An Approach for Damage Identification and Optimal Sensor Placement in Structural Health Monitoring by Genetic Algorithm Technique

Abstract

Civil engineering structures are constructed for strength, serviceability and durability. The structures thus constructed involve huge investment and labour work. In order to protect the structure from various damages, periodic monitoring of structures is necessary. Hence Structural Health Monitoring (SHM) plays a vital role in diagnosing the state of the structure at every moment during its life period. For this purpose, sensors are deployed in the structures for its efficient health monitoring. Sensors cannot be deployed at random locations of the structure. They have to be located at those points which reflect the damage. In this study, a 3-storey and a 4-storey building are taken and Modal Strain Energy (MSE) is used for finding the initial locations of sensors. The number of sensors obtained is then optimized using Genetic Algorithm (GA) technique. Finally damages are induced in certain locations of the structure and a damage detection technique called as "Flexibility Matrix Based Technique (FMBT)" is introduced for damage localization in the structure.

Analysis of Dynamic Parameters of a Railway Bridge

“…A random function compiled on the basis of arrays of measurement data of vibration parameters ϕ is considered stationary (in the broad sense), i.e., with the average of M ϕ(t) → const, while the covariance function depends solely on the difference between arguments τ − K ϕ (τ) Discrete Fourier transformation [16][17][18][19][28][29][30][31][32] may be used to process digital signals. Auto-covariance function of one data array or inter-covariance function of two arrays K ϕ (τ) is expressed as follows [78]:…”

“…The purpose of the information received is to identify structural defects or to provide information on structural changes (usually using modal analysis). Artificial Neural Networks (ANNs), Pattern Search (PS) and Evolutionary Strategies (ES), such as the Genetic Algorithms (GA) [5], the Particle Swarm Optimization (PSO) [6], and the Covariance-Matrix Adaptation Evolution Strategy (CMA-ES) are some of the countless examples available in the literature [7,[16][17][18]. This article focuses on analysing hard-to-reach significant points (because the examined object is an old-structure bridge that is not adapted for dynamic research) by performing dynamic load tests (with a train moving at different speeds).…”

Sensor Placement Strategies for the Seismic Monitoring of Complex Vaulted Structures of the Modern Architectural Heritage

“…Recently some optimisation methods based on analogies with biology and physics have been introduced. Artificial Neural Networks (ANNs), Pattern Search (PS), and Evolutionary Strategies (ES), such as the Genetic Algorithms (GA) [5], the Particle Swarm Optimization (PSO) [6], and the Covariance-Matrix Adaptation Evolution Strategy (CMA-ES), are only some of the countless examples present in the literature [7].…”