Cross-drainage hydraulic structures such as culverts and bridges in urban landscapes are prone to get blocked by the transported debris (e.g., urban, vegetated), which often reduces their hydraulic capacity and triggers flash floods. Unavailability of relevant data from blockage-originated flooding events and complex nature of debris accumulation are highlighted factors hindering the research within the blockage management domain. Wollongong City Council (WCC) blockage conduit policy is the leading formal guidelines to incorporate blockage into design guidelines; however, are criticized by the hydraulic engineers for its dependence on the post-flood visual inspections (i.e., visual blockage) instead of peak floods hydraulic investigations (i.e., hydraulic blockage). Apparently, no quantifiable relationship is reported between the visual blockage and hydraulic blockage; therefore, many consider WCC blockage guidelines invalid. This paper exploits the power of Artificial Intelligence (AI
Blockage of culverts causes reduction in hydraulic capacity and is one of the main contributors to trigger urban flooding. However, the highly non-linear nature of debris interaction during the flood and lack of blockage-related data from actual flooding events make conventional numerical modelling almost impossible. Literature investigating blockage phenomena reports blockage as a complex hydraulic process, which suggests exploring adaptive solutions using latest technologies. In this context, motivated by the success of data-driven algorithms, in this article, four data driven models (i.e., K-NN, ANN, SVR, 1D-CNN) are implemented to predict the hydraulic blockage at culverts. A new numerical Hydraulics-Lab Blockage Dataset (HBD) is established from a series of lab-scale hydraulic experiments. From the experimental investigations, the ANN model was reported as the best with a R 2 score of 0.95. A potential use-case of presented research for real-world application is also discussed to