ANALYSIS OF GENETIC ALGORITHM FOR MULTIPLE CRACK DETECTION
7.3 Results and discussion
The analyses of the results obtained from genetic algorithm model have been expressed in the current section. It is observed that the presence of cracks have noticeable effects on the vibration characteristics of a structural member and the vibration parameters can be used to predict the crack locations and their severities in cracked structures. Numerical, finite element and experimental analyses have been performed on the cantilever beam with different boundary conditions to extract the vibration signatures, which are later used for designing the GA system. A flow chart representing the various steps followed to design the GA model has been shown in Fig. 7.3. Experimental analysis has been carried out to validate the simulated results from the proposed crack diagnostic methodology. The use of single point crossover operator has been shown in Fig. 7.1 to find the optimal solution. In some cases the mutation operation (Fig. 7.2) has been presented to find the best fit child with in the search space for solution. Table 7.1 represents some of the examples of initial data pool used for the designing of the GA based model. The results for relative crack depths and relative crack locations from GA model, neural network, fuzzy Gaussian model and experimental analysis are shown in Table 7.2 (a) and the results from GA model have been proved to be the best to other AI techniques mentioned in the Table 7.2 (a). A comparison of results from GA model, finite element, numerical is presented in Table 7.2 (b) and the outcomes are found to be in agreement. The percentage of deviation of the predicted results from the GA model has been found as 4.33%. The graph for estimation error vs number of generations for the GA model has been shown in Fig. A5 of the Appendix section.
7.4. Summary
The following conclusions can be made by analyzing the results obtained from the GA model for multiple crack diagnosis in cantilever beam structure. This section presents a technique for automatic detection of crack locations and their severities of structural members using GA based model. Analysis of vibration parameters i.e. (natural frequencies, mode shapes) of the cracked structure have been done through numerical, finite element and experimental analysis and the extracted vibration signatures are used to create the initial data pool of the GA system, for multiple crack identification. Single point cross over and mutation procedure have been followed to find out the best possible solution with in the search space. The first
three relative natural frequencies and first three average relative mode shape differences are used as inputs to the GA crack identification method. Relative crack depths and relative crack locations are the output parameters from the proposed GA based technique. A close agreement between the results from simulation, experimental and GA model shows the effectiveness of the developed methodology for multiple crack diagnosis. The developed GA model can be used for automated condition monitoring of structural systems.
Publication:
• D.R.K.Parhi, Amiya Kumar Dash, H.C. Das Formulation of a GA based methodology for multiple crack detection in a beam structure, Australian journal of structural engineering, Vol. 12 (2), pp. 59-71, 2011.
Integration of Neural networks (NN) and Fuzzy logic (FL) have brought researchers from various scientific and engineering domains for the need of developing adaptive intelligent systems to address real time applications. NN learns by adjusting the synaptic weights of neurons between layers. FL is a potential computing model based on the concept of fuzzy set, fuzzy rules, and fuzzy reasoning. It is known that fuzzy logic and NN have the ability to perceive the working environment and mimic the human behavior, thus the advantages of combining neural network and fuzzy logic are immense. There are different procedures to integrate NN and FL and mostly it depends on the types of application. The integration of NN and FL can be classified broadly into three categories namely concurrent model, cooperative model and fully fused model. In the current chapter fuzzy logic and neural network have been adopted to form a multiple crack identification tool for structural health monitoring.
8.1 Introduction
Fuzzy-Neuro hybrid computing technique is a potential tool for solving problems with complexity. If the parameters representing a system can be expressed in terms of linguistic rules, a fuzzy inference system can be build up. A neural network can be built, if data required for training from simulations are available. From the analysis of NN and FL it is observed that drawbacks of the two methods are complementary and therefore it is desirable to build an integrated system combining the two techniques. The learning capability is an advantage for NN, while the formation of linguistic rule base is an advantage for fuzzy logic. Hence, the hybrid fuzzy-neuro technique can be used for identifying cracks present in a structural system using vibration data.
In this chapter, a novel identification algorithm (hybrid intelligent system) using inverse analysis of the vibration response of a cracked cantilever beam has been proposed. The crack identification algorithm utilizes the vibration signatures of the cracked beam derived from finite element and theoretical analysis. The hybrid model is designed to predict the crack