There has been significant research on the efficient and systematic management of road pavements as the aging of road pavements is rapidly progressing around the world. The road pavement management tasks can be broadly classified into (1) survey, (2) analysis, and (3) decision-making tasks. The recent research related to the investigation and analysis stage is represented by research utilizing surface scan images that are collected by roadway surface scanning vehicles using machines (e.g., Artificial Intelligence, machine learning, and deep learning) [1,2,3]. A majority of these studies attempted to increase the detection rate of distresses in surface scan images. However, it was observed that these methods do not have a high detection rate of distresses compared to the current approach where distress is analyzed by humans. Further, the false positive rate is still high. Moreover, these methods do not consider distress generation mechanisms and environmental factors. These limitations have limited their adoption in actual maintenance and rehabilitation (M&R) decision making. Regarding the decision-making stage, studies on segmentation prioritization and life cycle analysis are prevalent. Until recently, most road management organizations considered the overall pavement condition based on the pavement condition index, and the total surface distress amount and flatness are primary variables to identify the pavement condition at the network level and make management decisions such as selecting repair sections [4,5,6]. In this case, the project-level investigation of specific sections aims at rehabilitation and explores alternative construction methods to address the causes of representative distress. However, the selection process for these methods still relies on the subjective judgment of managers or field experts. Thus, there are some limitations in terms of time and cost. Furthermore, improper timing and method selection may increase the budget for pavement management and overhead costs [7].

Since the 1980s, state departments of transportation (DOTs) across the United States have extensively researched pavement condition ratings (PCRs) based on Mechanistic-Empirical (ME) design for pavement management at the network level [8]. During this period, individual deductive curves for various distresses occurring in road pavements were developed to measure the deterioration rate using time series analysis [4]. The management of a wider range of road networks was systematically conducted using non-linear regression modeling with the distress structural and roughness index as the basis [5]. In the early 2000s, computing approaches (machine/deep learning, AI, etc.) and satellite technologies (especially geographic information systems (GIS) and global positioning systems (GPS)) were used to perform time series analysis on the same pavement sections. As a result, future pavement management methods based on imaging and scanning, and automatic interpretation technologies were introduced [9]. The summary of the studies on predicting the future performance of road pavements revealed approximately 40 different analysis methodologies, including general statistical analysis, regression analysis, count data modeling, survival analysis, stochastic process modeling, supervised learning, and Bayesian analysis [10].

Despite significant research on predicting the future pavement condition spanning over 40 years, certain challenges persist, including the need for high predictive power due to varying rates of deterioration influenced by geographical environment, climate, equivalent single axial load (ESAL), vehicle-type distribution, and material formulation characteristics [11,12,13,14,15]. Although some studies adopted the international roughness index (IRI) and the distress index, which can represent various distresses that occur on the road pavement surface [16,17,18], they could not achieve high predictive power due to the impact on material properties (ductility and brittleness) and traffic volume distribution. The NCDoT (North Carolina Department of Transportation) in the U.S. is actively researching how to effectively maintain the 79,000-mile network in North Carolina through cost–benefit analysis. These studies have enhanced the predictive value’s explanatory power by operating separate deterioration models for each distress, deriving weighting factors and calibrating coefficient values based on performance data collected annually [19]. Similarly, the IDOT (Indiana Department of Transportation) operates its own PCI (Pavement Performance Index) based on individual prediction models and weights for representative variables (crack, rut, roughness, faulting, and friction) [20]. The studies mentioned so far have predominantly focused on utilizing regression modeling to predict future pavement conditions. However, compared to this approach, there has been relatively limited research using machine learning methods that can achieve higher accuracy. Especially when dealing with the complexity of predicting road pavement deterioration while considering various traffic and environmental loadings, there is a need for research to go beyond relying only on regression equations [21]. Additionally, the necessity of including machine learning approaches for deriving more precise conclusions from extensive road pavement networks has been increasingly recognized [22]. A notable example comes from the Florida Department of Transportation (DOT), where they have introduced methods involving recurrent neural networks (RNNs), Deep Neural Networks (DNNs), gated recurrent units (GRUs), long short-term memory (LSTM), and hybrid (LSTM-FCNN) models to process time series data representing continuous pavement conditions [23]. Deep Neural Networks (DNNs) have been utilized as a predictive tool for the pavement condition index by using a dataset of 536,848 samples. The development and training of various models with different hyperparameters and architectures demonstrated superior performance over traditional linear and non-linear regression models [24]. Additionally, utilizing DNNs with the long-term pavement performance (LTPP) database yielded higher predictive accuracy compared to current practice and multivariate linear regression models, offering potential applications in relative impact assessment and prioritization [25]. In Iowa, the Department of Transportation (DOT) utilized long short-term memory (LSTM) networks for modeling pavement deterioration across three pavement types: asphalt, Portland cement concrete, and composite pavements. This LSTM model achieved higher prediction accuracy over time for all pavement types, suggesting an improvement in future pavement performance prediction and the overall efficiency of pavement management systems compared to traditional regression models [26].

Furthermore, to address the challenge of standardizing maintenance strategies for road pavements with significant variability due to external factors, the application of Markov chain-based approaches has been proposed. These approaches aim to balance costs and effects from the life-cycle cost (LCC) perspective [27]. Upon reviewing most of the developed predictive models, a distinguishing aspect of this study is its focus on the concrete pavement as opposed to the majority of global research centered around asphalt pavement [28].

When establishing the causal relationships behind various damages on road surfaces, a significant amount of research evidence is often required. Therefore, efforts have been made to investigate relatively straightforward correlations, such as those between distress and roughness, using techniques like artificial neural networks (ANNs) and genetic programming (GP) to obtain results [29,30]. Furthermore, the impact of IRI on distress has been analyzed rather than conducting regression analysis through algorithms that process high-dimensional data by utilizing complex machine learning (Lasso: least absolute shrinkage and selection operator; SVR: support vector regression, regression tree, random forests method, etc.) for IRI [31]. The aforementioned studies, which were based on regression modeling, used various computing technologies to increase the predictive power. Validation between two predictive equations is performed through 𝑅₂, root mean squared error and mean absolute error [32].