Artificial Neural Network for Vertical Displacement Prediction of a Bridge from Strains (Part 2): Optimization of Strain-Measurement Points by a Genetic Algorithm under Dynamic Loading

• Published in: Applied sciences Vol. 10; no. 3; p. 777
• Main Authors: Moon, Hyun Su, Chun, Pang-Jo, Kim, Moon Kyum, Lim, Yun Mook
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2020
• Subjects: Accuracy; artificial neural network; Bridges; genetic algorithm; Genetic algorithms; Global positioning systems; GPS; longitudinal strain; Mutation; Neural networks; Optimization techniques; Sensors; strain-measurement point; vertical displacement
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10030777

Bridge displacements are one of the most important physical values in evaluating the health of bridges. However, the direct measurement of bridge displacements is not easy due to various factors, such as installation location and cost. For that reason, in a previous study (part 1), a method for predicting bridge displacements from strains was proposed using an artificial neural network (ANN), which has a strong ability in data mapping. In this paper, to predict the overall displacements from a small number of strains more efficiently, a method to optimize the number and locations of strain-measurement points was proposed using the genetic algorithm (GA), which is widely used for global optimization. To verify the proposed methods, two cases, a simple beam under sinusoidal loads and a girder bridge under vehicle loads, are carried out through numerical analysis. Also, a laboratory experiment is carried out with a vibrating cantilever beam. The results indicate that the predicted displacements from at least two strains at the optimized locations show good agreements with displacements by numerical analysis and measurements. The results suggest that the proposed method (optimization of strain-measurement points) is very efficient and can be applied in the actual field.