Development of group method of data handling based on genetic algorithm to predict incipient motion in rigid rectangular storm water channel

Sediment transport is a prevalent vital process in uvial and coastalenvironments, and \incipient motion" is an issue inseparably bound to this topic. Thisstudy utilizes a novel hybrid method based on Group Method of Data Handling (GMDH)and Genetic Algorithm (GA) to design GMDH structural (GMDH-GA). Also, SingularValue Decomposition (SVD) was utilized to compute the linear coefficient vectors. Inorder to predict the densimetric Froude number (Fr), the ratio of median diameter ofparticle size to hydraulic radius (d=R) and the ratio of sediment deposit thickness tohydraulic radius (ts=R) are utilized as e ective parameters. Using three di erent sources ofexperimental data and GMDH-GA model, a new equation is proposed to predict incipientmotion. The performance of development equation is compared using GMDH-GA andtraditional equations . The results indicate that the presented equation is more accurate(RMSE= 0:18 andMAPE= 6:48%) than traditional methods. Also, a sensitivityanalysis is presented to study the performance of each input combination in predictingincipient motion (15) Development of Group Method of Data Handling based on Genetic Algorithm to predict incipient motion in rigid rectangular storm water channel

Group method of data handling to predict scour depth around vertical piles under regular waves

AbstractThis paper presents a new application of the Group Method Of Data Handling (GMDH), to predict pile scour depth exposed to waves. The GMDH network was developed using the Levenberg–Marquardt (LM) method in the training stage for scour prediction. Scour depth due to regular waves was modeled as a function of five dimensionless parameters, including pile Reynolds number, grain Reynolds number, sediment number, Keulegan–Carpenter number, and shields parameter. The testing results of the GMDH-LM were compared with those obtained using the Adaptive Neuro-Fuzzy Inference System (ANFIS), Radial Basis Function-Neural Network (RBF-NN), and empirical equations. In particular, the GMDH-LM provided the most accurate prediction of scour depth compared to other models. Also, the Keulegan–Carpenter number has been determined as the most effective parameter on scour depth through a sensitivity analysis. The GMDH-LM was utilized successfully to investigate the influence of the pile cross section and Keulegan–Carpenter number on scour depth

New stochastic modeling strategy on the prediction enhancement of pier scour depth in cohesive bed materials

Abstract Scouring around the piers, especially in cohesive bed materials, is a fully stochastic phenomenon and a reliable prediction of scour depth is still a challenging concern for bridge designers. This study introduces a new stochastic model based on the integration of Group Method of Data Handling (GMDH) and Generalized Likelihood Uncertainty Estimation (GLUE) to predict scour depth around piers in cohesive soils. The GLUE approach is developed to estimate the related parameters whereas the GMDH model is used for the prediction target. To assess the adequacy of the GMDH-GLUE model, the conventional GMDH and genetic programming (GP) models are also developed for evaluation. Several statistical performance indicators are computed over both the training and testing phases for the prediction accuracy validation. Based on the attained numerical indicators, the proposed GMDH-GLUE model revealed better predictability performance of pier scour depth against the benchmark models as well as several gathered literature studies. To provide an informative comparison among the proposed techniques (i.e. GMDH-GLUE, GMDH, and GP models), an improvement index () is employed. Results indicated that the GMDH-GLUE model achieved = 6% and = 3%, demonstrating satisfying performance improvement in comparison with the previously proposed GMDH model

Investigation on scour protection of submarine piggyback pipeline

YesThis paper presents the results of laboratory experiments and numerical simulations to investigate the effect of different piggyback pipeline configuration on the morphology of local seabed scour subject to steady currents. Piggyback pipeline configuration investigated includes the commonly used piggyback pipeline, namely a small pipe attached on the top of large pipe and new form of piggyback pipeline proposed in this study in which a small pipe is attached to the large pipe on the upstream and downstream side, respectively. Pressure gradient, drag coefficient, lift coefficient and scour extent around pipelines are measured and analyzed for a range of pipelines and current conditions. Results show that the vortex strength downstream of the commonly used piggyback pipeline is larger than that for a single as well as the new piggyback pipeline under the same condition. This new type piggyback pipeline can effectively reduce the depth and width of the scour hole. In particular, when the ratio of the small pipe diameter over the large pipe diameter is greater than 0.3, little scour under this new type piggyback pipeline occurs for the test conditions. The bed topography downstream of the pipe has also been altered to favor the backfill.National Natural Science Foundation of China (No. 51279189)

