Optimal Sensor Placement for High-Rise Building via Genetic Algorithms and Improved Information Matrix Criterion

Abstract

To improve the accuracy of parameter identification and to reduce the test cost in the high-rise structure health monitoring systems , the paper proposes an optimal placement method combines genetic algorithms with improved information matrix criterion (GA-IIM). With traditional improved effective independent method (EFI-DPR), it is easily trapped into bureau of optimum solution, and the proposed method can resolve this problem. The solutions were coded by a two-dimensional integer array, each line stored a feasible solution and compulsory mutation was adopted to avoid the solution reappeared in the same position. To improve the convergence speed, each generation of optimal group was kept to the next one to participate in the competition. With the improved information matrix criterion embedded in the genetic algorithms, the placement design was produced. This technique was used to optimize the sensor placement of a nineteenth layer frame structure. Comparing the optimized sensor placement of the proposed method with that of improved effective independent method by the modal assurance criterion and the mean square error, the proposed method performs better.

Computational methodologies for optimal sensor placement in structural health monitoring: A review

“…GA is one of the commonly used heuristics methodologies to solve NP-hard problems. Both original GA 26–28,38,51,55,57,77,85–93 and improved GA 33,37,42,94–104 had been used in OSP problems.…”

“…Huang et al 26 proposed a point array coding method that included both sensor locations and sensor type information. Zhan et al 103 presented a 2D integer array coding method. Zhu et al 104 used quantum probability vector to encode the solution, where the quantum information was encoded using pairs of complicated numbers.…”

“…Zhang et al 42 adopted a proportional selection model to guarantee the quality of the population, and the partially matched crossover was implemented to improve the evolution performance. In addition, Zhan et al 103 embedded an improved information matrix into GA for sensor placement design. Zhu et al 104 adopted the quantum theory to improve GA performance, where chromosomes are represented with quantum probability vectors, and quantum-rotating doors were used to update and optimize the population by globally searching for an optimal solution.…”