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1.
The scour around submarine pipelines may influence their stability; therefore scour prediction is a very important issue in submarine pipeline design. Several investigations have been conducted to develop a relationship between wave-induced scour depth under pipelines and the governing parameters. However, existing formulas do not always yield accurate results due to the complexity of the scour phenomenon. Recently, machine learning approaches such as Artificial Neural Networks (ANNs) have been used to increase the accuracy of the scour depth prediction. Nevertheless, they are not as transparent and easy to use as conventional formulas. In this study, the wave-induced scour was studied in both clear water and live bed conditions using the M5’ model tree as a novel soft computing method. The M5’ model is more transparent and can provide understandable formulas. To develop the models, several dimensionless parameter, such as gap to diameter ratio, Keulegan-Carpenter number and Shields number were used. The results show that the M5’ models increase the accuracy of the scour prediction and that the Shields number is very important in the clear water condition. Overall, the results illustrate that the developed formulas could serve as a valuable tool for the prediction of wave-induced scour depth under both live bed and clear water conditions. 相似文献
2.
This paper presents soft computing approach for estimation of missing wave heights at a particular location on a real-time basis using wave heights at other locations. Six such buoy networks are developed in Eastern Gulf of Mexico using soft computing techniques of Artificial Neural Networks (ANN) and Genetic Programming (GP). Wave heights at five stations are used to estimate wave height at the sixth station. Though ANN is now an established tool in time series analysis, use of GP in the field of time series forecasting/analysis particularly in the area of Ocean Engineering is relatively new and needs to be explored further. Both ANN and GP approach perform well in terms of accuracy of estimation as evident from values of various statistical parameters employed. The GP models work better in case of extreme events. Results of both approaches are also compared with the performance of large-scale continuous wave modeling/forecasting system WAVEWATCH III. The models are also applied on real time basis for 3 months in the year 2007. A software is developed using evolved GP codes (C++) as back end with Visual Basic as the Front End tool for real-time application of wave estimation model. 相似文献
3.
Submarine pipelines are widely used coastal structures, and scour around them can influence their stability. In this study, scour around rigid submarine pipelines under normal-incidence irregular wave attack on horizontal and (1/10) sloping beaches is studied. This paper presents experimental results concerning scour under irregular wave attack. Multiple regression analysis is used to develop models to predict the scour depth under pipelines under the influence of irregular wave attack. The representative wave parameters that characterize the irregular sea state that causes the same scour depth as regular wave attack were determined. 相似文献
4.
Tsunami run-up height is a significant parameter for dimemsions of coastal structures.In the present study,tsunami run-up heights are estimated by three different Artificial Neural Network (ANN) models,i.e.Feed Forward Back Propagation (FFBP),Radial Basis Functions (RBF) and Generalized Regression Neural Network (GRNN).As the input for the ANN configuration,the wave height (H) values are employed.It is shown that the tsunami run-up height values are closely approximated with all of the applied ANN methods.The ANN estimations are slightly superior to those of the empirical equation.It can he seen that the ANN applications are especially significant in the absence of adequate number of laboratory experiments.The restdts also prove that the available experiment data set can he extended with ANN simulations.This may be helpful to decrease the burden of the experimental studies and to supply results for comparisons. 相似文献
5.
1 .IntroductionTheartificialneuralnetwork(ANN)hasbeenwidelyusedinmanyscientificfieldsinrecentyears .Itisakindofinformationmanagementsystemthatresemblesthehumanbraininworkpattern .Comparedwiththetraditionalmethodsofnumericalsimulation ,ANNhastheadvantagesofrelativein dependenceofphysicalmodel,uniformandsimplewayofrealization ,quicknessofcomputing ,andsoon .Sincethemodelofartificialneuronswasfirstlyintroducedin 1 943,ithasbeendevelopedthroughseveralstages.TheapplicationofANNhadnotbeenpopular… 相似文献
6.
Because of the complex geological conditions of the seabed, submarine pipelines buried beneath the ocean floor become suspended over the seabed under the long-term scour of waves eroding the surrounding sediment. Further, most oil fields were built in offshore areas while the country was developing. This gives the waves seen in shallow water obvious nonlinear features, and the abnormal characteristics of these waves must be considered when calculating their hydrodynamic forces. Particularly under such conditions, these suspended spans of submarine pipelines are prone to damage caused by the action of the external environment load. Such damages and eventual failures may result not only in great property losses but also pollution of the marine environment. The span length of these areas is a key predictive factor in pipeline damages. Therefore, determining the allowable span length for these submarine pipelines will allow future projects to avoid or prevent damage from excessive suspended span lengths. Expressions of the hydrodynamic loads placed on suspended spans of pipeline were developed in this work based on the first-order approximate cnoidal wave theory and Morison equation. The formula for the allowable free span length was derived for the common forms of free spanning submarine pipeline based on the point where maximum bending stresses remain less than the material’s allowable stress. Finally, the allowable free span length of real-world pipelines was calculated for a subsea pipeline project in Bohai Bay. This research shows that, with consideration for the complicated marine environment, existing suspended spans are within allowable length limitations. However, continuing to limit the length of these submarine pipeline spans in the Nanpu oil field will require ongoing attention. 相似文献
7.
