# Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions
## 1 Introduction
### How Have Pavement Management Decisions and Approaches Been Supported by Data Analytics?  
Once data have been identified in a system, the next important decision is the type of modelling and estimation to be done to support maintenance decisions by predicting future conditions to understand and manage maintenance and rehabilitation strategies. Models are typically grouped into four categories [9] as seen in Table 1. The application of them is heavily based on the available resources. There have been numerous models produced and researched with the majority of them being specific to a combination of variable or conditions, highlighting the need for a workflow that can work with different variable combinations [23].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Approach** & **Description** \\ \hline \multirow{3}{*}{Deterministic models} & Models that produce a single dependent value such as pavement condition from a combination of different variables concerning the characteristics of the pavement and network \\  & Models that predict a range of values for the dependent variable with probabilities of changes based on different conditions and timelines \\ \multirow{3}{*}{Bayesian models} & Models that combine objective and subjective data and having their variables described in a probabilistic distribution manner \\  & Similar to deterministic ones but with structures based on opinion and not historical data \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of typical types of pavement performance models.  
Considering and using these model types, there are several areas within the PMS decision-making processes where Artificial intelligence-based systems can help support decisions. These include estimation of pavement condition, assessment of maintenance needs, identification and selection of maintenance actions and prioritization of maintenance programs [24]. Studies have tried to develop algorithms using techniques such as decision trees to predict Pavement Condition Index (PCI) [25] values (a common index used to represent road conditions), and therefore future pavement conditions of road sections using input data such as historical PCI values, weather, historical maintenance data and traffic [26]. Additional studies have focused on the International Roughness Index (IRI), which is an index that considers the roughness of the profile of the road [27]. Using this index, random forests regressions have been used to predict values of IRI to determine pavement roughness of sections utilizing traffic information, previous IRI values and pavement distresses [28]. Artificial Neural networks (ANN) have also been constructed using the IRI as an input to identify maintenance strategies with reliance on large national datasets [29; 30]. Other studies have tried to predict PCI values using neural networks. Planning of road interventions utilizing Fuzzy based networks have also been considered [18]. In other cases, ANNs were utilized to predict an exact pavement maintenance decision based on inputs of distress data, functional class, traffic and pavement structure [31]. Another method of analysis is the use of Genetic Algorithms, which were shown as an effective tool for predicting maintenance programs and have been combined within neural networks to bolster their effectiveness [32; 33; 34]. These previous studies all represent attempts at helping plan maintenance interventions but have commonly relied on large databases. Additionally, whilst interoperability issues in machine learning have been raised about the difficulty in understanding relationships between model outputs and inputs [35], feature selection has been successfully used to filter out redundant data, improve accuracies and help produce more explainable models [36].