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首页> 外文期刊>Automation in construction >Hybrid computational model for predicting bridge scour depth near piers and abutments
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Hybrid computational model for predicting bridge scour depth near piers and abutments

机译：预测桥墩附近桥冲深度的混合计算模型

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摘要

Efficient bridge design and maintenance requires a clear understanding of channel bottom scouring near piers and abutment foundations. Bridge scour, a dynamic phenomenon that varies according to numerous factors (e.g., water depth, flow angle and strength, pier and abutment shape and width, material properties of the sediment), is a major cause of bridge failure and is critical to the total construction and maintenance costs of bridge building. Accurately estimating the equilibrium depths of local scouring near piers and abutments is vital for bridge design and management Therefore, an efficient technique that can be used to enhance the estimation capability, safety, and cost reduction when designing and managing bridge projects is required. This study investigated the potential use of genetic algorithm (GA)-based support vector regression (SVR) model to predict bridge scour depth near piers and abutments. An SVR model developed by using MATLAB® was optimized using a GA, maximizing generalization performance. Data collected from the literature were used to evaluate the bridge scour depth prediction accuracy of the hybrid model. To demonstrate the capability of the computational model, the GA-SVR modeling results were compared with those obtained using numeric predictive models (i.e., classification and regression tree, chi-squared automatic interaction detector, multiple regression, artificial neural network, and ensemble models) and empirical methods. The proposed hybrid model achieved error rates that were 81.3% to 96.4% more accurate than those obtained using other methods. The GA-SVR model effectively outperformed existing methods and can be used by civil engineers to efficiently design safer and more cost-effective bridge substructures.
机译：高效的桥梁设计和维护需要对墩墩基台附近的通道底部冲刷有清晰的了解。桥梁冲刷是一种动态现象，会根据许多因素（例如，水深，流角和强度，桥墩和桥台的形状和宽度，沉积物的材料特性）而变化，是导致桥梁破坏的主要原因，并且对整个桥梁至关重要桥梁建设的施工和维护费用。准确估算桥墩和桥墩附近局部冲刷的平衡深度对于桥梁设计和管理至关重要。因此，在设计和管理桥梁项目时，需要一种有效的技术来提高估算能力，安全性和降低成本。这项研究调查了潜在的使用基于遗传算法（GA）的支持向量回归（SVR）模型来预测桥墩和桥台附近的冲刷深度。使用GA优化了使用MATLAB®开发的SVR模型，从而最大化了泛化性能。从文献中收集的数据用于评估混合模型的桥梁冲刷深度预测准确性。为了证明计算模型的功能，将GA-SVR建模结果与使用数字预测模型（即分类和回归树，卡方自动交互检测器，多元回归，人工神经网络和集成模型）获得的结果进行比较。和经验方法。提出的混合模型实现的错误率比使用其他方法获得的错误率高81.3％至96.4％。 GA-SVR模型有效地超越了现有方法，并且可以被土木工程师用来有效地设计更安全，更具成本效益的桥梁子结构。

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