface deterioration cement concrete is due to surface abrasion. In the assessment of abrasion, the abrasive is very important. In the old standard, the grinding material is not F80 grain size fused alumina powder as the world is using. Changing the abrasive material will affect the specified values of abrasion for cement concrete pavement. This paper presents the influence of abrasives on concrete abrasion, which is the basis for proposing changes in the required value of abrasion for cement concrete pavement.

Exploring the Efficacy of Sparse Feature in Pavement Distress Image Classification: A Focus on Pavement-Specific Knowledge

Road surface deterioration, such as cracks and potholes, poses a significant threat to both road safety and infrastructure longevity. Swift and accurate detection of these issues is crucial for timely maintenance and user security. However, current techniques often overlook the unique characteristics of pavement images, where the small distressed areas are vastly outnumbered by the background. In response, we propose an innovative road distress classification model that capitalizes on sparse perception. Our method introduces a sparse feature extraction module using dilated convolution, tailored to capture and combine sparse features of different scales from the image. To further enhance our model, we design a specialized loss function rooted in domain-specific knowledge about pavement distress. This loss function enforces sparsity during feature extraction, guiding the model to align precisely with the sparse distribution of target features. We validate the strength and effectiveness of our model through comprehensive evaluations of a diverse dataset of road images containing various distress types and conditions. Our approach exhibits significant potential in advancing traffic safety by enabling more efficient and accurate detection and classification of road distress.

ConTrack Distress Dataset: A Continuous Observation for Pavement Deterioration Spatio-Temporal Analysis

Analysis of pavement deterioration is critical for road maintenance. Many section-based pavement performance evaluation methodologies have been investigated to determine the deteriorating tendency from a macro perspective. However, little research shed light on the refined deterioration analysis for single distress, which is valuable for daily and preventive maintenance. This paper proposed a deep-learning-based tracking framework to construct a large-scale continuous observation data set for every distress. A deep learning model is applied to detect six types of distress automatically. Then we adopted the spatial clustering method to match the pavement images in the same scene. Finally, image feature matching and perspective conversion methods are adopted to track the distress in the same scene. Using the data collected from the bus driving recorder, we have realized the daily observation of over 270 kilometers of the urban road network. More than 14,000 pavement distress have been continuously tracked, proving this framework’s effectiveness. In addition, the features of pavement deterioration are further discussed. The results show that heavy rain will significantly accelerate road surface deterioration. Under its influence, an intact pavement may suddenly deteriorate into serious potholes within a day. The established continuous pavement distress tracking dataset is significant for distress-level performance prediction research.

Smart Road Damage Detection and Warning using Machine Learning

We present a neural network topology, as well as training and prediction algorithms, in this research. To create a safe road environment, we present a deep neural network technique to detect road surface deterioration conditions. For training and testing, we provide an image dataset as input. Various sorts of road anxiety are depicted in the photographs. The suggested approach is compared to a variety of deep learning models from different disciplines. The findings of this study are expected to play a significant role in guaranteeing safe driving in the future by effectively detecting poor road conditions.

Assessing the surface material quality of unpaved rural roads to understand susceptibility to surface deterioration. A case study of four rural areas in KwaZulu-Natal, South Africa

Road surface deterioration is one of the most common problems of unpaved road networks worldwide. It is a reduction in the performance of a road due to a decline in road surface material quality. Accumulated damage from vehicles, environmental and physical effects may contribute to a decline in the surface material quality and hence deterioration on an unpaved road surface. This study assesses the surface material quality of unpaved rural roads in four rural areas in the KwaZulu-Natal Province, South Africa in order to understand susceptibility to surface deterioration. The study further establishes other possible factors such as slope gradient and rainfall, that could determine the surface material quality. Soil samples were collected from R3, R4, and R5 road classes in four rural areas which are: Emazabekweni, Dukuza, Mkhunya and Mhlwazini Area. Laboratory analyses were conducted in order to determine the performance of the material as potential wearing course. Material performance was then determined using the Standard Methods of Testing Road Construction Materials (TMH 1:1976) classification method. The results obtained imply that there is a need for better material selection during the construction of unpaved road networks. All road classes in Mkhunya, Emazabekweni and Mhlwazini areas exhibited grading coefficient (Gc) values less than 16 and some of the shrinkage product (Sp) values in excess of 365, corresponding to a classification of Class D, A and B. These results indicate material that is susceptible to slippery conditions, easily erodible and prone to the formation of ravels and corrugations. Correlation analysis results conducted to assess the individual relationship between measured rainfall and slope with field shrinkage product and grading coefficient values in each area indicated that variation in slope better explains shrinkage product values in each area with an R2 of 0.62 when compared to rainfall producing a lower R2 of 0.57. For grading coefficient, slope and rainfall produced similar R2 of 0.65 and 0.67, respectively.

Corrosion fatigue effects on life estimation of deteriorated bridges under vehicle impacts

Corrosion fatigue is more detrimental that of either one acting separately. The safety and reliability are greatly endangered by the combined effects from environmental corrosions and vehicle-induced fatigue damages, which could possibly lead to a catastrophic failure. In this paper, the corrosion fatigue effects on life estimation of deteriorated bridges subjected to dynamic vehicle loads are evaluated based on a reliability-based approach. Both the road surface deterioration and the structural member corrosion are considered in the vehicle-bridge dynamic system for stress range estimations. Varied material corrosion rates, vehicle types, vehicle speeds, and time-varied road surface conditions are considered. At the end of each stress block, the fatigue life is estimated by evaluating the cumulative probability of failure. The effects of the corrosion induced area loss and moment of inertia reduction are limited and are much less than the random effects from road surface condition. Corrosion induced fatigue strength reduction have a large effect on fatigue life. More than 60% reduction of fatigue life is predicted for different corrosion levels. The fatigue strength reduction is found more sensitive for the fatigue life estimation. With the refined vehicle-bridge dynamic analysis model and a refined material corrosion model, it is possible to assess the time-variant damages from random vehicle loads, environmental corrosion and their combined effects.

