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

2.1 Introduction to asphalt deformation and lath and nail method

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.

2.2 Different types of testing and mixture effects

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.

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.

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Figure 4. MLP "lath" predictions vs measured statistical value

The test stage includes 30% of the dataset, the calculation of the overall test set average absolute difference is 4.88%, R2=0.9948 and (Mean squared error) MSE=0.00042. Enhancements to the model are range and size dependent.

Figure 4 presents a prediction based on the normalized and linearized samples. The MLP projections use a dataset that has been arbitrarily divided into 70% training data and 30% testing data. Stochastic gradient descent is used for optimization, and the back-propagation algorithm is used for learning in the MLP model, with square error as the loss function. Table 2 gives a sample of test set. Last column (Predicted value) shows MLP results so we can compare to measured/statistical values Road erosion (lath). MLP takes rest of the table as input.

MLP prediction is accurate because predicted and measured values match. Heavy vehicles erode roads, as shown.

In Table 3, we show the result from right lane learned data set, meaning based on trained MLP model, we input new data points to understand the underlying impact of different inputs.

After 4.6 years since the road was completed, the actual measurement of road erosion (lath) is 60 mm. The limitations are related to the dataset as only data from one intersection was used for modeling. Traffic can drastically alter street conditions. Using AI, we can predict the erosion and significantly improve control of the traffic flow to optimize road erosion due to the traffic. The real test uses measurements from the 2nd intersection. The introduction to a new intersection data is the next step before the model is applied. The "Lath" measurement can be seen in Figure 5.

With the "Lath" standard reading equal to 16 mm, procedure on how the "Lath" standard reading is done can be seen from the Figure 5.

In Figure 6, intersections sketch is provided with indicated points of observations.

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Figure 5. "Lath" with measurements taken 75m from the intersection

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Figure 6. Intersection two sketch with indicated points of observations as T1 and T2 (meter)

All the measurements are summarized in Table 4, and all of them are for T1 location.

Both Table 4 input parameters are derived from traffic/statistical data, and the time interval is identical to that of the initial intersection. Table 4's first two parameters, determine the next three parameters. Since the first intersection was observed at 25 m and the second at 75 m, the values have to be considerably smaller than those from the first intersection. MLP Road erosion (lath) model prediction is 16.62 mm.

Finally, MLP that learned from right lane is used to predict the behavior in the left lane (shown in Figure 7 as T2 point). This impact is shown in Table 5 and Figure 8.

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Figure 7. A visual representation of the intersection used. T1 lane is used to teach AI the erosion and T2 is used for prediction

The following inputs for heavy and light vehicles are calculated by applying the known spectrum of past years which is later used to calculate the number of vehicles in the future. Other input variables are calculated with respect to the average and percentage values for the left lane which is explained in detail before.

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Figure 8. Model based visualization for the 10-year road erosion (lath)

Figure 8 is a visual representation of Table 5. After 7-8 years, asphalt characteristics are decaying much faster. With this result we can say that focusing on the road intersection segment quality and traffic fluidity is a higher priority than the other road segments.

The predicted outcomes that have been provided possess considerable importance within the domains of road maintenance and traffic management. Gaining insight into the relationship between traffic patterns and road erosion enables the implementation of proactive strategies to enhance road quality and durability. The MLP model possesses predictive capabilities that empower stakeholders, including transportation authorities and urban planners, to foresee forthcoming road deterioration and execute focused interventions. The scope of these interventions may encompass strategic road maintenance schedules and the optimization of traffic flow patterns, both of which ultimately contribute to the sustainability of infrastructure. Furthermore, an in-depth examination of the economic ramifications of these projections could assist policymakers in the judicious allocation of resources and the prioritization of high-impact areas. Fundamentally, a more comprehensive examination of the pragmatic implementations and ramifications of the outcomes of the MLP model would augment the research's worth in the domains of infrastructure management and transportation engineering.

Taking advantage of projected values enables the evaluation of pavement surface flatness by identifying areas of elevation or hollow that require grinding or filling. The High-Low Detector or Rolling Straight Edge gauges vertical deviations in increments of 0.125" (1 mm), with magnified readings spanning up to 0.25" (6.4 mm), indicating high or low areas. In accordance with these measurements, our methodology initially recorded a significantly elevated reading for low areas (60 mm) at the training intersection. The acceptable discrepancy between the predicted and actual values is clearly illustrated in Figure 4, where the mean absolute difference is recorded as 4.88%. Moreover, by including a wider range of intersections and fluctuating traffic flows, a more diverse dataset could potentially strengthen the model's ability to adapt to data fluctuations.