New Data Sources in Road Infrastructure Management

Abstract

Professionals in the Operations and Maintenance phase of national road
infrastructure projects are making decisions with large consequences based on low frequency measurements and subjectivity prone expert observations. A solution is expected in sensor innovations, IoT and User Generated Data to generate more frequent measurements and less subjective observations. The use of these methods has been researched and proven, however the main focus of these studies was often improvement of the technological capabilities or implementation in current practises with limited research into their contribution and effects on professionals in the construction sector. This research describes an experiment that tested the effects of more data and more diverse data on the decision-making of professionals in the construction industry. An attempt to answer this question is made by modelling different data sources into a Serious Game and testing the assumptions. After analysis of the gaming data, the questionnaires and the debriefing it can be concluded that in this experiment there was a correlation between better assessments and higher scores. However experts did not assess damage differently when presented with extra information, nor did they make significantly different decisions. From the qualitative section of the experiment the explanation was found that the extra information proved too much and experts were able to extrapolate with the marginal data that represents the current industry practise. This suggests that new information requires training, as our built environment gets richer in terms of data, the assessment of this built environment becomes too much for humans to cope with and solutions can be sought in the application smart algorithms, Machine Learning and Artificial Intelligence.