Nonlinear fatigue damage assessment of existing bridges considering progressively deteriorated road conditions

Variable stress ranges from dynamic vehicle loads can induce fatigue damage accumulations at certain bridge components and accelerate the road surface deterioration in bridges’ life cycle. The interactions of the road surface deterioration and random dynamic vehicle loads might accelerate the fatigue damage accumulations and lead to serious fatigue failures when such damages increase to a certain limit. This paper presents a nonlinear fatigue damage assessment approach for existing bridges to include the progressive road surface deterioration and the nonlinear effects of the fatigue damage accumulation due to random dynamic vehicle loads. In each stress block for fatigue damage calculation, vehicle types and speeds and road profiles for a certain road roughness coefficient are randomly generated for the vehicle–bridge dynamic system. Fatigue damage and cumulative probability of fatigue failures due to these variable stress ranges are calculated based on a nonlinear cumulative fatigue damage rule. Nevertheless, the vehicle types, numbers, and weights are used to predict the road roughness coefficient for the next stress block. The predicted fatigue damages are different when using the linear and nonlinear fatigue damage rules. The effects from the random variables in the vehicle–bridge dynamic system are discussed, as well.

Structural Assessment of Reinforced-Concrete Arch Underpasses Subjected to Vehicular Overloads

In most countries, the maximum dimensions and weights of vehicles that circulate on national roads and highways are legally regulated. However, several economic activities imply the circulation of exceptional vehicles or overloads, raising concerns not only about road surface deterioration but also about the structural safety of the bridges that are to be crossed. In a previous publication, the authors proposed a methodology for permit checking of vehicular overloads applicable to girder bridge decks that involves a comparison of code design-load effects with those resulting from the special vehicle being analyzed. The software developed therein [Bridge Investigation for Special Trucks (BIST)] performs a structural safety analysis of the bridges that are to be crossed, providing simplified safety factors associated with a particular overload, based on which the permit decision is made. This paper presents an update of the BIST software that extends the permit-checking methodology to underpass arch bridges. The first part of the paper describes the general methodology of BIST and its application to RC underpass arch bridges. Extension of the methodology involved the development of two- and three-dimensional finite-element (FE) models of a wide range of arch underpasses that were validated and calibrated based on full-scale experimental tests and the assumption of a set of proper conservative simplifications. The second part of the paper presents a study carried out to evaluate the conservativeness and accuracy of the method proposed by comparing the safety factors provided by BIST with those obtained from numerical models of real arch underpasses subjected to a wide range of real vehicular overloads. The safety factors provided by the BIST software were always conservative and reasonably accurate, thereby validating the methodology proposed and enabling fast, economical, and safe permit decision making.

Development of dynamic impact factor for performance evaluation of existing multi-girder concrete bridges

The dynamic effect of moving vehicles on bridges is generally treated as a dynamic load allowance (or dynamic impact factor) in many design codes. Due to the road surface deterioration of existing bridges, studies have shown that the calculated impact factors from field measurements could be higher than the values specified in design codes that mainly target at new bridge designs. This paper develops a 3D vehicle–bridge coupled model to simulate the interaction between a bridge and vehicles and investigates the impact factor for multi-girder concrete bridges. The effects of bridge span length, vehicle speed, and road surface condition on the impact factor are examined. Chi-square tests are then performed on the impact factors and it is found that the impact factors obtained under the same road surface condition follow the Extreme-I type distribution. Finally, simple expressions for calculating the impact factors are suggested applicable to both new and existing bridges. Corresponding confidence levels with the proposed impact factors for the five studied bridges indicate that the proposed expressions can be used with considerable confidence. The proposed expressions for impact factor can be used as a modification of the AASHTO specifications when dealing with short bridges and old bridges with poor road surface condition for which the AASHTO specifications may underestimate the impact factor.

Chloride Stabilization of Unpaved Road Aggregate Surfacing

There are few alternatives for improving long-term performance of aggregate surfacing materials in dry climates other than annual dust abatement treatments. In-place stabilization of properly graded aggregate with chloride additives is cost-effective and provides performance and intangible benefits. Similar results are achieved by mixing additives with aggregate during crushing. This 2-year study identifies conditions in which chloride-stabilized roads have a projected life of 10 years or more if properly maintained. The greatest benefits are realized in projects with high aggregate surfacing replacement costs and average daily traffic volumes that exceed 100. Stabilization provides a higher standard of road surface performance by improving ride quality and reducing dust, corrugations (washboarding), and raveling (loose aggregate). Other intangible benefits include reduced sedimentation in streams, reduced aggregate resource depletion, reduced health hazards from dust, and increased road-user safety. This project developed guidelines for chloride stabilization of various aggregate surfacing materials in semiarid to arid environments. Performance and cost-effectiveness of chloride stabilization were measured on 12 projects and monitored for two seasons in four western states; monitoring included construction and maintenance costs, road surface deterioration, traffic, weather conditions, environmental effects, and materials testing. Treated surfaces needed blading after 25,500 vehicles, whereas untreated surfaces needed blading after only 3,200 vehicles. Environmental effects on trees, streams, and roadside soils were insignificant. Many tools were developed to assist in the proper implementation of chloride stabilization of road surface aggregates.