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Preface; Contents; Chapter 1: Development and Characterization of an ITO Nanocomposite Film Sensor for Damage Detection; 1.1 Introduction; 1.2 Surface Damage Detecting Film Nanocomposite; 1.3 Experimental Setup; 1.4 Results and Discussions; 1.4.1 Surface Damage Detection; 1.4.2 Influence of the Temperature; 1.4.3 Influence of the Humidity; 1.5 Conclusion; References; Chapter 2: Fiber Optic Sensor Arrays for Real-Time Virtual Instrumentation and Control of Flexible Structures; 2.1 Introduction; 2.2 Approach; 2.3 Finite Element Analysis; 2.4 Model Formulation
2.5 FBG-IMU System Development (First Approach)2.6 Second Approach of FBG-IMU; 2.7 Results; 2.8 Conclusions and Future Work; References; Chapter 3: On the Output-Only Vibration-Based Damage Detection of Frame Structures; 3.1 Introduction; 3.2 Output-Only Vibration-Based Damage Detection Methods; 3.3 Application: Identification and Damage Detection; 3.3.1 Test Structure: Undamaged Condition; 3.3.2 Test Structure: Damaged Condition; 3.3.3 Simulation of the Structural Response; 3.4 Results and Discussion; 3.4.1 Output-Only Modal Identification; 3.4.2 Damage Detection; 3.5 Conclusions; References
Chapter 4: On the Influence of Sample Length and Measurement Noise on the Stochastic Subspace Damage Detection Technique4.1 Introduction; 4.2 Stochastic Subspace Damage Detection Technique; 4.2.1 Dynamic Equilibrium Equation in Discrete Time Domain; 4.2.2 Output-Only Covariance Based Subspace System Identification; 4.2.3 Residual Vector Formation; 4.2.4 Hypothesis Test; 4.2.4.1 Parametric Chi-Square Test; 4.2.4.2 Non-parametric Chi-Square Test; 4.3 Investigating the Effect of Noise and Number of Samples; 4.3.1 Effect of Number of Samples; 4.3.1.1 Effect on the Residual Covariance
4.3.1.2 Effect on the chi2 Test Value4.3.2 Effect of Measurement Noise; 4.3.2.1 Equal Noise Properties Between the Reference State and Possibly Damaged State; 4.3.2.2 Different Noise Properties Between the Reference State and Possibly Damaged State; 4.4 Numerical Application; 4.4.1 Cases Study 1, Effect of Number of Samples; 4.4.2 Case Study 2, Effect of Noise with Equal Properties; 4.4.3 Case Study 3, Effect of Noise with Unequal Properties; 4.5 Discussion and Conclusion; References; Chapter 5: Quantification of Structural Damage with Self-Organizing Maps; 5.1 Introduction
5.2 Qatar University Grandstand Simulator5.3 Verification of the Finite Element Model; 5.4 The SOM-Based Damage Detection Algorithm; 5.4.1 Training of the SOMs; 5.4.2 Structural Damage Assessment; 5.5 Numerical Demonstration of the Damage Detection Algorithm; 5.6 Discussion; 5.7 Conclusion; References; Chapter 6: Accuracy Enhancement of a Device for Automated Underbridge Inspections; 6.1 Introduction; 6.2 Method; 6.3 Results; 6.4 Concluding Remarks; References; Chapter 7: A Brief Overview of Mechatronics; 7.1 Introduction; 7.2 Background; 7.3 Basic Microcontroller; 7.4 Actuation; 7.5 Sensors
2.5 FBG-IMU System Development (First Approach)2.6 Second Approach of FBG-IMU; 2.7 Results; 2.8 Conclusions and Future Work; References; Chapter 3: On the Output-Only Vibration-Based Damage Detection of Frame Structures; 3.1 Introduction; 3.2 Output-Only Vibration-Based Damage Detection Methods; 3.3 Application: Identification and Damage Detection; 3.3.1 Test Structure: Undamaged Condition; 3.3.2 Test Structure: Damaged Condition; 3.3.3 Simulation of the Structural Response; 3.4 Results and Discussion; 3.4.1 Output-Only Modal Identification; 3.4.2 Damage Detection; 3.5 Conclusions; References
Chapter 4: On the Influence of Sample Length and Measurement Noise on the Stochastic Subspace Damage Detection Technique4.1 Introduction; 4.2 Stochastic Subspace Damage Detection Technique; 4.2.1 Dynamic Equilibrium Equation in Discrete Time Domain; 4.2.2 Output-Only Covariance Based Subspace System Identification; 4.2.3 Residual Vector Formation; 4.2.4 Hypothesis Test; 4.2.4.1 Parametric Chi-Square Test; 4.2.4.2 Non-parametric Chi-Square Test; 4.3 Investigating the Effect of Noise and Number of Samples; 4.3.1 Effect of Number of Samples; 4.3.1.1 Effect on the Residual Covariance
4.3.1.2 Effect on the chi2 Test Value4.3.2 Effect of Measurement Noise; 4.3.2.1 Equal Noise Properties Between the Reference State and Possibly Damaged State; 4.3.2.2 Different Noise Properties Between the Reference State and Possibly Damaged State; 4.4 Numerical Application; 4.4.1 Cases Study 1, Effect of Number of Samples; 4.4.2 Case Study 2, Effect of Noise with Equal Properties; 4.4.3 Case Study 3, Effect of Noise with Unequal Properties; 4.5 Discussion and Conclusion; References; Chapter 5: Quantification of Structural Damage with Self-Organizing Maps; 5.1 Introduction
5.2 Qatar University Grandstand Simulator5.3 Verification of the Finite Element Model; 5.4 The SOM-Based Damage Detection Algorithm; 5.4.1 Training of the SOMs; 5.4.2 Structural Damage Assessment; 5.5 Numerical Demonstration of the Damage Detection Algorithm; 5.6 Discussion; 5.7 Conclusion; References; Chapter 6: Accuracy Enhancement of a Device for Automated Underbridge Inspections; 6.1 Introduction; 6.2 Method; 6.3 Results; 6.4 Concluding Remarks; References; Chapter 7: A Brief Overview of Mechatronics; 7.1 Introduction; 7.2 Background; 7.3 Basic Microcontroller; 7.4 Actuation; 7.5 Sensors