LAUSR

Abstract The selection and use of technical parameters and performance indicators plays an essential role in the pavement management process. It is known that if more parameters are used, a more accurate evaluation of pavement condition is achieved, improving the choice of maintenance and rehabilitation interventions. However, one of the most expensive activities of the pavement management process is data collection. Accordingly, it is necessary to find a balance between the data collected and the real needs of the process. This paper presents a new approach for the development of pavement condition indicators using a machine learning algorithm named regularised regression with lasso. The present discussion is supported by a case study, which compares the proposed method with current practice for the description of the condition of a Portuguese motorway. The results suggest that the application of machine learning methods can improve the accuracy of pavement condition indicators when less data are available, contributing to achieve a balance between the needed data and information obtained.