Structural health monitoring, damage identification method and sensor placement optimization for carbon fiber reinforced polymer (CFRP) composite beam were studied in this thesis. In this work, different methodologies were investigated for the damage detection process to enhance the use of current structural health monitoring systems by identifying the optimal sensor placement. Carbon fiber reinforced polymer composite materials were fabricated and the fabrication process based on vacuum assisted resin transfer molding (VARTM) is briefly introduced. Numerical analysis using finite element method was subsequently performed for a composite beam based on the material properties determined by performing experimental material characterization tests. Three benchmarking tests with different types of elements were performed to verify the best method for modeling the composite panel. Moreover, shear lag analysis was also presented to model an embedded crack in the composite panel which would be used in damage detection and optimization process. Based on the finite element analysis and static strain data extracted, a comparative study on two damage detection algorithms based on artificial neural network (ANN) and support vector machine (SVM) is presented. The viability of these two methods was demonstrated by analysis of the numerical model of composite beam with a crack embedded in it and the performance for each algorithm is also presented with different number of sensors and different noise levels. Two experiments are presented for the performance evaluation of damage detection and identification. To identify the optimal locations of sensors in the optimization process, a statistical probability based method using the combination of artificial neural networks and evolutionary strategy were developed to increase the detection rate of damage in a structure. The proposed method was able to efficiently increase the detection accuracy compared with a uniform distribution of sensors for a composite beam that was damaged in different locations. The finite element model of the composite coupon was used as a representation of the real structure. Static strain data from finite element simulation was extracted with different damage scenarios and used as feature vector for the classification process. Based on the performance of the classification for a given sensor configuration, updated sensor locations would be selected by changing the coordinates of these sensor locations using strategy parameters. The viability of this method was demonstrated by conducting different examples and significant number of simulations was performed to check the repeatability of the algorithm.