A. Choubey, M. D. Goel,
Volume 6, Issue 2 (6-2016)
Abstract
The study aims to investigate the progressive collapse behaviour of RCC building under extreme loading events such as gas explosion in kitchen, terroristic attack, vehicular collisions and accidental overloads. The behavioural changes have been investigated and node displacements are computed when the building is subjected to sudden collapse of the
load bearing elements. Herein, a RCC building designed based on Indian standard code of practice is considered. The investigation is carried out using commercially available software. The node displacement values are found under the column removal conditions and collapse resistance of building frame is studied due to increased loading for different
scenarios. This simple analysis can be used to quickly analyse the structures for different failure conditions and then optimize it for various threat scenarios.
M. Shahrouzi, A. Barzigar, D. Rezazadeh,
Volume 9, Issue 3 (6-2019)
Abstract
Opposition-based learning was first introduced as a solution for machine learning; however, it is being extended to other artificial intelligence and soft computing fields including meta-heuristic optimization. It not only utilizes an estimate of a solution but also enters its counter-part information into the search process. The present work applies such an approach to Colliding Bodies Optimization as a powerful meta-heuristic with several engineering applications. Special combination of static and dynamic opposition-based operators are hybridized with CBO so that its performance is enhanced. The proposed OCBO is validated in a variety of benchmark test functions in addition to structural optimization and optimal clustering. According to the results, the proposed method of opposition-based learning has been quite effective in performance enhancement of parameter-less colliding bodies optimization.
