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

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.

Finite Element Analysis (FEA) for the Beam-Column Joint Subjected to Cyclic Loading Was Performed Using ANSYS

This paper analyses the seismic performance of exterior beam-column joints strengthened with unconventional reinforcement detailing. The beam-column joint specimens were tested with reverse cyclic loading applied at the beam end. The samples were divided into two groups based on the joint reinforcement detailing. The first group (Group A) of three non-ductility specimens had joint detailing in accordance with the construction code of practice in India IS456-2000, and the second group (Group B) of three ductility specimens had joint reinforcement detailed as per IS13920-1993, with similar axial load cases as the first group. The experimental studies are proven with the analytical studies carried out by finite element models using ANSYS. The results show that the hysteresis simulation is satisfactory for both un-strengthened and ferrocement strengthened specimens. Furthermore, when ferrocement strengthening is employed, the strengthened beam-column joints exhibit better structural performance than the un-strengthened specimens of about 31.56% and 38.98 for DD-T1 and DD-T2 respectively. The analytical shear strength predictions were in line with the test results reported in the literature, thus adding confidence to the validity of the proposed models.

ART Based Reliable Method for Prediction of Agricultural Land Changes Using Remote Sensing

This paper focuses on prediction of change in agricultural lands by using ART2 algorithm. The existing method used ENVI and ARCGIS software to predict the changes in land, which showed less accuracy due to human errors. To overcome this user friendly GUI based ART2 algorithm has been developed in java to obtain more accuracy in prediction of changes in land. The input is satellite temporal images of the years 1990 and 2014. By using the ART2 algorithm, the input images of the years 1990 and 2014 are classified, where the features are identified to form cluster. The clustered image is given as input and pixel to pixel comparison method in ART2 is implemented in java, for detecting the changes in agricultural lands. The comparison results of ENVI and ARCGIS and GUI based ART2 with in situ data show that the prediction of changes in agricultural land is more accurate in the case of GUI based ART2 implementation.