Implicit Redundant Representation Research Articles

Architectural design of apartment buildings using the Implicit Redundant Representation Genetic Algorithm

Abstract In the architectural design process, the conceptual design stage is to devise a creative alternative in response to the intent of the architects. In this paper, we propose an alternative evolutionary-based architectural design method by using the Implicit Redundant Representation Genetic Algorithm (IRRGA) that is highly suited to explore unstructured problem formulation such as conceptual design. Also, a new string representation for apartment building designs is proposed such that the size and the number of apartment units with stairs are not fixed and can be changed during the design evolution. The design objectives are selectively applied in terms of symmetry, structure, circulation, and facade. Each objective is used respectively as a fitness function to demonstrate the performance of IRRGA. Finally, a multi-objective fitness function is applied and the resulting apartment building designs show their own level of creativity.

Multi-objective optimization for actuator and sensor layouts of actively controlled 3D buildings

This paper investigates the multi-objective optimization of active control systems for vibration control of three-dimensional (3D) high-rise buildings under a variety of earthquake excitations. To this end, a novel multi-objective genetic algorithm is developed through the integration of the best features of a non-dominated sorting II (NS2) genetic algorithm (GA) and an implicit redundant representation (IRR) GA. The proposed NS2-IRR GA finds not only minimum distributions of both actuators and sensors within structures, but also minimum dynamic responses of 3D structures. Linear quadratic Gaussian controllers, hydraulic actuators and accelerometers are used for implementation of active control systems within the 3D buildings. To demonstrate the effectiveness of the proposed NS2-IRR GA, two 3D building models are investigated using finite element methods, including low- and high-rise buildings. It is shown that the proposed NS2-IRR GA is effective in finding not only optimal locations and numbers of both actuators and sensors in 3D buildings, but also minimum responses of the 3D buildings. The simulation also shows that the control performances of the proposed approach significantly enhance those of the engineering judgment oriented benchmark layout, which is validated by comparisons of each performance using the same number of actuators.

Multi-objective genetic algorithms for cost-effective distributions of actuators and sensors in large structures

This paper proposes a multi-objective genetic algorithm (MOGA) for optimal placements of control devices and sensors in seismically excited civil structures through the integration of an implicit redundant representation genetic algorithm with a strength Pareto evolutionary algorithm 2. Not only are the total number and locations of control devices and sensors optimized, but dynamic responses of structures are also minimized as objective functions in the multi-objective formulation, i.e., both cost and seismic response control performance are simultaneously considered in structural control system design. The linear quadratic Gaussian control algorithm, hydraulic actuators and accelerometers are used for synthesis of active structural control systems on large civil structures. Three and twenty-story benchmark building structures are considered to demonstrate the performance of the proposed MOGA. It is shown that the proposed algorithm is effective in developing optimal Pareto front curves for optimal placement of actuators and sensors in seismically excited large buildings such that the performance on dynamic responses is also satisfied.

Decentralized output feedback polynomial control of seismically excited structures using genetic algorithm

This paper presents novel decentralized output feedback control strategy for the active and semi-active controls of the highway bridge benchmark phases I and II problems subjected to earthquake ground motions. The control force is calculated using two third-order polynomial equations expressing a direct relationship between displacement and velocity of the control device and the control force. An advanced implicit redundant representation genetic algorithm is utilized to determine optimal coefficients of the two polynomial equations by minimizing the sum of three evaluation criteria for six prescribed earthquakes. The performance of the proposed controller is evaluated in terms of a set of prescribed 21 evaluation criteria and is compared with the other several control strategies published in the special issue of the benchmark problem. The results show that the proposed decentralized output feedback polynomial control strategy can achieve significant response reductions in the bridge system, and it is evidently superior to sample control strategies and other suggested active and semi-active controls for the phase I problem and is quite competitive with respect to the sample and other semi-active control approaches for the fully isolated phase II problem. Copyright © 2011 John Wiley & Sons, Ltd.

Improving the Performance of Structural Damage Detection Methods Using Advanced Genetic Algorithms

A frequency response function-based damage identification method is presented that accurately identifies both the location and severity of damage in structural systems using a limited amount of measurement information. Damage is identified by minimizing the error between measured and analytically computed frequency response functions obtained through finite element model updating. The impact that the type of genetic algorithm representation has on performance is evaluated for a fixed representation and an implicit redundant representation, which simplifies the search by exploiting the unstructured nature of damage identification. The performance of the proposed damage identification method is evaluated for beam and frame structures that consider different damage scenarios and measurement layouts. The impact of measurement noise on performance is also investigated. The damage identification method developed using the implicit redundant genetic algorithm provides greater accuracy in identifying the location and severity of damage in all case studies even in the presence of noise. For larger frame structures, the implicit redundant genetic algorithm performed well, while no valid results were obtained using the fixed genetic algorithm representation.

Evolving structural design solutions using an implicit redundant Genetic Algorithm

Performing synthesis during conceptual design provides substantial cost savings by selecting an efficient design topology and geometry, in addition to selecting the structural member properties. A new evolutionary-based representation, which combines redundancy and implicit fitness constraints, is introduced to represent and search for design solutions in an unstructured, multi-objective structural frame problem. The implicit redundant representation genetic algorithm, in tandem with the unstructured problem domain definition, allows the evaluation of diverse frame topologies and geometries. The IRR GA allows the representation of a variable number of location independent parameters, which overcomes the fixed parameter limitations of standard GAs. The novel frame designs evolved by the IRR GA synthesis design method compare favourably with traditional frame design solutions calculated by trial and error.