Towards a Technology-Driven Adaptive Decision Support System for Integrated Pavement and Maintenance strategies (TDADSS-IPM): focus on risk assessment framework for climate change adaptation
This addresses the vulnerability of road assets to climate change for infrastructure managers, but it is incremental as it builds on existing decision support systems with new adaptive features.
The paper tackles the problem of traditional decision support systems for pavement and maintenance being siloed and rigid, by introducing a technology-driven adaptive system (TDADSS-IPM) that uses a Bayesian Belief Network model for risk assessment of Danish roads under climate change, enabling real-time application and training over time.
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.