Structural deformation prediction model based on extreme learning machine algorithm and particle swarm optimization. (November 2022)

• Record Type: Journal Article
• Authors: Jiang, Shouyan; Zhao, Linxin; Du, Chengbin
• Is Part Of: Structural health monitoring. Volume 21:Number 6(2022)
• Page Start: 2786
• Page End: 2803
• Publication Date: 2022-11
• Subjects: Deformation monitoring -- extreme learning machine -- particle swarm optimization -- prediction model -- aqueduct; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/14759217211072237
• Languages: English
• ISSNs: 1475-9217

Abstract

In this paper, an extreme learning machine (ELM) algorithm based on particle swarm optimization (PSO) is proposed to predict structural deformation. Taking an aqueduct located in Tiantai County, Zhejiang, China, as a case study, a series of observations of the aqueduct vertical displacements and crack openings were used to train a neural network. Then, variables representing environmental factors (air temperature), hydraulic factors (water level), and aging were selected as the influence factors input into the prediction model. Finally, the proposed PSO–ELM model was used to predict the vertical deformation and crack opening of the aqueduct, and the predicted results were compared with the monitored values using four evaluation indexes: mean absolute error ( MAE ), mean squared error ( MSE ), maximum absolute error ( S ), and correlation coefficient ( R ). The prediction results obtained using the PSO–ELM model were then compared with those obtained using the evolutionary ELM, conventional ELM, back propagation neural network, long short-term memory, and multiple linear regression models. The results indicate that the proposed PSO–ELM model has an evidently superior predictive ability, with higher values of R and lower values of MAE, MSE, and S . The proposed model can therefore be confidently used to serve as a tool similar to a "weather forecast" function to predict the vertical deformation and crack openings of an aqueduct and may be employed for other structural monitoring applications as well.