AI-Enabled Assessment of Roadway Integrity: Forecasting Bitumen Deformation and Road Stability Throughout the Lifecycle Under Traffic Impact

Investigating the influence of traffic flow on the deformation of asphalt surfaces constitutes a significant domain within transportation engineering. Insight into the interaction between vehicles and asphalt deformation can inform the creation of more durable, cost-efficient roadways, given that road degradation is intrinsically linked to traffic flow dynamics.

Presently, road construction relies on robust calculations encompassing weather conditions and vehicular volume, with an assumed minimum lifespan of roadways spanning ten to thirty-five years. However, this study challenges these assumptions, revealing that the alteration of traffic flow at intersections significantly impacts road erosion, rendering calculations based on these assumptions potentially flawed. Particularly, the research indicates that a ten-year lifespan might not always be a realistic expectation for road segments at intersections.

To explore this phenomenon, data were extracted and processed from video footage to procure real-time vehicle movement and asphalt impact information. Subsequent analysis via computational algorithms, such as the backpropagation algorithm, unveiled patterns and correlations between traffic flow and asphalt deformation. Consequently, this analysis facilitated the identification of key factors influencing asphalt deformation and contributed to the development of predictive models for estimating asphalt lifespan. The results of this research are modeled using machine learning (ML) technique with the previously mentioned limitations.

The emphasis on road intersections in this study is justified by several factors:

• High Traffic Volume: Intersections, being convergence points of multiple roads, handle higher vehicular volume than other road segments, thereby increasing pavement strain and susceptibility to deformation.

• Complex Traffic Patterns: Intersections are characterized by intricate driving patterns, encompassing frequent acceleration, deceleration, and changes in direction. These abrupt shifts may induce pavement wear and deformation.

• Vehicle Braking and Acceleration: Rapid stopping and acceleration at intersections exert stress on the pavement, potentially accelerating asphalt degradation.

• Safety Concerns: Deformations in asphalt at junctions can result in uneven surfaces and potholes, escalating the risk of accidents, particularly in adverse weather conditions [1].

• Maintenance Costs: Intersections' susceptibility and high traffic volume necessitate regular, intensive maintenance, thereby increasing costs for road authorities.

• Improved Intersection Design: An understanding of the impact of traffic flow on pavement deformation at intersections can inform the development of more durable and safer intersection designs.

Overall, studying the impact of traffic flow on asphalt surface deformation at road intersections provides valuable insights into optimizing road design, reducing maintenance costs, and promoting safer and more efficient transportation infrastructure [2].

Although environmental factors do contribute to asphalt degradation, their exclusion in this study is justified by the micro-scale analysis focus on a singular junction. This approach allows for a detailed exploration of the direct impact of traffic flow patterns, eliminating potentially confounding factors. This study's structure is as follows: the introduction and literature review provide a succinct overview of the current research landscape. The methodology section outlines the approaches adopted for future erosion rate calculations. The results section discusses the findings and their implications using the proposed models. The conclusion offers distinct suggestions and directions for future research.

Rutting deformation, specifically in asphalt pavement, is a significant issue in the transportation sector because of its negative impact on road safety and performance. The study incorporated meteorological data implicitly into the on-site measurements performed at an actual intersection. In light of the ever-changing and unpredictable characteristics of the environment, we made the decision to exclude meteorological data as explicit input parameters in our analysis. The determination is based on the realistic difficulties that arise when striving to regulate or influence meteorological circumstances while conducting field observations. Our study relied on the real-world changes caused by the different weather conditions at the junction. The dataset utilized for research already incorporates the impact of weather on pavement conditions, accurately depicting the intersection's genuine performance under varying environmental conditions. This methodology guarantees a comprehensive and accurate comprehension of how the pavement reacts to the intricate interaction of environmental elements. This, in turn, enhances the credibility and practicality of our discoveries in real-life scenarios.

The "lath and nail method" is one fundamental technique used to comprehensively evaluate pavement erosion. It precisely measures the depth of cracks and abnormalities on asphalt surfaces. This approach entails using an aluminum lath with precise dimensions and a specified cross-section, together with accurate measurements obtained by inserting nails into the pavement.

The aluminum lath is meticulously chosen for its robustness and pliability, enabling it to effortlessly adapt to the shape of the pavement. For measurements to be consistent and accurate, its precise dimensions are vital. An unchanging point of reference for determining the depth of diverse pavement features is the cross-section of the lath.

