基于外海环境预报的近岸岛礁桥址区波高ANN推算模型
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TU528.41

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国家自然科学基金(51708455)


ANN model of wave height in nearshore island area for sea-crossing bridge based on open ocean environmental forecasting
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    摘要:

    中国跨海桥梁多建于近岸岛礁海域,桥址区的波浪要素随时空演变复杂。桥址区波高的准确推算对于桥梁结构设计和施工组织具有十分重要的意义。提出一种基于外海环境预报数据的近岸岛礁桥址区波高人工神经网络(ANN)推算模型,并以平潭海峡公铁两用大桥桥址海域为研究对象,运用ANN算法中常用的BP神经网络对外海海洋预报台提供的波高、风速数据以及在桥址区实测波高数据进行训练,建立二者之间的映射关系及ANN推算模型。为验证推算模型的可行性和有效性,运用上述模型对桥址区连续80 d的海浪波高进行推算,通过对比前人模型和实测数据发现,推算波高和实测波高的变化趋势基本吻合,均方根误差满足预测要求,获得了理想的预测效果。研究表明,提出的波高ANN推算模型可以利用外海预报信息进行近岸岛礁桥址区的波高推算,且建模过程较为简单。

    Abstract:

    Sea-crossing bridges in China are mainly built in nearshore island area where wave condition varies spatially. The accurate estimation of the wave height in the bridge site is of great significance for bridge design and construction organization. An artificial neural network (ANN) estimation model of wave height in nearshore island area was developed based on open ocean environmental forecasting data. Pingtan Strait sea-crossing bridge site was selected as the research object. The BP neural network commonly used in the ANN was adopted to train the data provided by the open ocean forecasting station and the measured wave height data in the bridge site area. In order to verify the feasibility of the model, the wave height in the bridge site for 80 consecutive days was estimated. By comparing the results of previous model and the measured data, it is found that the trend of the estimation and the measured value is generally consistent. The root mean square error satisfies the prediction requirements and the ideal prediction effect is obtained. The research showed that the proposed ANN estimation model can use the open ocean forecasting information to effectively estimate the wave height of the nearshore island area for sea-crossing bridge with a relatively simple modeling process.

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魏凯,林静,李明阳.基于外海环境预报的近岸岛礁桥址区波高ANN推算模型[J].土木与环境工程学报(中英文),2019,41(6):89-94.

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  • 收稿日期:2019-03-18
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  • 在线发布日期: 2019-12-12
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