Application of nature-inspired optimization algorithms to ANFIS model to predict wave-induced scour depth around pipelines

Wave-induced scour depth below pipelines is a physically complex phenomenon, whose reliable prediction may be challenging for pipeline designers. This study shows the application of adaptive neuro-fuzzy inference system (ANFIS) incorporated with particle swarm optimization (ANFIS-PSO), ant colony (ANFIS-ACO), differential evolution (ANFIS-DE) and genetic algorithm (ANFIS-GA) and assesses the scour depth prediction performance and associated uncertainty in different scour conditions including live-bed and clear-water. To this end, the non-dimensional parameters Shields number (θ), Keulegan–Carpenter number (KC) and embedded depth to diameter of pipe ratio (e=D) are considered as prediction variables. Results indicate that the ANFIS-PSO model (R 2 live bed ¼ 0:832 and R 2 clear water ¼ 0:984) is the most accurate predictive model in both scour conditions when all three mentioned non-dimensional input parameters are included. Besides, the ANFIS-PSO model shows a better prediction performance than recently developed models. Based on the uncertainty analysis results, the prediction of scour depth is characterized by larger uncertainty in the clear-water condition, associated with both model structure and input variable combination, than in live-bed condition. Furthermore, the uncertainty in scour depth prediction for both live-bed and clear-water conditions is due more to the input variable combination (R-factor ave ¼ 4:3) than it is due to the model structure (R-factor ave ¼ 2:2)

New formulations for prediction of velocity at limit of deposition in storm sewers based on a stochastic technique.

AbstractSedimentation in storm sewers strongly depends on velocity at limit of deposition. This study provides application of a novel stochastic-based model to predict the densimetric Froude number in sewer pipes. In this way, the generalized likelihood uncertainty estimation (GLUE) is used to develop two parametric equations, called GLUE-based four-parameter and GLUE-based two-parameter (GBTP) models to enhance the prediction accuracy of the velocity at the limit of deposition. A number of performance indices are calculated in training and testing phases to compare the developed models with the conventional regression-based equations available in the literature. Based on the obtained performance indices and some graphical techniques, the research findings confirm that a significant enhancement in prediction performance is achieved through the proposed GBTP compared with the previously developed formulas in the literature. To make a quantified comparison between the established and literature models, an index, called improvement index (IM), is computed. This index is a resultant of all the selected indices, and this indicator demonstrates that GBTP is capable of providing the most performance improvement in both training () and testing () phases, comparing with a well-known formula in this context

Scour Propagation Rates around Offshore Pipelines Exposed to Currents by Applying Data-Driven Models

Offshore pipelines are occasionally exposed to scouring processes; detrimental impacts on their safety are inevitable. The process of scouring propagation around offshore pipelines is naturally complex and is mainly due to currents and/or waves. There is a considerable demand for the safe design of offshore pipelines exposed to scouring phenomena. Therefore, scouring propagation patterns must be focused on. In the present research, machine learning (ML) models are applied to achieve equations for the prediction of the scouring propagation rate around pipelines due to currents. The approaching flow Froude number, the ratio of embedment depth to pipeline diameter, the Shields parameter, and the current angle of attack to the pipeline were considered the main dimensionless factors from the reliable literature. ML models were developed based on various setting parameters and optimization strategies coming from evolutionary and classification contents. Moreover, the explicit equations yielded from ML models were used to demonstrate how the proposed approaches are in harmony with experimental observations. The performance of ML models was assessed utilizing statistical benchmarks. The results revealed that the equations given by ML models provided reliable and physically consistent predictions of scouring propagation rates regarding their comparison with scouring tests

Experimental, Numerical and Field Approaches to Scour Research

This book presents fourteen state-of-the-art research papers prepared by research scientists and engineers around the world. They explore the subject of scour related to bridge piers, monopiles, propellers, turbines, weirs, dams, grade-control structures, and pipelines. Their works are based on three different research methodologies, namely experimental, numerical, and field approaches