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A significant number of dams worldwide are very old and thus require a reassessment of maximum flood discharges due to siltation and climate change effects. The Piano Key Weir (PKW) is a relatively new labyrinth-type weir that has emerged as a viable solution for dam rehabilitation and new projects with space constraints. While various studies have been conducted in the past to investigate the influence of geometrical parameters of PKW, there is a lack of information regarding the effect of non-linear slope profile of keys on the discharge efficiency. In this paper, ogee profiles have been introduced in the outlet keys to study their influence on the discharge coefficient. The experiments were performed at the Hydraulics Laboratory, IIT Roorkee, India, using a flume 15 m long, 0.39 m wide and 0.52 m deep. These experiments involved four PKW models with different slope profiles in the outlet key. The results of the laboratory studies and RANS model simulations indicate that the PKW models with ogee shaped outlet keys are more efficient than the linear shaped counterpart for heads higher than their design head. The maximum increase in discharge coefficient for the PKW models with curvilinear outlet keys was 9%. [ABSTRACT FROM AUTHOR]

Scour phenomena remain a significant cause of instability in offshore structures. The present study estimates scour depths using physics-based numerical modelling and machine-learning (ML) algorithms. For the ML prediction, datasets were collected from previous studies, and the trained models checked against the statistical measures and reported outcomes. The numerical assessment of the scour depth has been also carried out for the current and coupled wave–current environment within a computational fluid dynamics framework with the aid of the open-source platform REEF3D. The outcomes are validated against the previously reported experimental studies. The results obtained from ML schemes demonstrated that the artificial neural network and adaptive neuro-fuzzy interface system models have an elevated level of effectiveness compared with the other models. Whereas the numerical analysis results show a good agreement against the reported values. For the current only conditions, the normalized scour depth (S/D) at the front and rear end of the pier is 0.65 and 0.81. For the wave–current conditions, the normalized scour depth (S/D) is 0.26. The study highlights the importance of machine learning and physics-based numerical modelling to assess scour depth within a reasonable time frame without compromising accuracy. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'. [ABSTRACT FROM AUTHOR]

Scour phenomena remain a significant cause of instability in offshore structures. The present study estimates scour depths using physics-based numerical modelling and machine-learning (ML) algorithms. For the ML prediction, datasets were collected from previous studies, and the trained models checked against the statistical measures and reported outcomes. The numerical assessment of the scour depth has been also carried out for the current and coupled wave-current environment within a computational fluid dynamics framework with the aid of the open-source platform REEF3D. The outcomes are validated against the previously reported experimental studies. The results obtained from ML schemes demonstrated that the artificial neural network and adaptive neuro-fuzzy interface system models have an elevated level of effectiveness compared with the other models. Whereas the numerical analysis results show a good agreement against the reported values. For the current only conditions, the normalized scour depth ( S / D ) at the front and rear end of the pier is 0.65 and 0.81. For the wave-current conditions, the normalized scour depth ( S / D ) is 0.26. The study highlights the importance of machine learning and physics-based numerical modelling to assess scour depth within a reasonable time frame without compromising accuracy. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'.