Abstract
A dam deformation prediction model based on adaptive weighted least squares support vector machines (AWLSSVM) coupled with modified Ant Lion Optimization (ALO) is proposed, which can be utilized to evaluate the operational states of concrete dams. First, the Ant Lion Optimizer, a novel metaheuristic algorithm, is used to determine the punishment factor and kernel width in the least squares support vector machine (LSSVM) model, which simulates the hunting process of antlions in nature. Second, aiming to solve the premature convergence phenomenon, Levy flight is introduced into the ALO to improve the global optimization ability. Third, according to the statistical characteristics of the datum error, an improved normal distribution weighting rule is applied to update the weighted value of data samples based on the learning result of the LSSVM model. Moreover, taking a concrete arch dam in China as an example, the horizontal displacement recorded by a pendulum is used as a study object. The accuracy and validity of the proposed model are verified and evaluated based on the four evaluating criteria, and the results of the proposed model are compared with those of well-established models. The simulation results demonstrate that the proposed model outperforms other models and effectively overcomes the influence of outliers on the performance of the model. It also has high prediction accuracy, produces excellent generalization performance, and can be a promising alternative technique for the analysis and prediction of dam deformation and other fields, including flood interval prediction, the stock price market, and wind speed forecasting.
Highlights
A dam is a significant infrastructure project that integrates flood control, hydroelectric power, irrigation, navigation, and water supply, which can facilitate the development of the social economy for a nation
Due to the interference of many uncertain factors, such as the monitoring instruments, external environment, and human factors, the noise pollution is inevitable in prototype monitoring data, which leads to a reduction in the validity of the dam deformation series, introduces difficultly in reflecting the real deformation status of the dam, and reduces the accuracy of the data analysis results [3]. erefore, due to the nonlinearity and irregularity of the monitoring time series, an effective and reasonable prediction technique urgently needs to be introduced to ensure the safe operation of a dam based on prototype monitoring data
We present a novel model based on adaptive weighted least squares support vector machines (AWLSSVM) for dam deformation predictions. e proposed model inherits the advantage of the LSSVM fast learning and adaptively assigns different weight values to each training sample by using the improved normal distribution weighting rules to eliminate the influence with respect to outliers on the monitoring data and improve the anti-interference ability of the model [22]
Summary
A dam is a significant infrastructure project that integrates flood control, hydroelectric power, irrigation, navigation, and water supply, which can facilitate the development of the social economy for a nation. E analysis of the measured data and the construction of the dam deformation prediction model are determinants in structural health monitoring [3,4,5]. The time series of dam deformation has high nonlinearity and outliers, which further limits the predictive power of the models. Because the dam prototype monitoring data are affected by outliers, the accuracy and reliability of the dam deformation prediction model are directly affected. We present a novel model based on adaptive weighted least squares support vector machines (AWLSSVM) for dam deformation predictions. A hybrid dam deformation prediction technique based on the AWLSSVM model coupled with the MALO algorithm is constructed.
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