The current practice of bridge maintenance decision-making is informed by subjective visual inspection data with a long time interval between measurements, which creates inefficiencies and possible loss of value in the maintenance entity’s capabilities. New opportunities from technology-related innovations for measurement devices are available for better-informed decision-making and for improved knowledge on the condition of a bridge. Even though, the adoption and use of these opportunities seems to be disregarded by the market and the main focus of innovation pushing organisations is mostly on the technical capabilities of these systems. This research presents a study on the effects of using technology-related opportunities on maintenance decision-making from a usercentric perspective. It performed a serious gaming experiment where bridge maintenance decision-making professionals were questioned to set up maintenance advices based on different types of data. No previous similar research was found through extensive literature search. Bridge deterioration scenarios were presented to experts throughout a within-subject fractional factorial experiment design. The experiment treatment groups consisted of data presentation from new innovative or potential bridge condition assessment mechanisms and the experiment control group consisted of the current data source, which is visual inspection. To observe the effects, the experiment used a quantitative and qualitative approach that considered expert judgement, maintenance decisions, and explanations of the experts for observing whether differences occurred between the different settings. It was indicated that significant differences were present if new data sources were added to the current visual inspection data, but no significant differences were observed between different types of data sources. It is expected that the differences are caused by the ability to observe quantified values of important performance indicators better, and that they provided indicative values that can be used during decision-making. It is expected that the latter described indifferences were caused by a lack of guidance by the market on how to use new data sources properly and consistently. These outcomes mean that for efficient and effective adoption of new data sources, more standards and guidelines about threshold values and causal links between deterioration parameters and possible deterioration causes are needed for practical guidance during maintenance decision-making. It suggests that in order to provide a broad facilitation for adoption of new data sources, long-term strategy behaviour must be considered by the market in order to provide a safe space for pilots and trials to learn about these innovations.

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.