Precise prediction of extreme wave heights is still an evading problem whether it is done using physics based modeling or by extensively used data driven technique of Artificial Neural Network (ANN). In the present paper, Neuro Wavelet Technique (NWT) is used specifically to explore the possibility of prediction of extreme events for five major hurricanes Katrina 2005, Dean 2007, Gustav 2008, Ike 2008, Irene 2011 at four locations (NDBC wave buoys stations)1 namely; 42040, 42039, 41004, 41041 in the Gulf of Mexico. Neuro Wavelet Technique is employed by combining Discrete Wavelet Transform and Artificial Neural Networks. Discrete wavelet transform analyzes frequency of signal with respect to time at different scales. It decomposes time series into low (approximate) and high (detail) frequency components. The decomposition of approximate components (extreme events in the ocean wave series) can be carried out up to the desired multiple levels in order to provide relatively smooth varying amplitude series. This feature of wavelet transforms make it plausible for predicting extreme events with a better accuracy. In the present study third, fifth and seventh level of decompositions are used which facilitates 3 to 7 times filtering of low frequency events and seems to pay the dividend in the form of better prediction accuracy at extreme events. To develop these Neuro wavelet models to forecast the waves with lead times of 12 hr to 36 hr in advance, previously measured significant wave heights at same locations were used. The results were judged by wave plots, scatter plots and other error measures. From the results it can be concluded that the Neuro Wavelet Technique can be employed to solve the ever eluding problem of accurate forecasting of the extreme events. 相似文献
8.
This paper provides an approach by which the scour depth below pipelines in shoaling conditions beneath non-breaking and breaking random waves can be derived. Here the scour depth formula in shoaling conditions for regular non-breaking and breaking waves with normal incidence to the pipeline presented by Cevik and Yüksel [Cevik, E. and Yüksel, Y., (1999). Scour under submarine pipelines in waves in shoaling conditions. ASCE J. Waterw., Port, Coast. Ocean Eng., 125 (1), 9–19.] combined with the wave height distribution including shoaling and breaking waves presented by Mendez et al. [Mendez, F.J., Losada, I.J. and Medina, R., (2004). Transformation model of wave height distribution on planar beaches. Coast. Eng. 50 (3), 97–115.] are used. Moreover, the approach is based on describing the wave motion as a stationary Gaussian narrow-band random process. An example of calculation is also presented. 相似文献
9.
1 IntroductionSubmarine pipelines are important ocean engi-neeringequipm ents, especiallyfortheoiland gasin-dustries. M anyresearchershavebeendedicatedtotheproblem of local scour around the submarinepipelines, butmostoftheirwork arelaboratory testsand foc… 相似文献
10.
采用2010—2017年南海5个浮标波高观测资料和中国气象局热带气旋最佳路径集中的热带气旋参数, 基于前馈型误差反向传播(Forward Feedback Back Propagation, FFBP)神经网络(Artificial Neural Network, ANN)方法, 分别建立了各浮标站的台风浪高快速计算模型。研究显示, 基于热带气旋中心坐标、中心最低气压、近中心最大风速、热带气旋中心与浮标之间的距离和方位4个参数建立的神经网络模型经反复训练后, 模型输出结果可以很好地拟合观测数据, 各浮标有效波高计算值与观测值的均方根误差小于0.3m, 平均相对误差为5.78%~7.23%, 相关系数大于0.9, 属高度相关。独立测试结果显示, “山竹”( 国际编号: 1822)影响期间有效波高最大值的神经网络模型预报结果与观测值基本吻合, 相对误差为-31.06%~0.98%, 但计算的最大值出现时间和观测情况不完全一致。该计算方法可应用于热带气旋影响期间的有效波高最大值计算, 因而在海洋工程领域和海洋预报领域具有应用前景。 相似文献
11.
Scour around piles could endanger the stability of the structures placed on them. Hence, an accurate estimation of the scour depth around piles is very important in coastal and marine engineering. Due to the complex interaction between the wave, seabed and pile group; prediction of the scour depth is not an easy task and the available empirical formulas have limited accuracy. Recently, soft computing methods such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) have been used for the prediction of the scour depth. However, these methods do not give enough insight about the process and are not as easy to use as the empirical equations. In this study, new formulas are given that are easy to use, accurate and physically sound. Available empirical equations for estimating the pile group scour depth such as those of Sumer et al. (1992) and Bayram and Larson (2000), are less accurate compared to the given equations. These equations are as accurate as other soft computing methods such as ANN and SVM. Moreover, in this study, safety factors are given for different levels of acceptable risks, which can be so useful for engineers. 相似文献
12.
