Seismic damage assessment of highway bridges by means of soft computing techniques

Abstract

Seismic risk assessment in bridge networks is of great interest in reducing earthquake adverse effects in structures and the society. Among existing methods for risk and loss assessment, Hazus methodology is widely accepted and used. The method includes several steps and calculations, being thus rather complex and time-consuming, especially if several bridges with diverse characteristics are to be examined. The present study investigates the potential of using soft computing techniques for determining the seismic hazard and expected losses as an alternative to the Hazus methodology. In particular, a number of representative datasets have been developed on the basis of Hazus and a number of methods have been formulated and tested regarding their capacity to effectively simulate the process. The methods that are examined range from regression curve-fitting to artificial neural networks of different size and characteristics. Alternative statistical tests have been used to evaluate the developed model capabilities. Evaluation results indicate that appropriately designed ANN methods can attain high accuracy and stable results among different datasets (training, testing, and validation). The analysis indicates the potential of using similar techniques to simulate seismic risk assessment based on actual data from earthquake events that have taken place in the past.

Keywords:

• Seismic risk management
• Loss estimation
• Bridge damage
• Soft computing
• Artificial Neural Networks
• Genetic Algorithms

Disclosure statement

No potential conflict of interest was reported by the authors.