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.

翻译：面向路面与养护策略的决策支持系统（DSS）传统上以孤岛模式设计，导致系统仅能实现局部最优。此外，由于工业4.0时代的大数据应用至今尚未普及，此类DSS最初并未设计为可自适应不确定性来源，从而导致决策刚性。受道路资产对气候现象脆弱性的启发，本文前瞻性地提出一种面向集成化路面与养护活动的技术驱动型自适应决策支持系统，即TDADSS-IPM。作为该DSS的一部分，通过贝叶斯信念网络（BBN）构建自下而上的风险评估模型，以识别丹麦道路在天气条件下的实际状况。该模型填补了知识领域的空白，并开发出一个可随时间训练、并能实时应用于实际事件的平台。