In order to implement the "lath and nail method," scientists strategically place nails into the pavement at predefined intervals all throughout the length of the lath. These nails penetrate asphalt cracks, holes, and irregularities to indicate depth. Subsequently, the profundity of these insertions is quantified, yielding significant information regarding the degree of degradation or impairment to the pavement surface.

By utilizing this approach, scientists are capable of quantifying and analyzing the extent of erosion, thereby facilitating the identification of potential areas that require maintenance or restorations. The lath and nail method facilitates a thorough comprehension of pavement condition through the systematic measurement of crack and irregularity depths. This knowledge is crucial in the formulation of efficient maintenance strategies and in guaranteeing the durability and safety of road surfaces.

Numerous studies have been carried out in order to gain an understanding of the underlying causes and make predictions regarding the deformation that big vehicle loads will have on asphalt surfaces. One of which is a prediction model for asphalt pavement deformation using artificial neural networks. The model considered various environmental factors, such as temperature and rainfall, in addition to traffic volume. The model accurately predicted heavy vehicle load asphalt pavement deformation [3, 4]. Laboratory tests examined asphalt's rutting deformation under heavy-load vehicles [5]. Tire pressure and axle load impacted rutting. Axle load and tire pressure affected asphalt pavement rutting, according to the findings.

Similarly, the study [6] presented a prediction model for asphalt pavement deformation using an artificial neural network. Different data parameters were used to train and validate the model. Results indicated that the model was able to accurately predict the deformation of asphalt pavement under heavy vehicle loads. The support vector machine (SVM) optimized using a genetic algorithm (GA) and created by the study [7] predicts asphalt pavement rutting based on field test data and was evaluated using mean absolute error, root mean square error, and correlation coefficient. The model accurately predicted asphalt pavement rutting. Likewise, the study [8] investigated asphalt pavement deformation using a genetic algorithm optimized back-propagation neural network. Root mean square error and correlation coefficient were used to evaluate the field test-based model. The findings proved that the model accurately predicted the deformity of asphalt pavement.

Another study investigated the relationship between traffic flow and asphalt deformation using video data extraction and processing. This study analyzed the data with a back-propagation algorithm to determine the impact of traffic flow on asphalt deformation. Findings indicated that traffic flow significantly affected asphalt deformation, and the model had good accuracy in predicting the deformation [9, 10]. Finally, references [11, 12] studied the effects of vehicle speed on rutting deformation of asphalt pavement using real-time traffic data. The study used a regression analysis to identify the relationship between vehicle speed and rutting deformation. Deformation caused by rutting was found to be significantly affected by vehicle speed.

The Yong study analyzed the effects of graphene on asphalt's performance and the effectiveness of Stone Matrix Asphalt (SMA) in pavement. Based on a Gansu Province highway project, graphene enhanced asphalt was used [13]. To prepare CRCM asphalt, which includes CRCM-SBS, CRCM-Sasobit/BRF, and CRCM-RARX, the same amount of crumb rubber and various amounts of composite additives were added [14]. Long-term road performance is assessed and predicted for self-ice-melting asphalt surfaces equipped with salt-storage materials in this lab study [15]. Employing Dynamic Mechanical Analysis, MA tested three variations of an asphalt mixture used for the surface course and six standard RIOH Track structures [16]. Zhang et al. [17] proposes a new fatigue life prediction that takes temperature load into account, which could be disregarded in inspections of steel deck welds on suspension bridges subjected to dynamic vehicle load. Liu makes use of the data that is reflective of the weather for a period of twenty-four hours during the summer [18]. Two-dimensional image technology obtains air-void data acquired from rutting sample sections with varied loading cycles (500, 1000, 1500, 2000, 2500, and 3000 times) [19]. Wu intends to conduct a comprehensive investigation to determine the law of skid resistance attenuation of SMA pavement [20]. Langa examines how high-density polyethylene (HDPE) modified asphalt binder changes in terms of its physical, rheological, and thermal properties after soybean oil is added [21].

