# Economic Impact Analysis of the Application of Different
## 1 Introduction  
The objective of this study was to evaluate the economic impact of the application of different equations for pavement performance models. The impact of pavement performance models on repair technology and timing is well known [1]; however, there are other economic implications beyond repair technology costs linked to the road user [2]. This paper presents the sensitivity of road user costs and subsequent results of life cycle cost analysis (LCCA) and cost-benefit analysis (CBA) to even small changes in pavement performance models.  
In addition, a practical method for deriving such pavement performance models is described. This method uses the synergy between accelerated pavement testing and long-term pavement performance monitoring described in separate sections.  
_State of the Art_  
Currently, practitioners understand the importance of reliable and accrued pavement performance models. The pavement management system tools and their accuracy is dependent on the accuracy of the deterioration of the selected pavement parameter. Even more so, if we consider that these tools include LCA forecast that predicts pavement deterioration based on degradation characteristic of selected parameter [3].  
Following the Pavement Management Guide (second edition) [4], pavement deterioration models can be classified as deterministic, probabilistic, Bayesian, and subjective (or expert based). [PERSON] [5] defined three methodologies groups of performance modeling. The first group is defined as regression analysis techniques, (empirical performance models and mechanistic-empirical models). The second is defined as probabilistic performance modeling (Bayesian and Markov probabilistic modeling [6; 7]) and the third group is defined as artificial neural networks modeling (back-propagation networks [8]). Nevertheless, despite the wide range of available deterioration models, the deterministic and probabilistic groups are referred to as the basic groups because they attract the greatest attention [9].  
These models, which are in the line with the current state of the art of deterioration models, can be also divided into models according to the nature of the data employed for modeling; for example, [PERSON] [10] proposed various classifications, and, according to the conceptual format, they can be divided as purely mechanistic, purely empirical and mechanistic-empirical. Most common are mechanistic-empirical methods, which are also included in design methodologies as MEPDG (Mechanistic Empirical Pavement Design Guide) from AASHTO (The American Association of State Highway Transportation) [11; 12].  
The state-of-the-art deterioration models of asphalt pavements combine material science by including the rheological properties of paving materials [13] with real-life pavement monitoring and experimental testing and mathematic simulations [14]. However, currently, there is a growing interest in the literature in regard to artificial neural network (ANN) models [15; 16]. ANN application has increased drastically in the past few years and it is commonly used in combination with deep learning techniques. Such a combination can help address complex problems in pavement engineering [17]. Some authors regard ANN application as a \"black box\", where the exact relationship between the predicted variable (dependent variable) and the independent one(s) is unknown, and hence, the real influence of each parameter cannot be known [18].