## 3 Results and Discussion
### Keywords and Trending Topics of the Research on Pavement Management
#### 3.2.1 Pavement Data Collection
As they were time consuming and expensive, they were substituted by automated distress survey methods, ranging from high-speed contactless laser sensors, to devices recording video images of pavement surface. Later, processing algorithms, usually based on neural networks, are employed to identify the types of cracks and other distresses. Usual problems are classification errors and long processing times [68]. Papers published at initial years of the 2010s offered new solutions to these problems. On one hand, new tools were proposed [69; 70]. On the other hand, new algorithms were developed to process images [68; 71; 72; 73; 74]. Even a combination of both techniques was proposed [75]. Based on the results of the International Road Roughness carried out in Brazil in 1982 [76], the World Bank developed the International Roughness Index (IRI), and due to its stability over time and transferability throughout the world, it has become a recommended standard for road roughness or smoothness evaluation [77; 78]. Instead of calculating IRI in a one-way vertical section, [PERSON] et al. [79] proposed an automatic gadget using five laser probes to measure the whole cross section in urban roads. The Pavement Condition Index (PCI) is a very common distress survey method deployed in the United States. Alternatives techniques were presented to predict the PCI, by means of artificial neuronal networks (ANN) and genetic programming [80] or a fuzzy logic-based system [81]. Pavement structural capacity evaluation indicates pavement remaining life, i.e., the quantity of load repetitions that it can still withstand. Structural capacity of a pavement takes into account the structural capacity of all the layers of the pavement, as well as the condition of the subgrade [82]. Pavement structural evaluation methods are usually divided into destructive and non-destructive tests. Among the non-destructive techniques, pavement deflection is currently the most widely employed. One of the deflection measurement devices employed is the Benkelman beam, also deployed to measure the full deflection basin on forest roads [83]. Another device is the falling weight deflectometer (FWD), whose results were adjusted utilizing different sources of temperature gradient data and models [84]. Nevertheless, as these tools need to interrupt the traffic to operate, their use at the network level is limited, alternative devices are introduced, especially the rolling wheel deflectometer (RWD), because it can measure at traffic speeds. In Louisiana, an acceptable repeatability of RWD measurements at different test speeds was demonstrated [85], and the RWD index was developed as a useful index for pavement structural condition assessment, but not for selection of the correct treatment [86]. The structural condition cannot be forecasted from surface condition data [87]. Therefore, different decisions are taken if only functional condition parameters are employed, or if deflection tests are included in the decision process [88]. Hence, the introduction of a structural capacity index is suggested in network-level decisions to find the most cost-effective rehabilitation option [89]. On this basis, simplified deflection-based analytical techniques [90] and a new index related to the Structural Capacity Index (SCI) [91] have been introduced. Other new indices have been also proposed, like the pavement-assessment triangular model [92] or an index based on a visual evaluation of the conditions of road elements and pavement [93]. Besides these, normally employed indices were reconsidered to improve their calculation method, like the algorithm of the SCI [94], and the Pavement Condition Ratio (PCR) by means of fuzzy reasoning theories [95]. Finally, new methods for pavement segmentation have been also presented [96; 97; 98].