Details

Autor(en) / Beteiligte

• Alavi, Amir H.,

Titel

The rise of smart cities : advanced structural sensing and monitoring systems

Ort / Verlag

Amsterdam, Netherlands : Elsevier,

Erscheinungsjahr

[2022]

Link zum Volltext

• Elsevier Books AutoHoldings

Beschreibungen/Notizen

• Intro -- The Rise of Smart Cities: Advanced Structural Sensing and Monitoring Systems -- Copyright -- Contents -- Contributors -- Chapter 1: Advanced sensing and monitoring systems for smart cities -- 1. Introduction -- 2. Smart sensing and monitoring technologies -- 2.1. Fiber optic sensors -- 2.2. Piezoelectric sensors -- 2.3. Global navigation satellite system -- 2.4. Magnetostrictive sensors -- 3. Sensing data transmission in smart cities -- 3.1. Data acquisition for WSN -- 3.2. Wireless data transmission in WSN -- 4. Smart and multifunctional materials in smart cities -- 4.1. Crowdsourced sensing in smart cities -- 5. Data analytics in smart cities -- 5.1. Data preprocessing -- 5.2. Data fusion -- 5.3. Pattern recognition -- 5.4. Data analysis -- 5.5. Data visualization -- 6. Early warning systems in smart cities -- 6.1. Early warning of structural damages -- 6.2. Early warning of real-time abnormality -- 6.3. Early warning of disasters -- 7. Automated aerial sensing and monitoring systems in smart cities -- 8. Challenges and future trends -- 9. Conclusions -- References -- Part I: Smart materials for smart cities -- Chapter 2: Advanced multifunctional structures for future smart cities -- 1. Introduction -- 2. Multifunctional materials -- 3. Multifunctional structures -- 4. Multifunctional structures for smart civil infrastructure systems -- 5. Conclusions -- References -- Chapter 3: Impacts of metal additive manufacturing on smart city infrastructure -- 1. Additive manufacturing overview -- 1.1. Powder bed fusion -- 1.2. Direct energy deposition -- 1.3. Binder techniques -- 2. MAM smart city integration -- 2.1. Big area additive -- 2.2. Embedded sensors -- 2.3. Targeted repair and reinforcement -- 3. Challenges associated with MAM -- 3.1. Powder defects -- 3.2. Geometrical defects -- 3.3. Porosity -- 3.4. Performance.
• 3.5. The case for in situ monitoring -- 4. Structured light monitoring for MAM -- 4.1. Monitoring system overview -- 4.2. Supporting measurement models -- 4.3. Single-point uncertainty model -- 4.3.1. Output phase noise formulation -- 4.3.2. Phase uncertainty PDF -- 4.3.3. Height uncertainty PDF -- 4.4. Multipoint graphics model -- 5. Conclusion -- Acknowledgments -- References -- Chapter 4: Graphene-reinforced cement composites for smart infrastructure systems -- 1. Introduction -- 2. Graphene-based nanomaterials -- 3. Dispersion of graphene-based nanomaterials into cement composites -- 3.1. Dispersion methods -- 3.1.1. Physical dispersion methods -- 3.1.2. Chemical functionalization methods -- 3.2. Dispersion characterization -- 4. Electrical and self-sensing characterization of cement composites -- 5. Graphene-reinforced self-sensing cement composites for smart infrastructure applications -- 5.1. Review of literature for self-sensing GNP-based cement composites -- 5.2. Experimental study on self-sensing GNP-based cement composites -- 5.2.1. Experimental methods -- 5.2.2. Experimental results -- 6. Conclusions and outlook -- References -- Chapter 5: Role of acoustic metamaterials and phononic crystals in sensing and damage detection in solids -- 1. Introduction -- 2. Properties of phononic crystals for nondestructive evaluation methods -- 2.1. The bandgap in dispersion curves of 1D, 2D, and 3D phononic crystals -- 2.2. Gradient-index phononic crystal (GRIN-PC) lens -- 2.3. Phononic crystal lens based on negative refraction -- 3. Applications of phononic crystals to alter ultrasonic waves -- 3.1. State-of-the-art challenges of ultrasonics -- 3.2. Eliminating higher harmonics of nonlinear ultrasonics with 1D phononic crystals -- 3.3. Ultrasonic wave focusing on pipe-like structures with GRIN-PC lens.
• 3.4. High-resolution focusing with the PC lens based on negative refraction -- 4. Applications of phononic crystals to enhance acoustic emission -- 4.1. State-of-the-art challenges of acoustic emission -- 4.2. Noise blocking with 2D phononic crystals -- 5. The enhancement of electromagnetic wave-based NDE with metamaterial lens -- 6. Ultrasonic image enhancement with metamaterials -- 7. Summary and future directions -- Acknowledgments -- References -- Chapter 6: Distributed surface sensing for structural health monitoring using smart textiles -- 1. Introduction -- 2. Theoretical background -- 2.1. Plane stress problems -- 2.2. Plane strain problems -- 3. SHM of pipeline structures using distributed surface strains -- 3.1. Stress-strain analysis of pipeline structures -- 3.2. Surface strain-based pipeline pressure sensing -- 3.3. Surface strain-based pipeline deformation sensing -- 4. Smart textiles for surface strain sensing -- 5. Comparison with other sensing modes in SHM for smart cities -- 6. Conclusions -- Acknowledgments -- References -- Part II: Structural health monitoring techniques for smart cities -- Chapter 7: Using an accelerometer-based system for real-time structural monitoring and response prediction under extreme ... -- 1. Introduction -- 2. Background and related work -- 3. Methodology -- 3.1. Estimation -- 3.2. Prediction -- 3.3. Experimental setup -- 3.4. Evaluation -- 4. Results -- 4.1. Estimation -- 4.2. Estimation under uncertainty -- 4.3. Prediction -- 4.4. Prediction accuracy and computational cost -- 5. Conclusions -- References -- Chapter 8: Applications of computer vision-based structural health monitoring and condition assessment in future smart cities -- 1. Introduction -- 1.1. Overview -- 1.2. Scope -- 2. Condition assessment at local level -- 2.1. Classification and detection -- 2.1.1. Machine learning-based techniques.
