Prediction Of Shear Stress Distribution Research Articles

Prediction of shear stress distribution in compound channel with smooth converging floodplains

Abstract Climate change can have a profound impact on river flooding, leading to increased frequency and severity of floods. To mitigate these effects, it is crucial to focus on enhancing early warning systems and bolstering infrastructure resilience through improved forecasting. This proactive approach enables communities to better plan for and respond to flood events, thereby minimizing the adverse consequences of climate change on river floods. During river flooding, the channels often take on a compound nature, with varying geometries along the flow length. This complexity arises from construction and agricultural activities along the floodplains, resulting in converging, diverging, or skewed compound channels. Modelling the flow in these channels requires consideration of additional momentum transfer factors. In this study, machine learning techniques, including Gene Expression Programming (GEP), Artificial Neural Networks (ANN), and Support Vector Machines (SVM), were employed. The focus was on a compound channel with converging floodplains, predicting the shear force carried by the floodplains in terms of non-dimensional flow and hydraulic parameters. The findings indicate that the proposed ANN model outperformed GEP, SVM, and other established approaches in accurately predicting floodplain shear force. This research underscores the efficacy of utilizing machine learning techniques in the examination of river hydraulics.

MARS for Prediction of Shear Force and Discharge in Two-Stage Meandering Channel

AbstractAccurate prediction of shear stress distribution along the boundary in an open channel is the key to the solution of numerous critical engineering problems. This paper investigated the dist...

A comparative evaluation of shear stress modeling based on machine learning methods in small streams

Predicting shear stress distribution has proved to be a critical problem to solve. Hence, the basic objective of this paper is to develop a prediction of shear stress distribution by machine learning algorithms including artificial neural networks, classification and regression tree, generalized linear models. The data set, which is large and feature-rich, is utilized to improve machine learning-based predictive models and extract the most important predictive factors. The 10-fold cross-validation approach was used to determine the performances of prediction methods. The predictive performances of the proposed models were found to be very close to each other. However, the results indicated that the artificial neural network, which has the R value of 0.92 ± 0.03, achieved the best classification performance overall accuracy on the 10-fold holdout sample. The predictions of all machine learning models were well correlated with measurement data.

Prediction of boundary shear stress in circular and trapezoidal channels with entropy concept

Relying on the power law to develop new relationships, the present study developed the Shannon entropy concept to predict shear stress distribution. The presented method was evaluated and compared with valid laboratory results and the shear stress distribution surveyed through this concept in circular, circular with flat bed and trapezoidal channels. By increasing the flow depth in circular channels, the model presented herein gives an improved prediction of shear stress distribution, while in circular channels with flat bed it is better in predicting shear stress both at lower flow depth and subcritical flow conditions. In comparison, in trapezoidal channels the model is much better in predicting the shear stress distribution at lower flow depths. Accordingly, with a mean error percentage of 1.99% in circular channels, 3.69% in circular channels with flat bed and 4.1% in trapezoidal channels, this novel model shows good ability in predicting shear stress distribution.

Comparison between Shannon and Tsallis entropies for prediction of shear stress distribution in open channels

The concept of Tsallis entropy was applied to model the probability distribution functions for the shear stress magnitudes in circular channels (with filling ratios of 0.506, 0.666, 0.826), circular with flat bed (filling ratios of 0.333, 0.666), rectangular channel (1.34, 2, 3.94, 7.37 aspect ratios) and compound channel (with relative depths of 0.324, 0.46). The equation for the shear stress distribution was obtained according to the entropy maximization principle, and is able to estimate the shear stress distribution as much on the walls as the channel bed. The approach is also compared with the predictions obtained based on the Shannon entropy concept. By comparing the two prediction models, this study highlights the application of Tsallis entropy to estimate the shear stress distribution of open channels. Although the results of the two models are similar in the circular cross-section, the differences between them are more significant in circular with flat bed and rectangular channels. For a wide range of filling ratio values, experimental data are used to illustrate the accuracy and reliability of the proposed model.

Evaluation of the shear capacity of precast-prestressed hollow core slabs: numerical and experimental comparisons

Since eighties, 400 and 500 mm thick precast-prestressed concrete hollow core slabs, characterized by increasingly optimized cross-sections with non-circular voids, became very common. However, deeper slabs with long spans, which have to resist high line loads acting close to the supports, are subjected to initial web shear cracking and may fail at loads less than those predicted by traditional codes prescriptions. The shear strength capacity of these members without transverse reinforcement is evaluated through a campaign of detailed nonlinear finite element analyses, matching experimental test data collected from past programs. Constitutive models, based on nonlinear fracture mechanisms, are considered to numerically reproduce the experimental response of single span, simply supported, isolated hollow core units, highlighting web-shear failure mechanism, due to short development length and lack of transverse reinforcement. The adopted diffuse smeared fixed cracking constitutive model allows a reliable prediction of shear stress distributions and crack patterns for these members in their inelastic branch. The presence of a variable inclined strut is clearly evident. Peak shear stress is localized at the bottom side of the cross-section, rather than at the level of the centroid. The experienced brittle web-shear failure mechanism is governed by hollow core shapes with circular or non-circular voids, as evidenced by the evolution of the principal tensile strain distributions. Typically, less inclined, more rounded, diagonal crack, controlled by the smooth web width variation along depth, is opposed to a fairly constant variation of the fracture angle inclination, governed by the abrupt and irregular web width drop.