Shashi Kant Thakkar

Strong motion records

INTRODUCTION The Department of Earthquake Engineering, Indian Institute of Technology Roorkee, Roorkee, India operates a network of 17 structural response recorders (SRR) in the Kachchh region with the help of financial support from the Department of Science and Technology (DST), New Delhi, India. The main event was recorded at 13 stations (Figure 5-1). A 10-story residential building within the complex of the Regional Passport Office at Ahmedabad was instrumented under a separate DST-sponsored program on instrumentation of multistory buildings. The motion recorded at the ground floor of this building is the only ground motion acceleration record available for this event.

Identification of modal parameters of a multistoried RC building using ambient vibration and strong vibration records of Bhuj earthquake, 2001

Modal parameters of an instrumented multi-storied reinforced concrete building (G +9) have been studied using strong motion records of Bhuj Earthquake, 2001. The Ambient Vibration Testing (AVT) is also conducted to measure the modal parameters of the same building under ambient environmental forces. Frequency Domain Decomposition (FDD) or Peak Picking (PP) in frequency domain and Stochastic Subspace Identification (SSI) technique in time domain is used for extracting the modal parameters. The observed natural frequencies during strong motion are smaller than the ambient vibration testing. The difference in the frequencies may be caused by interaction between structure and soil due to high level of strain during strong motion earthquake. The modal pattern of first five modes obtained from strong motion records and ambient vibration records are identical.

A COMPARATIVE STUDY OF STRENGTHENING AND RETROFITTING MEASURES FOR UNREINFORCED BRICK MASONRY MODEL UNDER CYCLIC TESTING

An experimental study on half-scale brick-masonry models with different strengthening and retrofitting measures has been studied under cyclic loading in a quasi-static test facility. The strengthening measures undertaken for the studies axe the horizontal bond beam at the lintel and sill level with a combination of vertical reinforcement at corners and openings. The retrofitting measures studied are grouting with epoxy-sand-mortar and cement-grout-injection with welded wire mesh in the cracked region. The tests reveal that the horizontal bond beam at lintel level with vertical reinforcement is effective in reducing the cracking above the lintel level. The insertion of an additional sill-band signi-ficantly reduces the cracking in walls. The epoxy-sand-mortar techniques for retrofitting of cracked regions prove to be effective enough to restore the initial strength, stiffness and deformation capacity. Although specimen retrofitted with cement-grout-injection with welded wire mesh is effective to regain the ultimate strength yet the brittle failure is observed as the specimen is stressed beyond the elastic limit.

Neural Network-Based Damage Detection from Transfer Function Changes

This article puts forth the work on a neural network-based approach to determine the degree of damaged floors of the building considering changes in the transfer function. The transfer function is considered for that part of forced vibration in which system vibrates linearly after the structure has been damaged considering the building is instrumented during the earthquake. The results showed that accuracy of degree of damage detected increased with the increase in the number of combination of damages. The instrumentation of the first floor is expected to give best results for damage detection based on the transfer function-based approach.

Comparison of eigensensitivity and ANN based methods in model updating of an eight-story building

Analytical models prepared from field drawings do not generally provide results that match with experimental results. The error may be due to uncertainties in the property of materials, size of members and errors in the modelling process. It is important to improve analytical models using experimentally obtained data. For the past several years, data obtained from ambient vibration testing have been successfully used in many cases to update and match dynamic behaviors of analytical models with real structures. This paper presents a comparison between artificial neural network (ANN) and eigensensitivity based model updating of an existing multi-story building. A simple spring-mass analytical model, developed from the structural drawings of the building, is considered and the corresponding spring stiffness and lumped mass of all floors are chosen as updating parameters. The advantages and disadvantages of these updating methods are discussed. The advantage is that both methods ensure a physically meaningful model which can be further employed in determining structural response and health monitoring.