• 2.1.2. Deep learning-based techniques -- 2.2. Segmentation -- 2.2.1. Image processing-based techniques -- 2.2.2. Machine learning-based techniques -- 2.2.3. Deep learning-based techniques -- 2.3. Damage quantification -- 3. Condition assessment at global level -- 3.1. Methods -- 3.1.1. Correlation-based template matching -- 3.1.2. Keypoint matching -- 3.1.3. Optical flow -- 3.2. Applications -- 4. Current challenges and roadmap for future research -- 5. Conclusions -- References -- Chapter 9: On-board monitoring for smart assessment of railway infrastructure: A systematic review -- 1. Introduction -- 2. Track infrastructure components and condition -- 2.1. The track and its geometry -- 2.2. Rail connections: Welds and insulated joints -- 2.3. Rail corrugation -- 2.4. Transient rail defects -- 2.5. Switches -- 3. Condition monitoring in railways -- 3.1. Diagnostic vehicles -- 3.2. On-board monitoring -- 3.3. Uncertainty in vehicle localization -- 3.4. Digital infrastructure -- 3.5. Challenges in condition monitoring -- 4. Vehicle-track interaction -- 5. Parametric methods -- 5.1. Autoregressive moving average models -- 5.2. Linear parameter varying autoregressive models -- 5.3. Bayesian filtering -- 5.4. The Vold-Kalman filter -- 5.5. Extended Kalman filter -- 5.6. Unscented Kalman filter -- 6. Nonparametric methods -- 6.1. Time-frequency analysis -- 6.2. Numerical integration -- 6.3. Statistical features -- 6.4. Parametric versus nonparametric methods -- 7. Classification and outlier analysis -- 7.1. Classification -- 7.2. Outlier analysis -- 8. Conclusion -- Acknowledgments -- References -- Chapter 10: Mixed reality-assisted smart bridge inspection for future smart cities -- 1. Introduction -- 2. Current practices with advanced methods -- 3. Human-centered approach with mixed reality -- 4. Real-time machine learning using semisupervised data.
• 5. Using the real-time inspection data for bridge assessment -- 6. Concluding remarks -- Acknowledgments -- References -- Chapter 11: Deep learning for vibration-based data-driven defect diagnosis of structural systems -- 1. Introduction -- 2. Methodology -- 2.1. Basic one-dimensional CNN architecture -- 2.2. Transfer learning technology -- 2.3. Detailed procedure of defect diagnosis -- 3. Experimental and numerical simulation case study -- 3.1. Experiments -- 3.2. Numerical simulations -- 4. Results and discussion -- 4.1. Defect types identification -- 4.2. Defect degrees identification -- 5. Conslusions -- Acknowledgments -- References -- Chapter 12: Applications of depth sensing for advanced structural condition assessment in smart cities -- 1. Introduction -- 2. Depth-sensing modalities -- 3. State-of-the-art depth sensors -- 4. Applications of RGB-D sensing -- 4.1. Spalling and pothole detection -- 4.2. Estimation of rut depth -- 4.3. Estimation of pavement friction -- 4.4. Crack segmentation and quantification -- 4.5. Deep learning-based crack identification -- 4.6. Displacement measurement -- 5. Conclusions and future work -- References -- Chapter 13: Electrostatic micro-electro-mechanical system vibrational energy harvesters for bridge damage detection -- 1. Introduction -- 2. MEMS vibrational energy harvester -- 2.1. Overview -- 2.2. Energy harvesting calculation -- 3. Case studies and discussions -- 3.1. Numerical study on a pier scouring detection -- 3.2. Experimental study on girder damage and support malfunction detection -- 3.3. Field experimental study on an incrementally damaged truss bridge -- 3.4. Discussions -- 4. Concluding remarks -- Acknowledgments -- References -- Chapter 14: Smart bridge monitoring -- 1. Introduction -- 1.1. The objective of bridge monitoring -- 1.2. The concept of smart system.
• 1.3. Motivations behind smart monitoring of bridges.
• "The Rise of Smart Cities: Advanced Structural Sensing and Monitoring Systems provides engineers and researchers with a guide to the latest breakthroughs in the deployment of smart sensing and monitoring technologies. The book introduces readers to the latest innovations in the area of smart infrastructure-enabling technologies and how they can be integrated into the planning and design of smart cities. With this book in hand, readers will find a valuable reference in terms of civil infrastructure health monitoring, advanced sensor network architectures, smart sensing materials, multifunctional material and structures, crowdsourced/social sensing, remote sensing and aerial sensing, and advanced computation in sensor networks."--
• Description based on print version record.