  Decision Support Systems for pavement and maintenance strategies have
traditionally been designed as silos led to local optimum systems. Moreover,
since big data usage didn't exist as result of Industry 4.0 as of today, DSSs
were not initially designed adaptive to the sources of uncertainties led to
rigid decisions. Motivated by the vulnerability of the road assets to the
climate phenomena, this paper takes a visionary step towards introducing a
Technology-Driven Adaptive Decision Support System for Integrated Pavement and
Maintenance activities called TDADSS-IPM. As part of such DSS, a bottom-up risk
assessment model is met via Bayesian Belief Networks (BBN) to realize the
actual condition of the Danish roads due to weather condition. Such model fills
the gaps in the knowledge domain and develops a platform that can be trained
over time, and applied in real-time to the actual event.
