A review of recent research on mechanics of multifunctional composite materials and structures

Structural damage detection is still a challenging problem owing to the difficulty of extracting damage-sensitive and noise-robust features from structure response. This article presents a novel damage detection approach to automatically extract features from low-level sensor data through deep learning. A deep convolutional neural network is designed to learn features and identify damage locations, leading to an excellent localization accuracy on both noise-free and noisy data set, in contrast to another detector using wavelet packet component energy as the input feature. Visualization of the features learned by hidden layers in the network is implemented to get a physical insight into how the network works. It is found the learned features evolve with the depth from rough filters to the concept of vibration mode, implying the good performance results from its ability to learn essential characteristics behind the data.

Structural Damage Detection with Automatic Feature-Extraction through Deep Learning

http://mdog.mcgill.ca/paper%20pdf%20to%20upload/Optimum%20stacking%20sequence%20design%20part1.pdf

Optimum stacking sequence design of composite materials Part I: Constant stiffness design

Research developments in vibration control of structures using passive tuned mass dampers

Multifunctional Material Systems: A state-of-the-art review

Damage detection in bridges using accurate modal parameters

An arrangement of tuned mass dampers, termed coupled tuned mass dampers (CTMDs) has been introduced, where a mass is connected by translational springs and viscous dampers in an eccentric manner. The CTMD has coupled modes of lateral and rotational vibration, which have been utilized to control coupled lateral and torsional vibrations of asymmetric buildings. An efficient control strategy has been presented in this context to control displacements as well as acceleration responses of asymmetric buildings having asymmetry in both plan and elevation. The building is idealized as a simplified three-dimensional model with two translational and a rotational degrees of freedom for each floor. The principles of rigid body transformation have been incorporated to account for eccentricity between center of mass and center of rigidity. A nondominated sorting genetic algorithm (GA) has been used for solving the multi-objective optimization problem. Tuning of optimum locations of TMDs and their parameters have been done from the multiple pareto-optimal solutions obtained simultaneously. Comparative studies of performance of CTMD with conventional TMDs and bi-directional TMDs have been presented. It has been observed that CTMDs are more effective and robust in controlling coupled lateral and torsional vibrations of asymmetric buildings. The optimal locations have been observed to be reasonably compact and practically implementable for CTMDs. Copyright © 2005 John Wiley & Sons, Ltd.

Coupled tuned mass dampers for control of coupled vibrations in asymmetric buildings

Optimization of FRP composites against impact induced failure using island model parallel genetic algorithm

Applications of tuned mass damper (TMD) systems for bridge structures are observed for mitigation of problem related to excessive vibration induced by either wind loading or vehicle loading, where ...

Anjan Dutta

Papers

Damage detection in bridges using accurate modal parameters

Coupled tuned mass dampers for control of coupled vibrations in asymmetric buildings

Optimization of FRP composites against impact induced failure using island model parallel genetic algorithm

Multi-modal vibration control of truss bridges with tuned mass dampers under general loading:

Multi-objective optimization of hybrid laminates subjected to transverse impact