An accurate estimation of scour depth around piles is important for coastal and ocean engineers involved in the design of marine structures. Owing to the complexity of the problem, most conventional approaches are often unable to provide sufficiently accurate results. In this paper, an alternative attempt is made herein to develop adaptive neuro-fuzzy inference system (ANFIS) models for predicting scour depth as well as scour width for a group of piles supporting a pier. The ANFIS model provides the system identification and interpretability of the fuzzy models and the learning capability of neural networks in a single system. Two combinations of input data were used in the analyses to predict scour depth: the first input combination involves dimensional parameters such as wave height, wave period, and water depth, while the second combination contains nondimensional numbers including the Reynolds number, the Keulegan–Carpenter number, the Shields parameter and the sediment number. The test results show that ANFIS performs better than the existing empirical formulae. The ANFIS predicts scour depth better when it is trained with the original (dimensional) rather than the nondimensional data. The depth of scour was predicted more accurately than its width. A sensitivity analysis showed that scour depth is governed mainly by the Keulegan–Carpenter number, and wave height has a greater influence on scour depth than the other independent parameters. 相似文献
13.
ABSTRACTIn this research, group method of data handling (GMDH) as a one of the self-organized approaches is utilized to predict three-dimensional free span expansion rates around pipeline due to waves. The GMDH network is developed using gene-expression programming (GEP) algorithm. In this way, GEP was performed in each neuron of GMDH instead of polynomial quadratic neuron. Effective parameters on the three-dimensional scour rates include sediment size, pipeline geometry, and wave characteristics upstream of pipeline. Four-dimensionless parameters are considered as input variables by means of dimensional analysis technique. Furthermore, scour rates along the pipeline, vertical scour rate, and additionally scour rates in the left and right of pipeline are determined as output parameters. Results of the proposed GMDH-GEP models for the training stages and testing ones are evaluated using various statistical indices. Performances of the GMDH-GEP models are compared with artificial neural network (ANN), GEP, GMDH, and traditional equations-based regression models. Moreover, sensitivity analysis and parametric study are conducted to perceive influences of different input parameters on the three-dimensional scour rates. 相似文献
14.
The paper presents an experimental investigation of seabed evolution behavior around a submarine pipeline and the hydrodynamic forces on the pipeline under regular waves. Unlike the previous flume tests that have largely used beds with median sands, this study focuses on fine sediments such as sandy silt and silt. The primary objective of the study was to investigate: (i) the scour process under different wave conditions and with different sediments and (ii) the influence of the bedform evolution on the hydrodynamic forces experienced by the pipeline. In terms of scour and ripple formation, four distinct regimes of the near-field bed evolution behavior are identified which are: (I) no scour, (II) scour without ripples, (III) scour with small ripples and (IV) scour with large ripples. The influence of bedform evolution on wave forces was found to vary significantly in different regimes. In regime I, the wave forces were quite stable; in regime II and III, the wave forces underwent a gradual reduction before reaching their equilibrium values at fairly early stages of the scour process; in regime IV, the wave forces were significantly affected by the migrating ripples and can be rather unsteady throughout the testing period. 相似文献
15.
由于海床起伏不平,斜坡的存在必然改变波浪对管线及海床的作用特性,进而影响管线三维冲刷。基于波浪港池实验,考虑规则波的作用,采用中值粒径为0.22mm的原型沙铺设与波浪传播方向成45°夹角的斜坡,研究斜向波作用下斜坡上海底管线的三维冲刷特性。通过测量管线下方冲刷坑宽度和深度的差异,分析管线三维冲刷的不均衡性。实验表明:管线的存在使斜坡上的波高有所降低;斜向波作用下管线三维冲刷的不均衡性表现为深度不均衡性和宽度不均衡性,宽度不均衡性主要是管后淤积泥沙的后移引起的,周期对三维冲刷不均衡性的影响比波高对其的影响程度大;管线自深海向近岸延展时,随水深的减小,冲刷深度分为缓慢发展阶段和快速发展阶段。 相似文献
16.