To aid in the right decision-making processes, a long-term strategy for pavement preservation should include a thorough evaluation of the current road state. To predict flexible pavements' durability over time, the study [22] puts forward integrating Non-Destructive Testing (NDT) and ground truth data. Shaffie [23] proves RSM's statistical efficacy. Cao simulates the thermodynamic, diffusion, and adhesion effects of asphalt cement aging using molecular dynamics [24]. The study [25] carried out a model-based farm-scale exploratory study using two farms as case studies. In the study [26], researchers looked at 15 extracts from the peels of 5 different cultivars to determine their phytochemical makeup, antioxidant activity, tyrosinase influence, in vitro SPF, and cytotoxicity. As the service life increases, the actual load-carrying capacity of bridges gradually decreases due to the combined action of the environmental corrosion and repeated vehicle loads, resulting in shortened bridge service life. Nie studied fatigue reliability analysis and traffic load control of steel bridges based on artificial neural network [27]. The fatigue reliability index of a steel girder bridge over its whole life is investigated based on artificial neural networks. Hussein wants to emphasize the significance of planning marsh management, which may revitalize the marshes' natural world before drying through the Center for Marsh Revitalization in southern Iraq [28]. Cepa presents the main types of sensors and their applications in tunnels [29]. As discussed in the study [30], assessing pavement condition effectively helps making good decisions and provides longer-lasting pavement mixes.

In conclusion, the materials and methods employed in this research, particularly the lath and nail method, have proven to be instrumental in comprehensively evaluating pavement erosion. With its aluminum lath of a specific length and cross-section, the lath and nail method yield’s precise measurements which demonstrate wear, structural, and instability rutting. On-site measurements inevitably incorporate weather in-formation because environmental conditions affect pavement deterioration and rutting. Although data on weather was not explicitly used as an input parameter for machine learning (ML) instruction, it was unambiguously accounted for in the real-world metrics used in the training process. The lath and nail method and on-site measurements implicitly include weather effects, improving research reliability and leading to more accurate erosion coefficients and robust pavement management strategies.

Traffic flow's effect on road conditions is the study's main objective. Machine learning algorithms are used to analyse and simulate traffic flow data. In order to measure different types of asphalt erosion or decay, different measuring methods can be applied [31, 32]. Research results focus on the lath and nail method and future erosion coefficient will be lath and nail method related. For the lath and nail method, the changes in the pavement are measured by using an aluminium lath that is 4 meters in length. Typically, they have a rectangular cross-section and are made of solid wood or light metal, leaving no room for speculation. It is crucial that the measuring lath has no fewer than two supports. Figure 1 displays a sand patch and lath-and-nail method.

Erosion of roads and how it affects the flow of traffic are discussed. The predicted "lath" measurements are put forward in this paper. Figure 4 depicts the MLP model's prediction for the "lath" dataset set compared to the actual statistical value.

The progress made in artificial intelligence and image processing has created novel opportunities for engineering investigation, providing significant data for the assessment of road infrastructure and safety. The primary objective of this research endeavor was to evaluate the influence of heavy vehicles on the degradation of a particular intersection in Bosnia and Herzegovina. The study recognized the significant impact that vehicle retention and braking have on roadway conditions. Nevertheless, the research acknowledged certain constraints, such as its dependence on a restricted dataset, absence of meteorological data, and utilization of civil engineering measurements.

The research effectively utilized traffic data to develop a MLP model that predicted road erosion with remarkable precision, specifically in identifying heavy vehicles as substantial contributors to the erosion process. Recognizing the necessity for data originating from various intersections, the study put forth suggestions for future research avenues.

Assessment of newly collected intersection data from a distance of 75 meters yielded road erosion predictions. Placing considerable emphasis on the pragmatic implications for road maintenance and traffic management, the research underscored the model's capability to evaluate pavement surface flatness and pinpoint regions necessitating repair and maintenance.

It was emphasized that a diverse dataset is crucial for the robustness of a model, which suggests directions for future research. Looking into the future, it is imperative to thoroughly examine the effects of traffic flow management on road conditions while also integrating intelligent solutions to mitigate road erosion and improve safety. Additionally, for the benefit of a more sustainable future, research should investigate how climate conditions and vehicle fuel consumption affect road development strategies.

Further developments in AI and image processing technologies may enable future studies to analyze road deterioration and pavement deformation using larger datasets. By embracing state-of-the-art research and innovation, the engineering community possesses the capacity to make a positive impact on society and commuters alike by developing roads that are more intelligent, secure, and durable.