Comparative Study of Prior Models for Curb Opening Inlet Lengths and Neuro-Fuzzy Modeling for Hydraulic Design

Rapidly removing rainfall from roadways is necessary to avoid vehicle accidents caused by hydroplaning or suddenly unbalanced forces on the front wheels. Ensuring adequate water removal and minimal bypass requires correct sizing of drainage structures. Undepressed curb opening inlets (UCOIs) are often preferred along high-speed roads where curb depressions can cause a loss of vehicle control. Recent work has shown that classic curb inlet design equations can be in error for long curb opening inlets (>2 m). This study provides results of laboratory experiments that build on recent work to evaluate the performance of different curb inlet equations. A new approach using neuro-fuzzy modeling that applies the proven adaptive neuro-fuzzy inference systems (ANFIS) was evaluated for use in sizing UCOI. This study aims to find the method that has the best hydraulic performance. Results show that some earlier models actually estimate inlet lengths better than more recent design equations under some roadway configurations. The use of the ANFIS approach provides the lowest root mean square errors and mean absolute percentage errors when compared to available models and may be adopted in the practice of UCOI inlet design safely.