The results of a laboratory experimental program aimed at better understanding the scour around and burial of heavy cylindrical objects under oscillating flow on a sandy bed are described. This study was motivated by its application to the dynamics of isolated cobbles/mines on a sandy floor under nonlinear progressive waves, such as that occur in shallow coastal waters beyond the wave-breaking region. In the experiments, nonlinear progressive waves were generated in a long wave tank of rectangular cross-section with a bottom slope. Model mines (short cylinders) were placed on the sandy bottom and the temporal evolution of the bed profile and the velocity field in the near field of the object were observed. Experiments were conducted at relatively high Reynolds numbers for a range of flow conditions, which can be characterized by the Keulegan–Carpenter number and Shields parameter. Depending on the values of these parameters, four different scour regimes around the cylinder including periodical burial of cylinder under migrating sand ripples were observed; they were classified as: (i) no scour/burial, (ii) initial scour, (iii) expanded scour, and (iv) periodic burial cases. A scour regime diagram was developed and the demarcation criteria between different regimes were deduced. Semi-empirical formulae that permit estimation of the scour depth with time, the equilibrium maximum scour depth and length, and conditions necessary for the burial of the cylinder as a function of main external parameters are also proposed. 相似文献
17.
Iman Malekmohamadi Mohammad Reza Bazargan-Lari Reza Kerachian Mohammad Reza Nikoo Mahsa Fallahnia 《Ocean Engineering》2011,38(2-3):487-497
Wave Height (WH) is one of the most important factors in design and operation of maritime projects. Different methods such as semi-empirical, numerical and soft computing-based approaches have been developed for WH forecasting. The soft computing-based methods have the ability to approximate nonlinear wind–wave and wave–wave interactions without a prior knowledge about them. In the present study, several soft computing-based models, namely Support Vector Machines (SVMs), Bayesian Networks (BNs), Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used for mapping wind data to wave height. The data set used for training and testing the simulation models comprises the WH and wind data gathered by National Data Buoy Center (NDBC) in Lake Superior, USA. Several statistical indices are used to evaluate the efficacy of the aforementioned methods. The results show that the ANN, ANFIS and SVM can provide acceptable predictions for wave heights, while the BNs results are unreliable. 相似文献
18.
An approach by which the scour depth and scour width below a fixed pipeline and scour depth around a circular vertical pile in random waves can be derived is presented. Here, the scour depth formulas by Sumer and Fredsøe [ASCE J. Waterw., Port, Coastal Ocean Eng. 116 (1990) 307] for pipelines and Sumer et al. [ASCE J. Waterw., Port, Coastal Ocean Eng. 114 (1992) 599] for vertical piles as well as the scour width formula by Sumer and Fredsøe [The Mechanics of Scour in the Marine Environment, World Scientific, Singapore, 2002] for pipelines combined with describing the waves as a stationary Gaussian narrow-band random process are used to derive the cumulative distribution functions of the scour depths and width. Comparisons are made between the present approach and random wave scour data. Tentative approaches to related random wave scour cases are also suggested. 相似文献
19.
This paper presents the development of an Artificial Neural Network for the prediction of the wave reflection coefficient from a wide range of coastal and harbor structures. The Artificial Neural Network is trained and validated against an extensive database of about 6000 data, including smooth, rock and armor unit slopes, berm breakwaters, vertical walls, low crested structures, oblique wave attacks. The structure and data included in this database, as well as the approach used in this paper, follow the work done on wave overtopping within the CLASH project.In this new Artificial Neural Network 13 input elements are used to represent the physics of the reflection process taking into account the structure geometry (height, submergence, straight or non-straight slope, with or without berm or toe), the structure type (smooth or covered by an armor layer, with permeable or impermeable core) and the wave attack (water depth, wave height, wave length, wave obliquity, directional spreading).The selection of the input elements and of the algorithms used in the network is described based on an in-depth sensitivity analysis of the network performance.The accuracy of the network is quite satisfactory, being the average root mean squared error lower than 0.04. This value is consistent between the Artificial Neural Network calibrated on the original dataset and the one calibrated on boot-strapped datasets in which data reliability and structure complexity are considered.The performance of the network is compared for limited datasets with selected available literature formulae proving that this approach is able to estimate the experimental reflection coefficients with greater accuracy than the empirical formulae calibrated on these same datasets. 相似文献
20.
《Coastal Engineering》2007,54(9):643-656
This paper aims at improving the prediction of wave transmission behind low-crested breakwaters by means of a numerical model based on Artificial Neural Networks (ANNs). The data here used are those gathered within the European research project DELOS.Firstly, the motivations that lead to employ an ANN numerical model to forecast the wave transmission behind low-crested structures are discussed. Then, the ANN model is tested and its architecture is optimized with a test targeted on assessing both the accuracy and the robustness of the method. A study is devoted to investigate the ANN model capability in reproducing some physical relationships among the involved parameters. Finally, comparisons of ANN results with those from experimental formulations based on the classic regression approach demonstrate a considerable improvement in the forecast accuracy.The ANN forecasting tool is available as a user-friendly Internet applet at: http://w3.uniroma1.it/cmar/wave_transm_kt.htm. 相似文献