A data analytics case study assessing factors affecting pavement deflection values

Road networks are a national critical infrastructure. The road assets need to be monitored and maintained efficiently as their conditions deteriorate over time. The condition of one of such assets, road pavement, plays a major role in the road network maintenance programmes. Pavement conditions depend upon many factors such as pavement types, traffic and environmental conditions. This paper presents a data analytics case study for assessing the factors affecting the pavement deflection values measured by the traffic speed deflectometer (TSD) device. The analytics process includes acquisition and integration of data from multiple sources, data pre-processing, mining useful information from them and utilising data mining outputs for knowledge deployment. Data mining techniques are able to show how TSD outputs vary in different roads, traffic and environmental conditions. The generated data mining models map the TSD outputs to some classes and define correction factors for each class.

Quantitative data collection and study validity

The quality of data collection methods selected and the integrity of the data collected are integral tot eh success of a study. This chapter focuses on data collection and study validity. After reading the chapter, readers should be able to define types of data collection methods in quantitative research; list advantages and disadvantages of each method; discuss factors related to internal and external validity; critically evaluate data collection methods and discuss the need to operationalise variables of interest for data collection.